byteLAKE's CFD Suite (AI-accelerated CFD) - AI Training at the Edge (benchmark).
This report summarizes byteLAKE’s benchmark results of the recommended hardware platform for byteLAKE’s CFD Suite’s AI training at the Edge. Key takeaways are at the end of the report.
About the product:
• CFD Suite: AI-accelerated Computational Fluid Dynamics. In the chemical industry, it reduces mixing simulation time from hours to minutes.
Related blog posts:
1. https://becominghuman.ai/bytelakes-cfd-suite-ai-accelerated-cfd-recommended-hardware-for-ai-training-part-1-3-a6c72130b1b7
2. https://marcrojek.medium.com/bytelakes-cfd-suite-ai-accelerated-cfd-recommended-hardware-for-ai-training-at-the-edge-part-574f4663338e
3. https://marcrojek.medium.com/bytelakes-cfd-suite-ai-accelerated-cfd-recommended-hardware-for-ai-training-at-the-edge-3-3-1a5abbba552c
More: https://www.byteLAKE.com/en/CFDSuite and https://www.byteLAKE.com/en/AI4CFD-toc (blog post series).
byteLAKE
Artificial Intelligence for Industries. AI Products for Manufacturing, Automotive, Restaurants, Paper, and Chemical Industries.
About byteLAKE
We are a software company, focused on building Artificial Intelligence products for various industries. byteLAKE's CFD Suite leverages AI to reduce CFD (Computational Fluid Dynamics) chemical mixing simulations’ time from hours to minutes. byteLAKE's Cognitive Services offers a collection of pre-trained AI models for Industry 4.0 and Restaurants. In manufacturing, these are used to visually inspect processes, parts, components, or products. In automotive, Cognitive Services leverages microphones to assess the quality of car engines. In the paper industry, Cognitive Services leverages cameras to monitor the papermaking process and detect, measure, and analyze the wet line. In restaurants, Cognitive Services typically acts as an add-on software that recognizes meals and sends a list of these to the cashier's machine, ultimately reducing lines and the overall waiting time. The company also offers custom AI software development for real-time analytics of images, videos, sounds, and time-series data. To learn more about byteLAKE’s innovations, go to www.byteLAKE.com.
AI Solutions for Industries | Quality Inspection | Data Insights | AI-accelerated CFD | Self-Checkout | byteLAKE.com
byteLAKE: Empowering Industries with AI Solutions. Embrace cutting-edge technology for advanced quality inspection, data insights, and more. Harness the potential of our CFD Suite, accelerating Computational Fluid Dynamics for heightened productivity. Unlock new possibilities with Cognitive Services: image analytics for precise visual inspection for Manufacturing, sound analytics enabling proactive maintenance for Automotive, and wet line analytics for the Paper Industry. Seamlessly convert data into actionable insights using Data Insights' AI module, enabling advanced predictive maintenance and risk detection. Simplify Restaurant and Retail operations with our efficient self-checkout solution, recognizing meals and groceries and elevating customer satisfaction. Custom AI Development services available for tailored solutions. Discover more at www.byteLAKE.com.
► byteLAKE's CFD Suite: Accelerate your Computational Fluid Dynamics (CFD) simulations by leveraging the speed and efficiency of artificial intelligence. Slash simulation times, minimize trial-and-error costs, and supercharge decision-making for heightened productivity. Learn more at www.byteLAKE.com/en/CFDSuite.
byteLAKE's CFD Suite (AI-accelerated CFD) (2024-02)byteLAKE
► byteLAKE's CFD Suite: Accelerate your Computational Fluid Dynamics (CFD) simulations by leveraging the speed and efficiency of artificial intelligence. Slash simulation times, minimize trial-and-error costs, and supercharge decision-making for heightened productivity. Learn more at www.byteLAKE.com/en/CFDSuite.
byteLAKE pioneers the use of AI and HPC to provide automated and data-driven solutions for its customers. Case study: https://www.intel.com/content/www/us/en/data-center/idc-bytelake-case-study.html
byteLAKE
Artificial Intelligence for Chemical Industry, Paper Industry and Manufacturing.
We build AI products and help design custom AI software.
About byteLAKE
byteLAKE is a software company that builds Artificial Intelligence products for the chemical industry, paper industry and manufacturing. byteLAKE's CFD Suite leverages AI to reduce CFD (Computational Fluid Dynamics) chemical mixing simulations’ time from hours to minutes. byteLAKE's Cognitive Services offer AI-assisted Visual Inspection and Big Data analytics for the paper industry, detecting and visually inspecting the so-called Water Line and complex tasks automation for manufacturing. The company also offers custom AI software development for real-time data analytics (image / video / sound / time-series). To learn more about byteLAKE’s innovations, go to www.byteLAKE.com.
HARDWARE SOFTWARE CO-SIMULATION OF MOTION ESTIMATION IN H.264 ENCODERcscpconf
This paper proposes about motion estimation in H.264/AVC encoder. Compared with standards
such as MPEG-2 and MPEG-4 Visual, H.264 can deliver better image quality at the same
compressed bit rate or at a lower bit rate. The increase in compression efficiency comes at the
expense of increase in complexity, which is a fact that must be overcome. An efficient Co-design
methodology is required, where the encoder software application is highly optimized and
structured in a very modular and efficient manner, so as to allow its most complex and time
consuming operations to be offloaded to dedicated hardware accelerators. The Motion
Estimation algorithm is the most computationally intensive part of the encoder which is simulated using MATLAB. The hardware/software co-simulation is done using system generator tool and implemented using Xilinx FPGA Spartan 3E for different scanning methods.
SGI HPC Systems Help Fuel Manufacturing Rebirth 2015Josh Goergen
This document discusses how high performance computing (HPC) systems are helping fuel the resurgence of manufacturing. It outlines the ongoing challenges manufacturers face including stringent regulations, cost pressures, and intense global competition. SGI provides HPC solutions like the UV, Rackable, and ICE X clusters along with storage solutions to help manufacturers address these challenges and meet goals like faster time to market. Case studies describe how SGI systems helped companies like SL-Rasch perform complex simulations of umbrella structures and Škoda accelerate product development.
IRJET- A Review- FPGA based Architectures for Image Capturing Consequently Pr...IRJET Journal
This document presents a review of FPGA-based architectures for image capturing, processing, and display using a VGA monitor. It discusses using the Xilinx AccelDSP tool to develop the system on a Spartan 3E FPGA. The AccelDSP tool allows converting a MATLAB design into HDL for implementation on the FPGA. It summarizes the FPGA-based system architecture, which includes units for initialization, data transfer, image processing, and memory management. It then outlines the Xilinx AccelDSP design flow, which verifies the functionality at each stage of converting the floating-point MATLAB model to a fixed-point hardware implementation on the FPGA. The goal is to accelerate image processing applications using the parallel
How to estimate the cost of a Maximo migration project with a high level of c...Mariano Zelaya Feijoo
This document discusses how to estimate the cost of a high customization Maximo migration project from an older version to 7.5+. It involves:
1) Analyzing areas like architecture, new functionality, data model changes, reporting and integrations.
2) Assessing customization level using IBM's Customization Detection Tool or comparisons to out-of-box versions.
3) Categorizing customizations into components like UI, reports, APIs.
4) Using tools like SLOC counters to measure custom code lines to estimate effort.
5) Collecting metrics on customizations to input into estimation models like SLIM QSM for time/cost figures.
Benchmark of common AI accelerators: NVIDIA GPU vs. Intel MovidiusbyteLAKE
The document summarizes byteLAKE’s basic benchmark results between two different setups of example edge devices: with NVIDIA GPU and with Intel’s Movidius cards.
Key takeaway: the comparison of Movidius and NVIDIA as two competing accelerators for AI workloads leads to a conclusion that these two are meant for different tasks.
AI Solutions for Industries | Quality Inspection | Data Insights | AI-accelerated CFD | Self-Checkout | byteLAKE.com
byteLAKE: Empowering Industries with AI Solutions. Embrace cutting-edge technology for advanced quality inspection, data insights, and more. Harness the potential of our CFD Suite, accelerating Computational Fluid Dynamics for heightened productivity. Unlock new possibilities with Cognitive Services: image analytics for precise visual inspection for Manufacturing, sound analytics enabling proactive maintenance for Automotive, and wet line analytics for the Paper Industry. Seamlessly convert data into actionable insights using Data Insights' AI module, enabling advanced predictive maintenance and risk detection. Simplify Restaurant and Retail operations with our efficient self-checkout solution, recognizing meals and groceries and elevating customer satisfaction. Custom AI Development services available for tailored solutions. Discover more at www.byteLAKE.com.
► byteLAKE's CFD Suite: Accelerate your Computational Fluid Dynamics (CFD) simulations by leveraging the speed and efficiency of artificial intelligence. Slash simulation times, minimize trial-and-error costs, and supercharge decision-making for heightened productivity. Learn more at www.byteLAKE.com/en/CFDSuite.
byteLAKE's CFD Suite (AI-accelerated CFD) (2024-02)byteLAKE
► byteLAKE's CFD Suite: Accelerate your Computational Fluid Dynamics (CFD) simulations by leveraging the speed and efficiency of artificial intelligence. Slash simulation times, minimize trial-and-error costs, and supercharge decision-making for heightened productivity. Learn more at www.byteLAKE.com/en/CFDSuite.
byteLAKE pioneers the use of AI and HPC to provide automated and data-driven solutions for its customers. Case study: https://www.intel.com/content/www/us/en/data-center/idc-bytelake-case-study.html
byteLAKE
Artificial Intelligence for Chemical Industry, Paper Industry and Manufacturing.
We build AI products and help design custom AI software.
About byteLAKE
byteLAKE is a software company that builds Artificial Intelligence products for the chemical industry, paper industry and manufacturing. byteLAKE's CFD Suite leverages AI to reduce CFD (Computational Fluid Dynamics) chemical mixing simulations’ time from hours to minutes. byteLAKE's Cognitive Services offer AI-assisted Visual Inspection and Big Data analytics for the paper industry, detecting and visually inspecting the so-called Water Line and complex tasks automation for manufacturing. The company also offers custom AI software development for real-time data analytics (image / video / sound / time-series). To learn more about byteLAKE’s innovations, go to www.byteLAKE.com.
HARDWARE SOFTWARE CO-SIMULATION OF MOTION ESTIMATION IN H.264 ENCODERcscpconf
This paper proposes about motion estimation in H.264/AVC encoder. Compared with standards
such as MPEG-2 and MPEG-4 Visual, H.264 can deliver better image quality at the same
compressed bit rate or at a lower bit rate. The increase in compression efficiency comes at the
expense of increase in complexity, which is a fact that must be overcome. An efficient Co-design
methodology is required, where the encoder software application is highly optimized and
structured in a very modular and efficient manner, so as to allow its most complex and time
consuming operations to be offloaded to dedicated hardware accelerators. The Motion
Estimation algorithm is the most computationally intensive part of the encoder which is simulated using MATLAB. The hardware/software co-simulation is done using system generator tool and implemented using Xilinx FPGA Spartan 3E for different scanning methods.
SGI HPC Systems Help Fuel Manufacturing Rebirth 2015Josh Goergen
This document discusses how high performance computing (HPC) systems are helping fuel the resurgence of manufacturing. It outlines the ongoing challenges manufacturers face including stringent regulations, cost pressures, and intense global competition. SGI provides HPC solutions like the UV, Rackable, and ICE X clusters along with storage solutions to help manufacturers address these challenges and meet goals like faster time to market. Case studies describe how SGI systems helped companies like SL-Rasch perform complex simulations of umbrella structures and Škoda accelerate product development.
IRJET- A Review- FPGA based Architectures for Image Capturing Consequently Pr...IRJET Journal
This document presents a review of FPGA-based architectures for image capturing, processing, and display using a VGA monitor. It discusses using the Xilinx AccelDSP tool to develop the system on a Spartan 3E FPGA. The AccelDSP tool allows converting a MATLAB design into HDL for implementation on the FPGA. It summarizes the FPGA-based system architecture, which includes units for initialization, data transfer, image processing, and memory management. It then outlines the Xilinx AccelDSP design flow, which verifies the functionality at each stage of converting the floating-point MATLAB model to a fixed-point hardware implementation on the FPGA. The goal is to accelerate image processing applications using the parallel
How to estimate the cost of a Maximo migration project with a high level of c...Mariano Zelaya Feijoo
This document discusses how to estimate the cost of a high customization Maximo migration project from an older version to 7.5+. It involves:
1) Analyzing areas like architecture, new functionality, data model changes, reporting and integrations.
2) Assessing customization level using IBM's Customization Detection Tool or comparisons to out-of-box versions.
3) Categorizing customizations into components like UI, reports, APIs.
4) Using tools like SLOC counters to measure custom code lines to estimate effort.
5) Collecting metrics on customizations to input into estimation models like SLIM QSM for time/cost figures.
Benchmark of common AI accelerators: NVIDIA GPU vs. Intel MovidiusbyteLAKE
The document summarizes byteLAKE’s basic benchmark results between two different setups of example edge devices: with NVIDIA GPU and with Intel’s Movidius cards.
Key takeaway: the comparison of Movidius and NVIDIA as two competing accelerators for AI workloads leads to a conclusion that these two are meant for different tasks.
Stay up-to-date on the latest news, events and resources for the OpenACC community. This month’s highlights covers the Organization's newly elected president, an updated OpenACC 3.1 specification, upcoming 2021 GPU Hackathons, new resources and more!
Auto conversion of serial C code to CUDA codeIRJET Journal
This document describes a prototype compiler that automatically converts serial C code to CUDA code so it can run efficiently on GPUs. The compiler uses patterns to transform the C code and add parallelization so it can run on the CUDA architecture. The compiler was tested on benchmark programs and was able to achieve performance close to fully optimized CUDA code while greatly reducing the time needed to port programs to run on GPUs. The compiler generates CUDA code from the C code and handles memory allocation and parallelization automatically.
The Architecture Of Software Defined Radios EssayDivya Watson
This project aims to build a smart assistant to help users purchase books online by integrating
multiple sources of information about books and the purchasing process into a single system. By
consolidating data from sources about books, reviews, prices and retailers, the assistant can provide
users all the necessary information to make an informed purchase decision in one place. The goal is
to streamline the online book buying experience for users by eliminating the need to search across
multiple websites during the purchase process.
dassault-systemes-catia-application-scalability-guideJason Kyungho Lee
This document provides guidance on deploying Dassault CATIA V5/V6 applications in a virtualized environment using NVIDIA GRID virtual GPUs. It analyzes customer usage data to recommend virtual machine configurations and expected user densities based on the type of CATIA workloads. Standard users can expect 8-16 users per server, while power users and analysts may require dedicated resources. Testing with real workloads and user feedback is needed to validate the appropriate solution for each organization.
TAKUMI is a company founded in 2003 that licenses OpenGL ES- and OpenVG-compliant graphics accelerator IP cores for use in embedded systems and mobile devices. They used S2C's Virtex-6 FPGA platform to test and verify several of their graphics IP cores, including the GS3000 and GSV3000 cores. This allowed TAKUMI to demonstrate and evaluate the IP cores for customers more easily, reducing system-on-chip integration time. Using S2C's solution, TAKUMI was able to test a variety of software on different graphics IP cores at near real-time speeds.
For the full video of this presentation, please visit:
https://www.edge-ai-vision.com/2020/11/benchmarking-vs-benchmarketing-why-should-you-care-a-presentation-from-qualcomm/
For more information about edge AI and computer vision, please visit:
https://www.edge-ai-vision.com
Felix Baum, Director of Product Management at Qualcomm, presents the “Benchmarking vs. Benchmarketing: Why Should You Care?” tutorial at the September 2020 Embedded Vision Summit.
Qualcomm is determined to provide best in class AI hardware solutions, enabling companies to leverage AI acceleration in their products. But how can developers know what is the best hardware for their models? Comparing AI hardware is not as simple as it might seem; there are many caveats that need to be considered, such as INT8 and floating-point benefits, how commercial benchmarks are structured and what is the hardware optimized for.
Qualcomm chips score at the top of commercial benchmarks. Nonetheless, the company is devoted to enlightening its partners and developers about what is the best solution for their specific needs. In this talk you will learn about some of the most common ways of comparing AI hardware and what you need to consider in order to make an accurate assessment.
Vertex Perspectives | AI Optimized Chipsets | Part IIIVertex Holdings
In this instalment, we review the training and inference chipset markets, assess the dominance of tech giants, as well as the startups adopting cloud-first or edge-first approaches to AI-optimized chipsets.
Bhadale group of companies aviation industry products catalogueVijayananda Mohire
This document provides information on aviation industry products from Bhadale Group of Companies. It describes three main products: 1) An aircraft delay analysis system that uses historical data and machine learning to predict flight delays. 2) An engineering test rig with hardware, software and interfaces to test avionics units. 3) An avionics software test kit to interface with units, capture and analyze data, and display results. Details are provided on the features and capabilities of each product. Contact information is provided for anyone interested in learning more.
The document discusses Real Time Operating Systems (RTOS). It defines RTOS as a multitasking operating system intended for real-time applications. RTOS provides deterministic timing behavior and limited resource utilization for applications that require logically correct results within strict deadlines, such as those found in automotive and industrial systems. The document outlines some key RTOS concepts like multitasking, interrupt handling, and memory management. It explains that while not necessary for simple embedded systems, RTOS is beneficial for more complex real-time applications as it helps manage hardware resources and schedule tasks to meet application demands and deadlines.
Stay up-to-date on the latest news, events and resources for the OpenACC community. This month’s highlights covers the upcoming OpenACC Summit, a complete schedule of upcoming events, using OpenACC to optimize structural analysis, new resources and more!
AI Bridging Cloud Infrastructure (ABCI) and its communication performanceinside-BigData.com
In this deck from the MVAPICH User Group, Shinichiro Takizawa from AIST presents: AI Bridging Cloud Infrastructure (ABCI) and its communication performance.
"AI Bridging Cloud Infrastructure (ABCI) is the world's first large-scale Open AI Computing Infrastructure, constructed and operated by National Institute of Advanced Industrial Science and Technology (AIST), Japan. It delivers 19.9 petaflops of HPL performance and world' fastest training time of 1.17 minutes in ResNet-50 training on ImageNet datasets as of July 2019. ABCI consists of 1,088 compute nodes each of which equipped with two Intel Xeon Gold Scalable Processors, four NVIDIA Tesla V100 GPUs, two InfiniBand EDR HCAs and an NVMe SSD. ABCI offers a sophisticated high performance AI development environment realized by CUDA, Linux containers, on-demand parallel filesystem, MPI, including MVAPICH, etc. In this talk, we focus on ABCI’s network architecture and communication libraries available on ABCI and shows their performance and recent research achievements."
Watch the video: https://wp.me/p3RLHQ-kLz
Learn more: https://abci.ai/
and
http://mug.mvapich.cse.ohio-state.edu/program/
Sign up for our insideHPC Newsletter: http://insidehpc.com/newsletter
Stay up-to-date on the latest news, events and resources for the OpenACC community. This month’s highlights covers the on-demand sessions from the OpenACC Summit 2020, upcoming GPU Hackathons and Bootcamps, an OpenACC-to-FPGA framework, the NERSC GPU Hackathon, new resources and more!
Ignitho’s CDP accelerator allows implementations in as little as 2 Weeks. Your CDP implementation is not just an aggregated repository of customer data with visualizations and basic segmentations.
Download Our Brochure Today to Get a Closer Look at Ignitho's Innovative Solutions! https://www.ignitho.com/customer-data-platform-accelerator/
This document discusses accelerating artificial intelligence and contains the following key points:
1. The amount of data generated from various technologies is growing exponentially and will require vast increases in computing power to process and analyze.
2. Intel is developing new hardware like Intel Xeon and FPGA chips as well as frameworks and libraries to provide the computing capabilities needed for advanced AI workloads.
3. Optimizations in hardware, software, frameworks and algorithms can significantly boost AI performance on Intel platforms, allowing complex deep learning models to be trained in hours instead of days.
A comparison of the Dell AI portfolio vs. similar offerings from Supermicro
Conclusion
When it comes to designing, implementing, managing, and maintaining AI solutions in your business, there are many factors to consider. To help you invest wisely and get the most out of your AI solution, you may wish to look for a vendor that can be more than a hardware supplier. Our research indicates that Dell offers services that can help you as a partner throughout the entire journey, so consider investing with Dell as you dive into AI.
OpenACC and Open Hackathons Monthly Highlights: September 2022.pptxOpenACC
Stay up-to-date on the latest news, research and resources. This month's edition covers the Princeton GPU Hackathon, OpenACC at SC22, updates from GNU Tools Cauldron, the upcoming UK DPU Hackathon, relevant research and more!
GPGPU in Commercial Software: Lessons From Three Cycles of the Adobe Creative...Kevin Goldsmith
This was a talk I gave at NVidia's Graphics Technology Conference in San Jose, California in 2010. On NVidia's site you can find this talk, synced with the audio here: http://nvidia.fullviewmedia.com/gtc2010/0923-k-2051.html
This document discusses computer vision and Mobica's work in the field. It provides an overview of computer vision, including common uses and relevant technologies like OpenCV. Mobica has experience implementing and optimizing computer vision algorithms using technologies like OpenCL. They have worked on projects involving object recognition, image processing, augmented reality, and using computer vision in applications like automotive systems and gesture recognition. Mobica sees opportunities to improve computer vision performance and make it more accessible to developers.
For the full video of this presentation, please visit: https://www.edge-ai-vision.com/2023/07/a-new-open-standards-based-open-source-programming-model-for-all-accelerators-a-presentation-from-codeplay-software/
Charles Macfarlane, Chief Business Officer at Codeplay Software, presents the “New, Open-standards-based, Open-source Programming Model for All Accelerators” tutorial at the May 2023 Embedded Vision Summit.
As demand for AI grows, developers are attempting to squeeze more and more performance from accelerators. Ideally, developers would choose the accelerators best suited to their applications. Unfortunately, today many developers are locked into limited hardware choices because they use proprietary programming models like NVIDIA’s CUDA. The oneAPI project was launched to create an open specification and open-source software that enables developers to write software using standard C++ code and deploy to GPUs from multiple vendors.
OneAPI is an open-source ecosystem based on the Khronos open-standard SYCL with libraries for enabling AI and HPC applications. OneAPI-enabled software is currently deployed on numerous supercomputers, with plans to extend into other market segments. OneAPI is evolving rapidly and the whole community of hardware and software developers is invited to contribute. In this presentation, Macfarlane introduces how oneAPI enables developers to write multi-target software and highlights opportunities for developers to contribute to making oneAPI available for all accelerators.
Atmel - Next-Generation IDE: Maximizing IP Reuse [WHITE PAPER]Atmel Corporation
Atmel® Studio 6 is the integrated development platform (IDP) for developing and debugging Atmel ARM® Cortex™-M and Atmel AVR® microcontroller- (MCU-) based applications. The Atmel Studio 6 IDP gives you a seamless and easy-to-use environment to write, build and debug your applications written in C/C++ or assembly code.
Atmel Studio 6 is free of charge and is integrated with the Atmel Software Framework (ASF)—a large library of free source code with 1,600 ARM and AVR project examples. ASF strengthens the IDP by providing, in the same environment, access to ready-to-use code that minimizes much of the low-level design required for projects. Use the IDP for our wide variety of AVR and ARM Cortex-M processor-based MCUs, including our broadened portfolio of Atmel SAM3 ARM Cortex-M3 and M4 Flash devices.
With the introduction of Atmel Gallery and Atmel Spaces, Atmel Studio 6 further simplifies embedded MCU designs to reduce development time and cost. Atmel Gallery is an online apps store for development tools and embedded software. Atmel Spaces is a cloud-based collaborative development workspace allowing you to host software and hardware projects targeting Atmel MCUs.
For more information, please visit http://www.atmel.com/Microsite/atmel_studio6.
Follow along on Twitter at http://www.twitter.com/Atmel and 'Like' Atmel on Facebook at http://www.facebook.com/atmelcorporation.
byteLAKE's AI Products (use cases) (short)byteLAKE
AI Solutions for Industries | Quality Inspection | Data Insights | Predictive Maintenance | AI-accelerated CFD | Self-Checkout | byteLAKE.com
byteLAKE: Empowering Industries with AI Solutions. Embrace cutting-edge technology for advanced quality inspection, data insights, and more. Harness the potential of our CFD Suite, accelerating Computational Fluid Dynamics for heightened productivity. Unlock new possibilities with Cognitive Services: image analytics for precise visual inspection for Manufacturing, sound analytics enabling proactive maintenance for Automotive, and wet line analytics for the Paper Industry. Seamlessly convert data into actionable insights using Data Insights' AI module, enabling advanced predictive maintenance and risk detection. Simplify Restaurant and Retail operations with our efficient self-checkout solution, recognizing meals and groceries and elevating customer satisfaction. Custom AI Development services available for tailored solutions. Discover more at www.byteLAKE.com.
Benefits offered by byteLAKE’s Cognitive Services
Accelerate Data Analytics
- Processing data from various sources, including images, videos, and sensors.
Automate Quality Inspection
- Ensuring high accuracy in inspecting products and processes.
- Eliminating potential human errors for consistent and reliable results.
- Increasing overall quality and reliability.
Optimize Operations and Maintenance
- Reducing unnecessary inspections and lowering maintenance costs.
- Predicting potential failures and downtimes.
Continuous Monitoring
- Offering 24/7/365 monitoring without boredom or distraction.
- Offloading and supporting human operators.
Easy Replication
- Enabling quick deployment.
- Functioning offline without an internet connection.
Continuous Improvement
- The solution can learn and improve over time.
AI in Manufacturing - benefits
Enhanced Productivity
- Streamlining processes for increased productivity.
- Efficient resource allocation based on real-time data.
Customization and Adaptability
- Tailoring AI models to specific manufacturing requirements.
- Adapting to changing production needs seamlessly.
Reduced Downtime
- Minimizing production downtime through predictive maintenance.
- Optimizing machine uptime and reliability.
Data-Driven Decision-Making
- Empowering decision-makers with actionable insights.
- Enabling data-driven strategies for process improvement.
Consistent Quality Control Across the Organization
- Ensuring consistent product quality throughout the production process.
- Meeting industry standards and regulations effortlessly.
Licensing & Cost of Deployment
Cognitive Services
Licensing (annual)
- Annual/monthly licensing plans for Cognitive Services, including upgrades, customer care, and support.
AI Model Development (one time)
- Costs for AI model training and calibration.
- Data Management
- Expenses related to data collection and cleaning.
Hardware, integration, other )one time)
byteLAKE's AI Products (use cases) - presentationbyteLAKE
AI Solutions for Industries | Quality Inspection | Data Insights | Predictive Maintenance | AI-accelerated CFD | Self-Checkout | byteLAKE.com
byteLAKE: Empowering Industries with AI Solutions. Embrace cutting-edge technology for advanced quality inspection, data insights, and more. Harness the potential of our CFD Suite, accelerating Computational Fluid Dynamics for heightened productivity. Unlock new possibilities with Cognitive Services: image analytics for precise visual inspection for Manufacturing, sound analytics enabling proactive maintenance for Automotive, and wet line analytics for the Paper Industry. Seamlessly convert data into actionable insights using Data Insights' AI module, enabling advanced predictive maintenance and risk detection. Simplify Restaurant and Retail operations with our efficient self-checkout solution, recognizing meals and groceries and elevating customer satisfaction. Custom AI Development services available for tailored solutions. Discover more at www.byteLAKE.com.
Contenu connexe
Similaire à byteLAKE's CFD Suite (AI-accelerated CFD) - AI Training at the Edge (benchmark)
Stay up-to-date on the latest news, events and resources for the OpenACC community. This month’s highlights covers the Organization's newly elected president, an updated OpenACC 3.1 specification, upcoming 2021 GPU Hackathons, new resources and more!
Auto conversion of serial C code to CUDA codeIRJET Journal
This document describes a prototype compiler that automatically converts serial C code to CUDA code so it can run efficiently on GPUs. The compiler uses patterns to transform the C code and add parallelization so it can run on the CUDA architecture. The compiler was tested on benchmark programs and was able to achieve performance close to fully optimized CUDA code while greatly reducing the time needed to port programs to run on GPUs. The compiler generates CUDA code from the C code and handles memory allocation and parallelization automatically.
The Architecture Of Software Defined Radios EssayDivya Watson
This project aims to build a smart assistant to help users purchase books online by integrating
multiple sources of information about books and the purchasing process into a single system. By
consolidating data from sources about books, reviews, prices and retailers, the assistant can provide
users all the necessary information to make an informed purchase decision in one place. The goal is
to streamline the online book buying experience for users by eliminating the need to search across
multiple websites during the purchase process.
dassault-systemes-catia-application-scalability-guideJason Kyungho Lee
This document provides guidance on deploying Dassault CATIA V5/V6 applications in a virtualized environment using NVIDIA GRID virtual GPUs. It analyzes customer usage data to recommend virtual machine configurations and expected user densities based on the type of CATIA workloads. Standard users can expect 8-16 users per server, while power users and analysts may require dedicated resources. Testing with real workloads and user feedback is needed to validate the appropriate solution for each organization.
TAKUMI is a company founded in 2003 that licenses OpenGL ES- and OpenVG-compliant graphics accelerator IP cores for use in embedded systems and mobile devices. They used S2C's Virtex-6 FPGA platform to test and verify several of their graphics IP cores, including the GS3000 and GSV3000 cores. This allowed TAKUMI to demonstrate and evaluate the IP cores for customers more easily, reducing system-on-chip integration time. Using S2C's solution, TAKUMI was able to test a variety of software on different graphics IP cores at near real-time speeds.
For the full video of this presentation, please visit:
https://www.edge-ai-vision.com/2020/11/benchmarking-vs-benchmarketing-why-should-you-care-a-presentation-from-qualcomm/
For more information about edge AI and computer vision, please visit:
https://www.edge-ai-vision.com
Felix Baum, Director of Product Management at Qualcomm, presents the “Benchmarking vs. Benchmarketing: Why Should You Care?” tutorial at the September 2020 Embedded Vision Summit.
Qualcomm is determined to provide best in class AI hardware solutions, enabling companies to leverage AI acceleration in their products. But how can developers know what is the best hardware for their models? Comparing AI hardware is not as simple as it might seem; there are many caveats that need to be considered, such as INT8 and floating-point benefits, how commercial benchmarks are structured and what is the hardware optimized for.
Qualcomm chips score at the top of commercial benchmarks. Nonetheless, the company is devoted to enlightening its partners and developers about what is the best solution for their specific needs. In this talk you will learn about some of the most common ways of comparing AI hardware and what you need to consider in order to make an accurate assessment.
Vertex Perspectives | AI Optimized Chipsets | Part IIIVertex Holdings
In this instalment, we review the training and inference chipset markets, assess the dominance of tech giants, as well as the startups adopting cloud-first or edge-first approaches to AI-optimized chipsets.
Bhadale group of companies aviation industry products catalogueVijayananda Mohire
This document provides information on aviation industry products from Bhadale Group of Companies. It describes three main products: 1) An aircraft delay analysis system that uses historical data and machine learning to predict flight delays. 2) An engineering test rig with hardware, software and interfaces to test avionics units. 3) An avionics software test kit to interface with units, capture and analyze data, and display results. Details are provided on the features and capabilities of each product. Contact information is provided for anyone interested in learning more.
The document discusses Real Time Operating Systems (RTOS). It defines RTOS as a multitasking operating system intended for real-time applications. RTOS provides deterministic timing behavior and limited resource utilization for applications that require logically correct results within strict deadlines, such as those found in automotive and industrial systems. The document outlines some key RTOS concepts like multitasking, interrupt handling, and memory management. It explains that while not necessary for simple embedded systems, RTOS is beneficial for more complex real-time applications as it helps manage hardware resources and schedule tasks to meet application demands and deadlines.
Stay up-to-date on the latest news, events and resources for the OpenACC community. This month’s highlights covers the upcoming OpenACC Summit, a complete schedule of upcoming events, using OpenACC to optimize structural analysis, new resources and more!
AI Bridging Cloud Infrastructure (ABCI) and its communication performanceinside-BigData.com
In this deck from the MVAPICH User Group, Shinichiro Takizawa from AIST presents: AI Bridging Cloud Infrastructure (ABCI) and its communication performance.
"AI Bridging Cloud Infrastructure (ABCI) is the world's first large-scale Open AI Computing Infrastructure, constructed and operated by National Institute of Advanced Industrial Science and Technology (AIST), Japan. It delivers 19.9 petaflops of HPL performance and world' fastest training time of 1.17 minutes in ResNet-50 training on ImageNet datasets as of July 2019. ABCI consists of 1,088 compute nodes each of which equipped with two Intel Xeon Gold Scalable Processors, four NVIDIA Tesla V100 GPUs, two InfiniBand EDR HCAs and an NVMe SSD. ABCI offers a sophisticated high performance AI development environment realized by CUDA, Linux containers, on-demand parallel filesystem, MPI, including MVAPICH, etc. In this talk, we focus on ABCI’s network architecture and communication libraries available on ABCI and shows their performance and recent research achievements."
Watch the video: https://wp.me/p3RLHQ-kLz
Learn more: https://abci.ai/
and
http://mug.mvapich.cse.ohio-state.edu/program/
Sign up for our insideHPC Newsletter: http://insidehpc.com/newsletter
Stay up-to-date on the latest news, events and resources for the OpenACC community. This month’s highlights covers the on-demand sessions from the OpenACC Summit 2020, upcoming GPU Hackathons and Bootcamps, an OpenACC-to-FPGA framework, the NERSC GPU Hackathon, new resources and more!
Ignitho’s CDP accelerator allows implementations in as little as 2 Weeks. Your CDP implementation is not just an aggregated repository of customer data with visualizations and basic segmentations.
Download Our Brochure Today to Get a Closer Look at Ignitho's Innovative Solutions! https://www.ignitho.com/customer-data-platform-accelerator/
This document discusses accelerating artificial intelligence and contains the following key points:
1. The amount of data generated from various technologies is growing exponentially and will require vast increases in computing power to process and analyze.
2. Intel is developing new hardware like Intel Xeon and FPGA chips as well as frameworks and libraries to provide the computing capabilities needed for advanced AI workloads.
3. Optimizations in hardware, software, frameworks and algorithms can significantly boost AI performance on Intel platforms, allowing complex deep learning models to be trained in hours instead of days.
A comparison of the Dell AI portfolio vs. similar offerings from Supermicro
Conclusion
When it comes to designing, implementing, managing, and maintaining AI solutions in your business, there are many factors to consider. To help you invest wisely and get the most out of your AI solution, you may wish to look for a vendor that can be more than a hardware supplier. Our research indicates that Dell offers services that can help you as a partner throughout the entire journey, so consider investing with Dell as you dive into AI.
OpenACC and Open Hackathons Monthly Highlights: September 2022.pptxOpenACC
Stay up-to-date on the latest news, research and resources. This month's edition covers the Princeton GPU Hackathon, OpenACC at SC22, updates from GNU Tools Cauldron, the upcoming UK DPU Hackathon, relevant research and more!
GPGPU in Commercial Software: Lessons From Three Cycles of the Adobe Creative...Kevin Goldsmith
This was a talk I gave at NVidia's Graphics Technology Conference in San Jose, California in 2010. On NVidia's site you can find this talk, synced with the audio here: http://nvidia.fullviewmedia.com/gtc2010/0923-k-2051.html
This document discusses computer vision and Mobica's work in the field. It provides an overview of computer vision, including common uses and relevant technologies like OpenCV. Mobica has experience implementing and optimizing computer vision algorithms using technologies like OpenCL. They have worked on projects involving object recognition, image processing, augmented reality, and using computer vision in applications like automotive systems and gesture recognition. Mobica sees opportunities to improve computer vision performance and make it more accessible to developers.
For the full video of this presentation, please visit: https://www.edge-ai-vision.com/2023/07/a-new-open-standards-based-open-source-programming-model-for-all-accelerators-a-presentation-from-codeplay-software/
Charles Macfarlane, Chief Business Officer at Codeplay Software, presents the “New, Open-standards-based, Open-source Programming Model for All Accelerators” tutorial at the May 2023 Embedded Vision Summit.
As demand for AI grows, developers are attempting to squeeze more and more performance from accelerators. Ideally, developers would choose the accelerators best suited to their applications. Unfortunately, today many developers are locked into limited hardware choices because they use proprietary programming models like NVIDIA’s CUDA. The oneAPI project was launched to create an open specification and open-source software that enables developers to write software using standard C++ code and deploy to GPUs from multiple vendors.
OneAPI is an open-source ecosystem based on the Khronos open-standard SYCL with libraries for enabling AI and HPC applications. OneAPI-enabled software is currently deployed on numerous supercomputers, with plans to extend into other market segments. OneAPI is evolving rapidly and the whole community of hardware and software developers is invited to contribute. In this presentation, Macfarlane introduces how oneAPI enables developers to write multi-target software and highlights opportunities for developers to contribute to making oneAPI available for all accelerators.
Atmel - Next-Generation IDE: Maximizing IP Reuse [WHITE PAPER]Atmel Corporation
Atmel® Studio 6 is the integrated development platform (IDP) for developing and debugging Atmel ARM® Cortex™-M and Atmel AVR® microcontroller- (MCU-) based applications. The Atmel Studio 6 IDP gives you a seamless and easy-to-use environment to write, build and debug your applications written in C/C++ or assembly code.
Atmel Studio 6 is free of charge and is integrated with the Atmel Software Framework (ASF)—a large library of free source code with 1,600 ARM and AVR project examples. ASF strengthens the IDP by providing, in the same environment, access to ready-to-use code that minimizes much of the low-level design required for projects. Use the IDP for our wide variety of AVR and ARM Cortex-M processor-based MCUs, including our broadened portfolio of Atmel SAM3 ARM Cortex-M3 and M4 Flash devices.
With the introduction of Atmel Gallery and Atmel Spaces, Atmel Studio 6 further simplifies embedded MCU designs to reduce development time and cost. Atmel Gallery is an online apps store for development tools and embedded software. Atmel Spaces is a cloud-based collaborative development workspace allowing you to host software and hardware projects targeting Atmel MCUs.
For more information, please visit http://www.atmel.com/Microsite/atmel_studio6.
Follow along on Twitter at http://www.twitter.com/Atmel and 'Like' Atmel on Facebook at http://www.facebook.com/atmelcorporation.
Similaire à byteLAKE's CFD Suite (AI-accelerated CFD) - AI Training at the Edge (benchmark) (20)
byteLAKE's AI Products (use cases) (short)byteLAKE
AI Solutions for Industries | Quality Inspection | Data Insights | Predictive Maintenance | AI-accelerated CFD | Self-Checkout | byteLAKE.com
byteLAKE: Empowering Industries with AI Solutions. Embrace cutting-edge technology for advanced quality inspection, data insights, and more. Harness the potential of our CFD Suite, accelerating Computational Fluid Dynamics for heightened productivity. Unlock new possibilities with Cognitive Services: image analytics for precise visual inspection for Manufacturing, sound analytics enabling proactive maintenance for Automotive, and wet line analytics for the Paper Industry. Seamlessly convert data into actionable insights using Data Insights' AI module, enabling advanced predictive maintenance and risk detection. Simplify Restaurant and Retail operations with our efficient self-checkout solution, recognizing meals and groceries and elevating customer satisfaction. Custom AI Development services available for tailored solutions. Discover more at www.byteLAKE.com.
Benefits offered by byteLAKE’s Cognitive Services
Accelerate Data Analytics
- Processing data from various sources, including images, videos, and sensors.
Automate Quality Inspection
- Ensuring high accuracy in inspecting products and processes.
- Eliminating potential human errors for consistent and reliable results.
- Increasing overall quality and reliability.
Optimize Operations and Maintenance
- Reducing unnecessary inspections and lowering maintenance costs.
- Predicting potential failures and downtimes.
Continuous Monitoring
- Offering 24/7/365 monitoring without boredom or distraction.
- Offloading and supporting human operators.
Easy Replication
- Enabling quick deployment.
- Functioning offline without an internet connection.
Continuous Improvement
- The solution can learn and improve over time.
AI in Manufacturing - benefits
Enhanced Productivity
- Streamlining processes for increased productivity.
- Efficient resource allocation based on real-time data.
Customization and Adaptability
- Tailoring AI models to specific manufacturing requirements.
- Adapting to changing production needs seamlessly.
Reduced Downtime
- Minimizing production downtime through predictive maintenance.
- Optimizing machine uptime and reliability.
Data-Driven Decision-Making
- Empowering decision-makers with actionable insights.
- Enabling data-driven strategies for process improvement.
Consistent Quality Control Across the Organization
- Ensuring consistent product quality throughout the production process.
- Meeting industry standards and regulations effortlessly.
Licensing & Cost of Deployment
Cognitive Services
Licensing (annual)
- Annual/monthly licensing plans for Cognitive Services, including upgrades, customer care, and support.
AI Model Development (one time)
- Costs for AI model training and calibration.
- Data Management
- Expenses related to data collection and cleaning.
Hardware, integration, other )one time)
byteLAKE's AI Products (use cases) - presentationbyteLAKE
AI Solutions for Industries | Quality Inspection | Data Insights | Predictive Maintenance | AI-accelerated CFD | Self-Checkout | byteLAKE.com
byteLAKE: Empowering Industries with AI Solutions. Embrace cutting-edge technology for advanced quality inspection, data insights, and more. Harness the potential of our CFD Suite, accelerating Computational Fluid Dynamics for heightened productivity. Unlock new possibilities with Cognitive Services: image analytics for precise visual inspection for Manufacturing, sound analytics enabling proactive maintenance for Automotive, and wet line analytics for the Paper Industry. Seamlessly convert data into actionable insights using Data Insights' AI module, enabling advanced predictive maintenance and risk detection. Simplify Restaurant and Retail operations with our efficient self-checkout solution, recognizing meals and groceries and elevating customer satisfaction. Custom AI Development services available for tailored solutions. Discover more at www.byteLAKE.com.
byteLAKE's AI Products for Industries (2024-02)byteLAKE
AI Solutions for Industries | Quality Inspection | Data Insights | Predictive Maintenance | AI-accelerated CFD | Self-Checkout | byteLAKE.com
byteLAKE: Empowering Industries with AI Solutions. Embrace cutting-edge technology for advanced quality inspection, data insights, and more. Harness the potential of our CFD Suite, accelerating Computational Fluid Dynamics for heightened productivity. Unlock new possibilities with Cognitive Services: image analytics for precise visual inspection for Manufacturing, sound analytics enabling proactive maintenance for Automotive, and wet line analytics for the Paper Industry. Seamlessly convert data into actionable insights using Data Insights' AI module, enabling advanced predictive maintenance and risk detection. Simplify Restaurant and Retail operations with our efficient self-checkout solution, recognizing meals and groceries and elevating customer satisfaction. Custom AI Development services available for tailored solutions. Discover more at www.byteLAKE.com.
AI Solutions for Industries | Quality Inspection | Data Insights | Predictive...byteLAKE
► byteLAKE's Cognitive Services: Unlock the full potential of Industry 4.0 with our comprehensive suite of AI solutions.
• Manufacturing: Employ image analytics for precise visual inspection of processes, parts, components, and products, ensuring impeccable quality control and minimizing errors. Learn more at www.byteLAKE.com/en/manufacturing.
• Automotive: Harness sound analytics to assess and analyze the quality of car engines, enabling proactive maintenance and preventing potential issues. Discover more at www.byteLAKE.com/en/automotive.
• Paper Industry: Implement advanced cameras to continuously monitor the papermaking process, accurately detecting and analyzing the wet line. Optimize production, reduce waste, and enhance efficiency. Explore further at www.byteLAKE.com/en/paper.
• Data Insights: Leverage our AI module for advanced predictive maintenance, detecting risky situations and triggering alarms. Seamlessly turn data from various sources (IoT sensors, documents, online weather forecasts, etc.) into actionable information for better decisions. Learn more at: www.byteLAKE.com/en/DataInsights.
Self-Checkout for Restaurants / AI Restaurants (2024-02)byteLAKE
• Restaurants / Retail: Simplify and expedite the checkout process with our solution for self-checkout stations. Our AI module can recognize meals and groceries effortlessly, sending the list directly to the cashier's machine for efficient self-checkout. Shorten queues and wait times, elevating customer satisfaction. Learn more at www.byteLAKE.com/en/AI4Restaurants.
Self-Checkout (AI for Restautants) - case study by byteLAKE's partner: SimprabyteLAKE
This is a case study one-pager prepared by byteLAKE's partner: Simpra.
Simplify Restaurant and Retail operations with our efficient self-checkout solution, recognizing meals and groceries and elevating customer satisfaction.
• Restaurants / Retail: Simplify and expedite the checkout process with our solution for self-checkout stations. Our AI module can recognize meals and groceries effortlessly, sending the list directly to the cashier's machine for efficient self-checkout. Shorten queues and wait times, elevating customer satisfaction. Learn more at www.byteLAKE.com/en/AI4Restaurants.
byteLAKE: Sztuczna Inteligencja dla Przemysłu i UsługbyteLAKE
Rozwiązania Oparte na Sztucznej Inteligencji dla Przemysłu (SI; ang. AI, Artificial Intelligence) | Monitorowanie Jakości | Analityka Danych z Czujników | Wykrywanie Usterek | Optymalizacja Symulacji CFD | AI dla Kas Samoobsługowych | Rozpoznawanie Produktów | byteLAKE.com (EN) | byteLAKE.pl (PL)
Poznaj byteLAKE: Tworzymy innowacyjne rozwiązania oparte na Sztucznej Inteligencji dla różnorodnych sektorów przemysłu. Nasza pasja polega na wykorzystaniu potencjału potężnej SI/AI do przekształcania danych w wartościowe informacje. Nasze obszary działania obejmują szeroki wachlarz branż: od przemysłu produkcyjnego, poprzez motoryzację, przemysł papierniczy, chemiczny, energetykę, aż po sektor restauracyjny. Dzięki naszemu zestawowi narzędzi CFD Suite, przyspieszamy symulacje związane z obliczeniową mechaniką płynów (CFD; ang. Computational Fluid Dynamics), co pozwala skrócić czas potrzebny na ich wykonanie. Efektem tego jest oszczędność kosztów oraz umożliwienie szybkiego podejmowania kluczowych decyzji. Nasze Cognitive Services to produkt, który usprawnia procesy produkcyjne. Dzięki automatycznej inspekcji wizualnej, ocenie jakości silników opartej na analizie dźwięków oraz monitorowaniu linii mokrej w przemyśle papierniczym, otwieramy przed Tobą szereg nowych możliwości. Cognitive Services obejmują także zaawansowany moduł do analityki danych z czujników (IoT), stanowiący fundament dla systemów Predictive Maintenance, umożliwiających uniknięcie awarii, wykrycie potencjalnych ryzyk oraz generowanie alarmów, by zoptymalizować działanie złożonych infrastruktur. W branży restauracyjnej i detalicznej efektywnie rozpoznajemy produkty i umożliwiamy tworzenie kas samoobsługowych, dzięki czemu eliminujemy kolejki i zwiększamy satysfakcję klientów. Co więcej, tworzymy również oprogramowanie na zamówienie gdzie korzystając z algorytmów SI/AI, pomagamy dokładnie przeanalizować obrazy, filmy, dźwięki oraz dane z czujników. Zapraszamy do zapoznania się z naszą ofertą na stronie internetowej www.byteLAKE.pl. Jeśli preferujesz język angielski, zachęcamy do odwiedzenia wersji anglojęzycznej pod adresem www.byteLAKE.com.
Rozwiązania Oparte na Sztucznej Inteligencji dla Przemysłu (SI; ang. AI, Artificial Intelligence) | Monitorowanie Jakości | Analityka Danych z Czujników | Wykrywanie Usterek | Optymalizacja Symulacji CFD | AI dla Kas Samoobsługowych | Rozpoznawanie Produktów | byteLAKE.com (EN) | byteLAKE.pl (PL)
Poznaj byteLAKE: Tworzymy innowacyjne rozwiązania oparte na Sztucznej Inteligencji dla różnorodnych sektorów przemysłu. Nasza pasja polega na wykorzystaniu potencjału potężnej SI/AI do przekształcania danych w wartościowe informacje. Nasze obszary działania obejmują szeroki wachlarz branż: od przemysłu produkcyjnego, poprzez motoryzację, przemysł papierniczy, chemiczny, energetykę, aż po sektor restauracyjny. Dzięki naszemu zestawowi narzędzi CFD Suite, przyspieszamy symulacje związane z obliczeniową mechaniką płynów (CFD; ang. Computational Fluid Dynamics), co pozwala skrócić czas potrzebny na ich wykonanie. Efektem tego jest oszczędność kosztów oraz umożliwienie szybkiego podejmowania kluczowych decyzji. Nasze Cognitive Services to produkt, który usprawnia procesy produkcyjne. Dzięki automatycznej inspekcji wizualnej, ocenie jakości silników opartej na analizie dźwięków oraz monitorowaniu linii mokrej w przemyśle papierniczym, otwieramy przed Tobą szereg nowych możliwości. Cognitive Services obejmują także zaawansowany moduł do analityki danych z czujników (IoT), stanowiący fundament dla systemów Predictive Maintenance, umożliwiających uniknięcie awarii, wykrycie potencjalnych ryzyk oraz generowanie alarmów, by zoptymalizować działanie złożonych infrastruktur. W branży restauracyjnej i detalicznej efektywnie rozpoznajemy produkty i umożliwiamy tworzenie kas samoobsługowych, dzięki czemu eliminujemy kolejki i zwiększamy satysfakcję klientów. Co więcej, tworzymy również oprogramowanie na zamówienie gdzie korzystając z algorytmów SI/AI, pomagamy dokładnie przeanalizować obrazy, filmy, dźwięki oraz dane z czujników. Zapraszamy do zapoznania się z naszą ofertą na stronie internetowej www.byteLAKE.pl. Jeśli preferujesz język angielski, zachęcamy do odwiedzenia wersji anglojęzycznej pod adresem www.byteLAKE.com.
Tworzymy innowacyjne rozwiązania oparte na Sztucznej Inteligencji dla różnorodnych sektorów przemysłu (SI; ang. AI, Artificial Intelligence). W byteLAKE korzystamy z najnowocześniejszych technologii do automatyzacji kontroli jakości oraz analityki danych, dostosowanych do sektorów produkcji, motoryzacji, papierniczego, chemicznego oraz energetycznego. Dodatkowo, w branży restauracyjnej i detalicznej, skutecznie rozpoznajemy produkty oraz umożliwiamy tworzenie kas samoobsługowych.
Nasze produkty:
► byteLAKE's CFD Suite wykorzystuje sztuczną inteligencję, aby przyspieszyć symulacje z zakresu obliczeniowej mechaniki płynów (CFD; ang. Computational Fluid Dynamics), co pozwala na skrócenie czasu potrzebnego do ich przeprowadzenia. Dzięki temu uzyskujemy oszczędności kosztów oraz umożliwiamy szybkie podejmowanie kluczowych decyzji. Więcej informacji na stronie: www.byteLAKE.com/en/CFDSuite (strona w języku angielskim).
► byteLAKE's Cognitive Services: Wykorzystaj pełny potencjał Przemysłu 4.0 dzięki naszemu kompleksowemu zestawowi rozwiązań opartych na SI.
• Przemysł Produkcyjny: Wykorzystaj analizę obrazów do precyzyjnej kontroli wizualnej procesów, części, komponentów i produktów, zapewniając doskonałą kontrolę jakości i minimalizując błędy. Dowiedz się więcej na stronie www.byteLAKE.com/pl/produkcja.
• Przemysł Motoryzacyjny: Wykorzystaj analizę dźwięku do oceny i analizy jakości silników samochodowych, umożliwiając proaktywną inspekcję jakości i zapobiegając potencjalnym problemom. Odkryj więcej na stronie www.byteLAKE.com/pl/samochodowy.
• Przemysł Papierniczy: Wykorzystaj zaawansowane kamery do ciągłego monitorowania procesu produkcji papieru, dokładnie wykrywając i analizując linię mokrą. Optymalizuj produkcję, zmniejszaj odpady i zwiększaj efektywność. Dowiedz się więcej na stronie: www.byteLAKE.com/pl/papierniczy.
• Analityka Danych: Wykorzystaj nasz moduł SI do analityki danych z czujników (IoT), stanowiący fundament dla systemów Predictive Maintenance, umożliwiających uniknięcie awarii, wykrycie potencjalnych ryzyk oraz generowanie alarmów, by zoptymalizować działanie złożonych infrastruktur. Przekształć dane z różnych źródeł (czujniki IoT, dokumenty, prognozy pogody online itp.) w wartościowe informacje, umożliwiające podejmowanie lepszych decyzji. Dowiedz się więcej na stronie: www.byteLAKE.com/pl/AnalitykaDanych.
• Restauracje / Detal: Uprość i przyspiesz proces płatności dzięki naszemu rozwiązaniu dla kas samoobsługowych. Nasz moduł SI jest w stanie rozpoznać posiłki i produkty spożywcze, przesyłając listę bezpośrednio do kasy. Skróć kolejki i czas oczekiwania, zwiększając satysfakcję klientów. Dowiedz się więcej na stronie: www.byteLAKE.com/pl/SI-dla-Restauracji.
Poznaj wszystkie możliwości, jakie oferują rozwiązania oparte na sztucznej inteligencji. Więcej informacji o Cognitive Services od byteLAKE znajdziesz na stronie: www.byteLAKE.com/pl/CognitiveServices.
► Oprogramowanie SI na Zamówienie
Empowering Industries with Artificial Intelligence Solutions. At byteLAKE, we harness cutting-edge technology to provide advanced quality inspection and data insights tailored for the Manufacturing, Automotive, Paper, Chemical, and Energy sectors. Additionally, we offer self-checkout stations for Restaurants and object recognition solutions for Retail businesses.
Explore our featured products:
► byteLAKE's CFD Suite: Accelerate your Computational Fluid Dynamics (CFD) simulations by leveraging the speed and efficiency of artificial intelligence. Slash simulation times, minimize trial-and-error costs, and supercharge decision-making for heightened productivity. Learn more at www.byteLAKE.com/en/CFDSuite.
► byteLAKE's Cognitive Services: Unlock the full potential of Industry 4.0 with our comprehensive suite of AI solutions.
• Manufacturing: Employ image analytics for precise visual inspection of processes, parts, components, and products, ensuring impeccable quality control and minimizing errors. Learn more at www.byteLAKE.com/en/manufacturing.
• Automotive: Harness sound analytics to assess and analyze the quality of car engines, enabling proactive maintenance and preventing potential issues. Discover more at www.byteLAKE.com/en/automotive.
• Paper Industry: Implement advanced cameras to continuously monitor the papermaking process, accurately detecting and analyzing the wet line. Optimize production, reduce waste, and enhance efficiency. Explore further at www.byteLAKE.com/en/paper.
• Data Insights: Leverage our AI module for advanced predictive maintenance, detecting risky situations and triggering alarms. Seamlessly turn data from various sources (IoT sensors, documents, online weather forecasts, etc.) into actionable information for better decisions. Learn more at: www.byteLAKE.com/en/DataInsights.
• Restaurants / Retail: Simplify and expedite the checkout process with our solution for self-checkout stations. Our AI module can recognize meals and groceries effortlessly, sending the list directly to the cashier's machine for efficient self-checkout. Shorten queues and wait times, elevating customer satisfaction. Learn more at www.byteLAKE.com/en/AI4Restaurants.
Experience the future of AI-driven excellence. Discover more about byteLAKE's Cognitive Services at www.byteLAKE.com/en/CognitiveServices.
► Custom AI Software Development: Choose from our range of AI services, including AI Workshops with inspiring case studies and insights, Edge AI for real-time analytics on images, videos, sounds, and time-series data, Cognitive Automation for the automation of complex tasks with software robots, and HPC for algorithm optimization across various CPU, GPU, and FPGA architectures.
► Incubation: Explore our innovative product brainello, an AI-powered document processing software that works without templates, and Ewa Guard, AI for Drones enabling large area visual analytics.
Advanced Quality Inspection and Data Insights (Artificial Intelligence)byteLAKE
AI Solutions for Industries | Quality Inspection | Data Insights | AI-accelerated CFD | Self-Checkout | byteLAKE.com
byteLAKE: Empowering Industries with AI Solutions. Embrace cutting-edge technology for advanced quality inspection, data insights, and more. Harness the potential of our CFD Suite, accelerating Computational Fluid Dynamics for heightened productivity. Unlock new possibilities with Cognitive Services: image analytics for precise visual inspection for Manufacturing, sound analytics enabling proactive maintenance for Automotive, and wet line analytics for the Paper Industry. Seamlessly convert data into actionable insights using Data Insights' AI module, enabling advanced predictive maintenance and risk detection. Simplify Restaurant and Retail operations with our efficient self-checkout solution, recognizing meals and groceries and elevating customer satisfaction. Custom AI Development services available for tailored solutions. Discover more at www.byteLAKE.com.
► byteLAKE's Cognitive Services: Unlock the full potential of Industry 4.0 with our comprehensive suite of AI solutions.
• Manufacturing: Employ image analytics for precise visual inspection of processes, parts, components, and products, ensuring impeccable quality control and minimizing errors. Learn more at www.byteLAKE.com/en/manufacturing.
• Automotive: Harness sound analytics to assess and analyze the quality of car engines, enabling proactive maintenance and preventing potential issues. Discover more at www.byteLAKE.com/en/automotive.
• Paper Industry: Implement advanced cameras to continuously monitor the papermaking process, accurately detecting and analyzing the wet line. Optimize production, reduce waste, and enhance efficiency. Explore further at www.byteLAKE.com/en/paper.
• Data Insights: Leverage our AI module for advanced predictive maintenance, detecting risky situations and triggering alarms. Seamlessly turn data from various sources (IoT sensors, documents, online weather forecasts, etc.) into actionable information for better decisions. Learn more at: www.byteLAKE.com/en/DataInsights.
Empowering Industries with Artificial Intelligence Solutions. At byteLAKE, we harness cutting-edge technology to provide advanced quality inspection and data insights tailored for the Manufacturing, Automotive, Paper, Chemical, and Energy sectors. Additionally, we offer self-checkout stations for Restaurants and object recognition solutions for Retail businesses.
Explore our featured products:
► byteLAKE's CFD Suite: Accelerate your Computational Fluid Dynamics (CFD) simulations by leveraging the speed and efficiency of artificial intelligence. Slash simulation times, minimize trial-and-error costs, and supercharge decision-making for heightened productivity. Learn more at www.byteLAKE.com/en/CFDSuite.
► byteLAKE's Cognitive Services: Unlock the full potential of Industry 4.0 with our comprehensive suite of AI solutions.
• Manufacturing: Employ image analytics for precise visual inspection of processes, parts, components, and products, ensuring impeccable quality control and minimizing errors. Learn more at www.byteLAKE.com/en/manufacturing.
• Automotive: Harness sound analytics to assess and analyze the quality of car engines, enabling proactive maintenance and preventing potential issues. Discover more at www.byteLAKE.com/en/automotive.
• Paper Industry: Implement advanced cameras to continuously monitor the papermaking process, accurately detecting and analyzing the wet line. Optimize production, reduce waste, and enhance efficiency. Explore further at www.byteLAKE.com/en/paper.
• Data Insights: Leverage our AI module for advanced predictive maintenance, detecting risky situations and triggering alarms. Seamlessly turn data from various sources (IoT sensors, documents, online weather forecasts, etc.) into actionable information for better decisions. Learn more at: www.byteLAKE.com/en/DataInsights.
• Restaurants / Retail: Simplify and expedite the checkout process with our solution for self-checkout stations. Our AI module can recognize meals and groceries effortlessly, sending the list directly to the cashier's machine for efficient self-checkout. Shorten queues and wait times, elevating customer satisfaction. Learn more at www.byteLAKE.com/en/AI4Restaurants.
Experience the future of AI-driven excellence. Discover more about byteLAKE's Cognitive Services at www.byteLAKE.com/en/CognitiveServices.
► Custom AI Software Development: Choose from our range of AI services, including AI Workshops with inspiring case studies and insights, Edge AI for real-time analytics on images, videos, sounds, and time-series data, Cognitive Automation for the automation of complex tasks with software robots, and HPC for algorithm optimization across various CPU, GPU, and FPGA architectures.
► Incubation: Explore our innovative product brainello, an AI-powered document processing software that works without templates, and Ewa Guard, AI for Drones enabling large area visual analytics.
AI Solutions for Industries | Quality Inspection | Data Insights | AI-accelerated CFD | Self-Checkout | byteLAKE.com
byteLAKE: Empowering Industries with AI Solutions. Embrace cutting-edge technology for advanced quality inspection, data insights, and more. Harness the potential of our CFD Suite, accelerating Computational Fluid Dynamics for heightened productivity. Unlock new possibilities with Cognitive Services: image analytics for precise visual inspection for Manufacturing, sound analytics enabling proactive maintenance for Automotive, and wet line analytics for the Paper Industry. Seamlessly convert data into actionable insights using Data Insights' AI module, enabling advanced predictive maintenance and risk detection. Simplify Restaurant and Retail operations with our efficient self-checkout solution, recognizing meals and groceries and elevating customer satisfaction. Custom AI Development services available for tailored solutions. Discover more at www.byteLAKE.com.
• Restaurants / Retail: Simplify and expedite the checkout process with our solution for self-checkout stations. Our AI module can recognize meals and groceries effortlessly, sending the list directly to the cashier's machine for efficient self-checkout. Shorten queues and wait times, elevating customer satisfaction. Learn more at www.byteLAKE.com/en/AI4Restaurants.
Applying Industrial AI Models to Product Quality InspectionbyteLAKE
Seamlessly integrate AI, sensors, and real-time insights for
Predictive Maintenance and the superior Quality Control you are looking for. Elevate your operations with byteLAKE's AI-powered solution.
This article originally appeared on insight.tech: https://www.insight.tech/industry/applying-industrial-ai-models-to-product-quality-inspection
About the Author
Pedro Pereira has covered technology for a quarter century. He has freelanced for some of the biggest names in IT publishing and an extensive list of marketing agencies and technology vendors. He was a pioneer in covering managed services and cloud computing, and currently writes about cybersecurity, IoT, cloud, and space. He holds a degree in Journalism from UMass/Amherst.
Learn more:
https://bit.ly/45gUQVX
via @insight.tech
AI Solutions for Industries | Quality Inspection | Data Insights | AI-accelerated CFD | Self-Checkout | byteLAKE.com
byteLAKE: Empowering Industries with AI Solutions. Embrace cutting-edge technology for advanced quality inspection, data insights, and more. Harness the potential of our CFD Suite, accelerating Computational Fluid Dynamics for heightened productivity. Unlock new possibilities with Cognitive Services: image analytics for precise visual inspection for Manufacturing, sound analytics enabling proactive maintenance for Automotive, and wet line analytics for the Paper Industry. Seamlessly convert data into actionable insights using Data Insights' AI module, enabling advanced predictive maintenance and risk detection. Simplify Restaurant and Retail operations with our efficient self-checkout solution, recognizing meals and groceries and elevating customer satisfaction. Custom AI Development services available for tailored solutions. Discover more at www.byteLAKE.com.
byteLAKE's expertise across NVIDIA architectures and configurationsbyteLAKE
AI Solutions for Industries | Quality Inspection | Data Insights | AI-accelerated CFD | Self-Checkout | byteLAKE.com
byteLAKE: Empowering Industries with AI Solutions. Embrace cutting-edge technology for advanced quality inspection, data insights, and more. Harness the potential of our CFD Suite, accelerating Computational Fluid Dynamics for heightened productivity. Unlock new possibilities with Cognitive Services: image analytics for precise visual inspection for Manufacturing, sound analytics enabling proactive maintenance for Automotive, and wet line analytics for the Paper Industry. Seamlessly convert data into actionable insights using Data Insights' AI module, enabling advanced predictive maintenance and risk detection. Simplify Restaurant and Retail operations with our efficient self-checkout solution, recognizing meals and groceries and elevating customer satisfaction. Custom AI Development services available for tailored solutions. Discover more at www.byteLAKE.com.
CFD Suite (AI-accelerated CFD) - Sztuczna Inteligencja Przyspiesza Symulacje ...byteLAKE
X Kongres #PolskaChemia za nami! Razem z AB Innovation Designer zaprezentowaliśmy rozwiązania #IT dla przemysłu chemicznego. Poniżej nagranie z naszego wystąpienia na temat: CFD Suite (AI-accelerated CFD), #SztucznaInteligencja #SI Przyspiesza Symulacje #CFD.
Prezentacja dostępna jest tutaj: https://www.slideshare.net/byteLAKE/cfd-suite-aiaccelerated-cfd-sztuczna-inteligencja-przyspiesza-symulacje-cfd-prezentacja
Nagranie: https://youtu.be/XxFSLZBwgys
Więcej informacji o byteLAKE:
byteLAKE: Rozwiązania oparte na Sztucznej Inteligencji dla Przemysłu (SI; ang. AI, Artificial Intelligence). Produkty AI dla przemysłu produkcyjnego, motoryzacyjnego, restauracyjnego, papierniczego i chemicznego.
Poznaj byteLAKE: Tworzymy innowacyjne rozwiązania oparte na Sztucznej Inteligencji dla różnych branż. Nasz zestaw narzędzi CFD Suite przyspiesza symulacje dot. obliczeniowej mechaniki płynów (CFD; ang. Computational Fluid Dynamics), skracając ich czas z godzin do minut - co pozwala zaoszczędzić koszty i szybciej podejmować decyzje. Usługi Cognitive Services usprawniają procesy produkcyjne oferując automatyczną inspekcję wizualną, w motoryzacji potrafią dokonać oceny jakości silników a w przemyśle papierniczym monitorować i analizować tzw. linię mokrą. Cognitive Services zawierają również zaawansowany moduł do analizy danych z czujników (IoT, ang. Internet of Things), który stanowi bazę systemów Predictive Maintenance, pozwalających uniknąć awarii, wykrywających ryzyka oraz generujących alarmy i umożliwiających optymalizację działania złożonych infrastruktur. W restauracjach nasze oprogramowanie rozpoznaje posiłki i umożliwia zbudowanie kas samoobsługowych, skracając czas oczekiwania. Tworzymy także oprogramowanie na zamówienie, które przy pomocy algorytmów SI/AI umożliwia precyzyjną analizę obrazów, filmów, dźwięków i danych z czujników. Zapraszamy do odwiedzenia naszej strony internetowej www.byteLAKE.pl. Angielska wersja strony: www.byteLAKE.com.
#AI #ArtificialIntelligence #FluidDynamics #OpenFOAM #Chemia #Technologia #Przemysł #Webinar #Symulacje #Fabryka #Produkcja #Manufacturing #Automotive #Industry40
byteLAKE's Scan&GO - Self-Check-Out Solution for Retail (EuroShop'23)byteLAKE
Introducing Cognitive Services for Restaurants: a powerful add-on software that revolutionizes your operations. Seamlessly recognizing meals and instantly transmitting a comprehensive list to the cashier's machine, this innovative solution is designed to elevate your restaurant experience.
Combined with the Simpra Suite, a comprehensive software package tailored for hotels and restaurants, you unlock a wealth of benefits. From efficient payment processing and order management to seamless reservations, inventory control, and loyalty program administration, Simpra Suite empowers you to streamline your entire operation with ease.
With Cognitive Services for Restaurants and Simpra Suite, you can:
► Enhance Efficiency: Optimize your workflow by automating meal recognition and instantly relaying information to the cashier's machine. Say goodbye to manual processes, reducing errors and saving valuable time.
► Improve Customer Experience: Delight your customers with swift and accurate transactions. By effortlessly managing payments, orders, reservations, and loyalty programs, you can provide a seamless and personalized dining experience.
► Boost Operational Productivity: Take control of your inventory, streamline operations, and gain valuable insights into your business performance. Simpra Suite enables you to make data-driven decisions and optimize your resources effectively.
Experience the power of Cognitive Services for Restaurants bundled with Simpra Suite—an all-encompassing software solution designed to transform your restaurant into a well-oiled machine. Elevate your customer service, increase efficiency, and unlock the full potential of your establishment. Contact us today to explore how our comprehensive suite of tools can revolutionize your business.
Learn more:
• byteLAKE's Cognitive Services for Restaurants: https://www.bytelake.com/en/AI4Restaurants (EN) and https://www.bytelake.com/pl/SI-dla-Restauracji (PL)
• Simpra Suite: www.SimpraSuite.com and www.GetSimpra.com
• Videos:
o EuroGastro'23: https://youtu.be/vY5w6kdy3io
o EuroShop'23: https://youtu.be/RsxTBjaTl2E
o Explainer video: https://youtu.be/FZmfGUyvZtk
Empowering Industries with byteLAKE's High-Performance AIbyteLAKE
Here's our latest #whitepaper we created in collaboration with our amazing friends and partners from Intel Corporation and Lenovo and Lenovo Infrastructure Solutions Group.
► Key Takeaways:
• We have optimized #byteLAKE's #CognitiveServices to #OpenVINO which for #ComputerVision related scenario translated into 8x improved performance. #VNNI (Vector Neural Network Instructions, being part of Intel® Deep Learning Boost i.e. i7 12 gen.) delivered an additional 3x improvement in performance. Both totaled in 8*3 = 24x increase in performance.
• Cognitive Services is also about #data analytics for advanced data analytics (#IoT) to keep operations running smoothly and #PredictiveMaintenance, preventing unplanned downtimes. Here we optimized our algorithms with scikit-learn-intelex which translated into speedups ranging from 1.12 to 22.14 for #AI training and up to 21.17 during inference, depending on the scenario, data types, and sizes.
• Deployment on #Lenovo's #Edge portfolio: we are excited to announce that byteLAKE's Cognitive Services are now seamlessly deployable across the complete range of Lenovo's Edge portfolio. Starting from the #ThinkEdge SE10/SE50 and spanning all the way up to the ThinkEdge SE450 Edge Server, this compatibility encompasses the utilization of Intel® Core™ i7 processor, Intel® Xeon® processors, as well as the latest Intel® #DataCenter #GPU Flex Series #IntelGPU.
• The scalability empowers our customers to effortlessly enhance the performance of their AI workloads by incorporating more powerful #CPUs and #GPUs.
► What's next?
Benchmark results for #IntelGPU. Learn more here: www.linkedin.com/feed/update/urn:li:activity:7064245763471478784 and here: https://www.linkedin.com/feed/update/urn:li:activity:7062790707945611264.
Thank you for all the support in the project: Kristina Macakova, David Clarke, Valerio Rizzo, PhD, Nicholas Borsotto, Andrzej Jankowski, Walter Riviera.
Learn more:
Cognitive Services for Manufacturing #Manufacturing #Industry4.0
www.byteLAKE.com/en/manufacturing
Cognitive Services for Automotive #Automotive #Quality
www.byteLAKE.com/en/automotive
Cognitive Services for Paper Industry #PaperIndustry #PaperMaking #WaterLine
www.bytelake.com/en/ai-for-paper-industry/
Cognitive Services for Restaurants #Restaurant #Hotel #Retail #SelfCheckOut
www.bytelake.com/en/AI4Restaurants
NEW: Cognitive Services includes an IoT/data analytics module for advanced predictive maintenance, preventing unplanned downtimes and ensuring smooth operations i.e. triggering an alarm when risky situations are detected or suggesting optimal configurations for large-scale infrastructures. More details on this new functionality will be shared in Q4 2023.
More: www.byteLAKE.com
Learn more: www.byteLAKE.com
Automatyczny Monitoring Jakości w Fabryce (Sztuczna Inteligencja, byteLAKE)byteLAKE
Automatyczny Monitoring Jakości w Fabryce, czyli jak wykorzystać kamery, mikrofony i czujniki oraz algorytmy uczenia maszynowego (ang. machine learning) czy głębokiego uczenia (ang. deep learning) by zautomatyzować kontrolę jakości oraz inspekcję produktów i procesów w przemyśle.
Dzięki algorytmom sztucznej inteligencji (SI, ang. artificial intelligence, AI) oraz danym z czujników możemy uzyskać odpowiedzi na pytania: dlaczego coś się dzieje? co się prawdopodobnie stanie? jakie są trendy?
Kamery i mikrofony stają się naszymi oczami i uszami, dzięki którym możemy zliczyć obiekty, sprawdzić ich jakość, zidentyfikować problemy typu popsute łożysko, zły kolor, brak etykiety, rysa, pęknięcie.
Dodatkowo możemy odczytać numery identyfikacyjne oraz zautomatyzować procesy kontroli jakości, logistyczne lub uzyskać informację o aktualnej sytuacji w fabryce np. czy w trakcie produkcji papieru tzw. linia mokra nie przekroczyła krytyczne rozmiary, czy w danej strefie nie jest zagrożone bezpieczeństwo pracowników.
Podobne algorytmy znajdują zastosowanie także w innych branżach np. w hotelarskiej czy restauracyjnej. Tutaj kamery mogą przeanalizować produkty wybrane przez klientów i przyspieszyć proces kompletowania listy na rachunku. Dodatkowo mogą na bieżąco zliczać wolne miejsca, monitorować postęp konsumpcji i podpowiedzieć obsłudze komu sprzątnąć stolik lub w której strefie warto dopytać o uzupełnienie napojów.
Uzupełnieniem prezentacji jest poniższy odcinek podcastu, który nagraliśmy razem z Lenovo: https://www.podbean.com/ew/pb-tvcca-11d645e
Więcej na stronach byteLAKE: www.byteLAKE.com, www.byteLAKE.pl oraz na naszym blogu: www.byteLAKE.com/en/blog.
"Frontline Battles with DDoS: Best practices and Lessons Learned", Igor IvaniukFwdays
At this talk we will discuss DDoS protection tools and best practices, discuss network architectures and what AWS has to offer. Also, we will look into one of the largest DDoS attacks on Ukrainian infrastructure that happened in February 2022. We'll see, what techniques helped to keep the web resources available for Ukrainians and how AWS improved DDoS protection for all customers based on Ukraine experience
Freshworks Rethinks NoSQL for Rapid Scaling & Cost-EfficiencyScyllaDB
Freshworks creates AI-boosted business software that helps employees work more efficiently and effectively. Managing data across multiple RDBMS and NoSQL databases was already a challenge at their current scale. To prepare for 10X growth, they knew it was time to rethink their database strategy. Learn how they architected a solution that would simplify scaling while keeping costs under control.
Introduction of Cybersecurity with OSS at Code Europe 2024Hiroshi SHIBATA
I develop the Ruby programming language, RubyGems, and Bundler, which are package managers for Ruby. Today, I will introduce how to enhance the security of your application using open-source software (OSS) examples from Ruby and RubyGems.
The first topic is CVE (Common Vulnerabilities and Exposures). I have published CVEs many times. But what exactly is a CVE? I'll provide a basic understanding of CVEs and explain how to detect and handle vulnerabilities in OSS.
Next, let's discuss package managers. Package managers play a critical role in the OSS ecosystem. I'll explain how to manage library dependencies in your application.
I'll share insights into how the Ruby and RubyGems core team works to keep our ecosystem safe. By the end of this talk, you'll have a better understanding of how to safeguard your code.
In the realm of cybersecurity, offensive security practices act as a critical shield. By simulating real-world attacks in a controlled environment, these techniques expose vulnerabilities before malicious actors can exploit them. This proactive approach allows manufacturers to identify and fix weaknesses, significantly enhancing system security.
This presentation delves into the development of a system designed to mimic Galileo's Open Service signal using software-defined radio (SDR) technology. We'll begin with a foundational overview of both Global Navigation Satellite Systems (GNSS) and the intricacies of digital signal processing.
The presentation culminates in a live demonstration. We'll showcase the manipulation of Galileo's Open Service pilot signal, simulating an attack on various software and hardware systems. This practical demonstration serves to highlight the potential consequences of unaddressed vulnerabilities, emphasizing the importance of offensive security practices in safeguarding critical infrastructure.
[OReilly Superstream] Occupy the Space: A grassroots guide to engineering (an...Jason Yip
The typical problem in product engineering is not bad strategy, so much as “no strategy”. This leads to confusion, lack of motivation, and incoherent action. The next time you look for a strategy and find an empty space, instead of waiting for it to be filled, I will show you how to fill it in yourself. If you’re wrong, it forces a correction. If you’re right, it helps create focus. I’ll share how I’ve approached this in the past, both what works and lessons for what didn’t work so well.
Main news related to the CCS TSI 2023 (2023/1695)Jakub Marek
An English 🇬🇧 translation of a presentation to the speech I gave about the main changes brought by CCS TSI 2023 at the biggest Czech conference on Communications and signalling systems on Railways, which was held in Clarion Hotel Olomouc from 7th to 9th November 2023 (konferenceszt.cz). Attended by around 500 participants and 200 on-line followers.
The original Czech 🇨🇿 version of the presentation can be found here: https://www.slideshare.net/slideshow/hlavni-novinky-souvisejici-s-ccs-tsi-2023-2023-1695/269688092 .
The videorecording (in Czech) from the presentation is available here: https://youtu.be/WzjJWm4IyPk?si=SImb06tuXGb30BEH .
For the full video of this presentation, please visit: https://www.edge-ai-vision.com/2024/06/how-axelera-ai-uses-digital-compute-in-memory-to-deliver-fast-and-energy-efficient-computer-vision-a-presentation-from-axelera-ai/
Bram Verhoef, Head of Machine Learning at Axelera AI, presents the “How Axelera AI Uses Digital Compute-in-memory to Deliver Fast and Energy-efficient Computer Vision” tutorial at the May 2024 Embedded Vision Summit.
As artificial intelligence inference transitions from cloud environments to edge locations, computer vision applications achieve heightened responsiveness, reliability and privacy. This migration, however, introduces the challenge of operating within the stringent confines of resource constraints typical at the edge, including small form factors, low energy budgets and diminished memory and computational capacities. Axelera AI addresses these challenges through an innovative approach of performing digital computations within memory itself. This technique facilitates the realization of high-performance, energy-efficient and cost-effective computer vision capabilities at the thin and thick edge, extending the frontier of what is achievable with current technologies.
In this presentation, Verhoef unveils his company’s pioneering chip technology and demonstrates its capacity to deliver exceptional frames-per-second performance across a range of standard computer vision networks typical of applications in security, surveillance and the industrial sector. This shows that advanced computer vision can be accessible and efficient, even at the very edge of our technological ecosystem.
Skybuffer SAM4U tool for SAP license adoptionTatiana Kojar
Manage and optimize your license adoption and consumption with SAM4U, an SAP free customer software asset management tool.
SAM4U, an SAP complimentary software asset management tool for customers, delivers a detailed and well-structured overview of license inventory and usage with a user-friendly interface. We offer a hosted, cost-effective, and performance-optimized SAM4U setup in the Skybuffer Cloud environment. You retain ownership of the system and data, while we manage the ABAP 7.58 infrastructure, ensuring fixed Total Cost of Ownership (TCO) and exceptional services through the SAP Fiori interface.
Digital Banking in the Cloud: How Citizens Bank Unlocked Their MainframePrecisely
Inconsistent user experience and siloed data, high costs, and changing customer expectations – Citizens Bank was experiencing these challenges while it was attempting to deliver a superior digital banking experience for its clients. Its core banking applications run on the mainframe and Citizens was using legacy utilities to get the critical mainframe data to feed customer-facing channels, like call centers, web, and mobile. Ultimately, this led to higher operating costs (MIPS), delayed response times, and longer time to market.
Ever-changing customer expectations demand more modern digital experiences, and the bank needed to find a solution that could provide real-time data to its customer channels with low latency and operating costs. Join this session to learn how Citizens is leveraging Precisely to replicate mainframe data to its customer channels and deliver on their “modern digital bank” experiences.
For the full video of this presentation, please visit: https://www.edge-ai-vision.com/2024/06/temporal-event-neural-networks-a-more-efficient-alternative-to-the-transformer-a-presentation-from-brainchip/
Chris Jones, Director of Product Management at BrainChip , presents the “Temporal Event Neural Networks: A More Efficient Alternative to the Transformer” tutorial at the May 2024 Embedded Vision Summit.
The expansion of AI services necessitates enhanced computational capabilities on edge devices. Temporal Event Neural Networks (TENNs), developed by BrainChip, represent a novel and highly efficient state-space network. TENNs demonstrate exceptional proficiency in handling multi-dimensional streaming data, facilitating advancements in object detection, action recognition, speech enhancement and language model/sequence generation. Through the utilization of polynomial-based continuous convolutions, TENNs streamline models, expedite training processes and significantly diminish memory requirements, achieving notable reductions of up to 50x in parameters and 5,000x in energy consumption compared to prevailing methodologies like transformers.
Integration with BrainChip’s Akida neuromorphic hardware IP further enhances TENNs’ capabilities, enabling the realization of highly capable, portable and passively cooled edge devices. This presentation delves into the technical innovations underlying TENNs, presents real-world benchmarks, and elucidates how this cutting-edge approach is positioned to revolutionize edge AI across diverse applications.
What is an RPA CoE? Session 1 – CoE VisionDianaGray10
In the first session, we will review the organization's vision and how this has an impact on the COE Structure.
Topics covered:
• The role of a steering committee
• How do the organization’s priorities determine CoE Structure?
Speaker:
Chris Bolin, Senior Intelligent Automation Architect Anika Systems
Fueling AI with Great Data with Airbyte WebinarZilliz
This talk will focus on how to collect data from a variety of sources, leveraging this data for RAG and other GenAI use cases, and finally charting your course to productionalization.
Driving Business Innovation: Latest Generative AI Advancements & Success StorySafe Software
Are you ready to revolutionize how you handle data? Join us for a webinar where we’ll bring you up to speed with the latest advancements in Generative AI technology and discover how leveraging FME with tools from giants like Google Gemini, Amazon, and Microsoft OpenAI can supercharge your workflow efficiency.
During the hour, we’ll take you through:
Guest Speaker Segment with Hannah Barrington: Dive into the world of dynamic real estate marketing with Hannah, the Marketing Manager at Workspace Group. Hear firsthand how their team generates engaging descriptions for thousands of office units by integrating diverse data sources—from PDF floorplans to web pages—using FME transformers, like OpenAIVisionConnector and AnthropicVisionConnector. This use case will show you how GenAI can streamline content creation for marketing across the board.
Ollama Use Case: Learn how Scenario Specialist Dmitri Bagh has utilized Ollama within FME to input data, create custom models, and enhance security protocols. This segment will include demos to illustrate the full capabilities of FME in AI-driven processes.
Custom AI Models: Discover how to leverage FME to build personalized AI models using your data. Whether it’s populating a model with local data for added security or integrating public AI tools, find out how FME facilitates a versatile and secure approach to AI.
We’ll wrap up with a live Q&A session where you can engage with our experts on your specific use cases, and learn more about optimizing your data workflows with AI.
This webinar is ideal for professionals seeking to harness the power of AI within their data management systems while ensuring high levels of customization and security. Whether you're a novice or an expert, gain actionable insights and strategies to elevate your data processes. Join us to see how FME and AI can revolutionize how you work with data!
Monitoring and Managing Anomaly Detection on OpenShift.pdfTosin Akinosho
Monitoring and Managing Anomaly Detection on OpenShift
Overview
Dive into the world of anomaly detection on edge devices with our comprehensive hands-on tutorial. This SlideShare presentation will guide you through the entire process, from data collection and model training to edge deployment and real-time monitoring. Perfect for those looking to implement robust anomaly detection systems on resource-constrained IoT/edge devices.
Key Topics Covered
1. Introduction to Anomaly Detection
- Understand the fundamentals of anomaly detection and its importance in identifying unusual behavior or failures in systems.
2. Understanding Edge (IoT)
- Learn about edge computing and IoT, and how they enable real-time data processing and decision-making at the source.
3. What is ArgoCD?
- Discover ArgoCD, a declarative, GitOps continuous delivery tool for Kubernetes, and its role in deploying applications on edge devices.
4. Deployment Using ArgoCD for Edge Devices
- Step-by-step guide on deploying anomaly detection models on edge devices using ArgoCD.
5. Introduction to Apache Kafka and S3
- Explore Apache Kafka for real-time data streaming and Amazon S3 for scalable storage solutions.
6. Viewing Kafka Messages in the Data Lake
- Learn how to view and analyze Kafka messages stored in a data lake for better insights.
7. What is Prometheus?
- Get to know Prometheus, an open-source monitoring and alerting toolkit, and its application in monitoring edge devices.
8. Monitoring Application Metrics with Prometheus
- Detailed instructions on setting up Prometheus to monitor the performance and health of your anomaly detection system.
9. What is Camel K?
- Introduction to Camel K, a lightweight integration framework built on Apache Camel, designed for Kubernetes.
10. Configuring Camel K Integrations for Data Pipelines
- Learn how to configure Camel K for seamless data pipeline integrations in your anomaly detection workflow.
11. What is a Jupyter Notebook?
- Overview of Jupyter Notebooks, an open-source web application for creating and sharing documents with live code, equations, visualizations, and narrative text.
12. Jupyter Notebooks with Code Examples
- Hands-on examples and code snippets in Jupyter Notebooks to help you implement and test anomaly detection models.
byteLAKE's CFD Suite (AI-accelerated CFD) - AI Training at the Edge (benchmark)
1. CFD Suite
(AI-accelerated CFD)
AI TRAINING
BENCHMARK
This report summarizes byteLAKE’s benchmark results of the
recommended hardware platform for byteLAKE’s CFD Suite’s AI
training at the Edge. Key takeaways are at the end of the report.
Artificial
Intelligence
AI-accelerated
CFD
AI-assisted
Visual Inspection
AI-powered Big
Data Analytics
Complex Tasks
Automation
Chemical
Industry
Paper Industry
Manufacturing
Industry 4.0
byteLAKE
Europe & USA
+48 508 091 885
+48 505 322 282
+1 650 735 2063
www.byteLAKE.com
2. CFD Suite (AI-accelerated CFD): AI Training Benchmark Jul-22 2
Foreword
Last year we published CFD Suite's scalability report. The goal was to address our clients' and
partners' fundamental question of how CFD Suite's AI Training performs when deployed in a multi-
node HPC (High-Performance Computing) configuration. The focus at that time was to help them
understand the benefits of performing the AI Training on GPU-enabled architectures vs. CPU-only
clusters which at that time were more common in the CFD (Computational Fluid Dynamics) industry.
The conclusion was that: "we can assume that 4 nodes with Intel Xeon Gold CPUs will give
comparable performance as a single NVIDIA V100 Tensor Core GPU node" and one can read more at
byteLAKE's blog (link: https://marcrojek.medium.com/bytelakes-cfd-suite-ai-accelerated-cfd-hpc-
scalability-report-25f9786e6123) or by downloading the full report from byteLAKE's website (link:
www.byteLAKE.com/en/CFDSuite or directly at: https://www.bytelake.com/en/download/4013/).
As we progressed with the product development and related research efforts, we significantly
changed the underlying AI architectures to better address our clients' and partners' needs. This effort
led to improved accuracy of AI predictions but also better performance and increased automation.
Previously, CFD Suite's users had to manually configure the number of iterations that the traditional
CFD solver had to perform before AI could take over and generate the prediction. Now AI has taken
over that task as well and the users no longer need to worry about how to properly calibrate CFD
Suite for optimal results. That allowed us to implement a mechanism that could find the best tradeoff
between performance and accurate predictions, ultimately improving the overall quality of
predictions. Previously, CFD Suite's AI Training phase's performance could only be improved by adding
more nodes within a multi-node HPC architecture. Thanks to our latest upgrade, it can now benefit
from many NVIDIA GPU cards within a single node. A much-awaited feature for those who prefer such
setups.
„We have significantly changed the architecture of the underlying AI within the CFD Suite. With the
mechanisms like dynamic generating of the learning samples, we are now able to fully utilize multiple
GPU cards within one node and provide better accuracy. Unlike in the previous versions, where CFD
Suite’s AI training performance could only be increased by adding more nodes. Now we can greatly
benefit from having more accelerators within a single node.”, said Krzysztof Rojek, DSc, PhD, CTO at
byteLAKE.
Having said the above, let us warmly invite you to read another report about how we benchmarked
the performance of CFD Suite's AI Training with a special focus on our partners and clients who prefer
performing it on the edge server type of configurations. With that in mind, we have also selected a
byteLAKE’s recommended hardware platform, validated specifically for that purpose. Enjoy!
Marcin Rojek, Mariusz Kolanko, byteLAKE’s co-founders.
3. CFD Suite (AI-accelerated CFD): AI Training Benchmark Jul-22 3
Introduction
A few years ago, when we started extensive research at byteLAKE in the space of CFD simulations
(Computational Fluid Dynamics), we naturally drifted towards experiments with various different
hardware configurations. Our focus has always been on performance though. It is no different today
although we think we might expand our research in the future towards other comparably exciting
aspects and a broader context of how AI (Artificial Intelligence) can deliver value for industries
performing CFD simulations. We have been describing these efforts and results including case studies
in a blog post series which can be found here: www.byteLAKE.com/en/AI4CFD-toc. This report,
however, contains byteLAKE’s conclusions and recommendations about edge hardware configuration
for the AI Training phase, required for the byteLAKE’s CFD Suite to deliver its predictions and
eventually significantly reduce time to results in CFD simulations.
What is byteLAKE’s CFD Suite?
It is a Collection of innovative AI Models for Computational Fluid Dynamics (CFD) acceleration. It is a
Deep Learning, data-driven solution which is currently available for the chemical industry, reducing
mixing simulation time from hours to minutes. While one can read more about the product on
byteLAKE’s website at www.byteLAKE.com/en/CFDSuite or find us listed in various independent
benchmarks i.e. Discover 5 Top Startups working on Computational Fluid Dynamics (startus-
insights.com), CFD Suite works in the following way:
• first, it needs to be trained. We typically need some number of historic simulations to train the
embedded AI models about the physical phenomenon, its parameters, corner cases, etc. The
exact number of such historic simulations varies across phenomena but the rule of thumb is
that 30–100 of such simulations is enough to be able to address various possible input
configurations (speed of mixing, viscosity, pressure, etc.) and geometries;
• once the AI Training completes, CFD Suite can be used for inference purposes, meaning it can
predict the results of the CFD simulations. CFD Suite starts by calling a traditional CFD solver
first. CFD Suite analyzes its initial results and when its AI “feels” it is ready to predict, it takes
over the simulation and generates its final result (steady-state). It does so within the seconds
and including the overhead, CFD Suite has been able to reduce the time of chemical mixing
simulations from 4–8 hrs. to 10–20 mins and keep the accuracy of predictions north from 93%.
Example results of the CFD Simulation performed by a traditional CFD Solver and CFD Suite are visible
on the image below.
4. CFD Suite (AI-accelerated CFD): AI Training Benchmark Jul-22 4
Figure 1. CFD simulation results vs. byteLAKE’s CFD Suite’s AI predictions.
Industry of focus and a reference hardware
Our current focus is on the chemical industry and how AI can deliver value there through the
acceleration of various CFD workloads. While considering various hardware options to perform the AI
Training part, we decided to start with an edge configuration that would meet the following criteria:
• a standalone configuration that does not require any server-related specific or advanced
infrastructure (racks, server rooms) — something you just plug into a power outlet and is ready
to work;
• edge-type of the device but something definitely stronger than laptops and definitely more
flexible than desktop PCs (many CPUs, many GPUs, huge amount of RAM, large and fast
storage);
• ready for some of the most powerful GPUs as the main workload here is the AI training.
NVIDIA was our preferred vendor as we have had a great experience with almost all of their
GPUs and APIs.
5. CFD Suite (AI-accelerated CFD): AI Training Benchmark Jul-22 5
In addition, we wanted also:
• an optimal performance per value (installed at the edge, edge-sized, edge-priced);
• to ensure security to keep the data in-house without a need to send anything to the Cloud;
• to ensure no external data transfer and enable all computing locally;
• compatibility for a variety of communications ports for connection flexibility;
• a modular structure for easy hardware upgrades.
After all, we needed a single hardware option that we could recommend to the clients who need
something more than a laptop and are not yet ready or might not need complex server
configurations, not to mention the HPC (High-Performance Computing) as such. We did not want to
consider Cloud options at that time but we might do so in the future as we expand our offering
towards aaS (as-a-Service) options. Eventually we decided to pick Lenovo ThinkEdge SE450 Edge
Server (Product Guide, Press Release) powered by 2 NVIDIA A100 80GB Tensor Core GPUs (Learn
More).
We picked NVIDIA’s A100 80GB variant to ensure that the cards are capable of handling relatively
large mesh sizes of the CFD simulations during the AI training of the CFD Suite. Although we currently
focus on simulations of around 5 million cells, we will definitely go north of that number in the
nearest future.
6. CFD Suite (AI-accelerated CFD): AI Training Benchmark Jul-22 6
Figure 2. byteLAKE’s CFD Suite (AI-accelerated CFD) — recommended hardware for AI training at the
Edge
“We at byteLAKE picked Lenovo’s SE450 edge HPC server as a recommended edge platform for the AI
Training phase of the CFD Suite (AI-accelerated CFD) product. We love its design and flexibility and are
convinced our clients in the chemical industry will appreciate how easily it enables the AI Training
capabilities at the edge with NVIDIA GPUs”, said Marcin Rojek, byteLAKE’s Co-Founder.
7. CFD Suite (AI-accelerated CFD): AI Training Benchmark Jul-22 7
Benchmark
We examined the performance and memory requirements of byteLAKE’s CFD Suite that uses AI to
accelerate CFD Simulations (hereinafter also referred to as a “framework”). CFD Suite is based on a
data-driven model, where in the first step we need to train the model using CFD simulations executed
with a traditional CFD solver (historic simulations build-up in a form of a training dataset). Then CFD
Suite can provide the prediction that allows us to significantly reduce the simulation execution time.
In this benchmark, we focus on the AI training part that requires highly parallel hardware to create an
accurate model. To train the model, we used a real-life scenario, where our framework takes 10 initial
iterations generated by the CFD solver as an input and returns the final (steady-state) iteration. Our
dataset includes 50 such CFD simulations. From each simulation, the framework generates 20
different packages of inputs and a single iteration as an output. As a result, we use 1000 packages
containing 10 input and 1 output iteration.
All the simulations are generated with the 3-dimensional rhoSimpleFoam solver. The rhoSimpleFoam
is a steady-state solver for compressible, turbulent flow, using the SIMPLE (Semi-Implicit Method for
Pressure Linked Equations) algorithm. That means that a pressure equation is solved and the density
is related to the pressure via an equation of state. We generate 3 different mesh configurations:
• the mesh of size 32 768 cells;
• the mesh of size 262 144 cells;
• the mesh of size 884 736 cells.
The training of our model generates a set of sub-models for each quantity used by the solver. We use
here 2 types of quantities: scalar quantities (pressure, temperature, …), and the vector quantity
(velocity). Our model trains them sequentially one by one, so for the purpose of this benchmark, we
focus on analyzing a single scalar and vector quantity.
Hardware and software environment
This benchmark has been executed on the Lenovo SE450 node. The node is equipped with a single
Intel Xeon Gold CPU and 2xNVIDIA A100 GPUs. Moreover, the performance results are compared with
a single node equipped with 2xNVIDIA V100 GPUs. The server node includes 128GB of the host
memory. All the platforms that we have benchmarked will be further referred to as:
• A100 – NVIDIA A100 GPU with 80GB of GPU global memory;
• V100 – NVIDIA V100 GPU with 16GB of GPU global memory;
• CPU or Gold - Intel Xeon Gold 6330N CPU clocked 2.20GHz with 28 physical (56 logical) cores.
The software environment of the SE450 node includes:
• Ubuntu 20.04.4 LTS (GNU/Linux 5.13.0-51-generic x86_64) OS;
8. CFD Suite (AI-accelerated CFD): AI Training Benchmark Jul-22 8
• NVIDIA Driver version: 510.73.05;
• CUDA Version: 11.6;
• cuDNN version: 8.4.0;
• TensorFlow version: 2.9.0;
• Keras: the Python deep learning API version: 2.9.1;
• Dataset: float32 data type (single-precision arithmetic).
A100 performance results and host memory requirements for a scalar quantity
In the table below we included the performance results for the scalar quantity training. The
performance results include a different batch (the number of samples that are propagated through
the network) and mesh size. We examined here the host memory requirements, and execution time
using a single A100 GPU and 2xA100 GPUs. To train the network we used 1000 epochs. We have also
included here the average execution time per epoch and a speedup of 2xA100 over 1xA100 GPU.
1xA100 2xA100
batch mesh
[cells]
host memory
[GiB]
time [s] time/epoch
[s]
time [s] time/epoch
[s]
Speedup
1 32768 9.7 19020 19.02 10316 10.32 1.84
2 32768 9.7 10021 10.02 5634 5.63 1.78
4 32768 9.8 6025 6.03 3428 3.43 1.76
8 32768 10.0 3028 3.03 1618 1.62 1.87
16 32768 11.0 2340 2.34 1316 1.32 1.78
32 32768 12.0 1460 1.46 791 0.79 1.85
64 32768 16.0 1750 1.75 982 0.98 1.78
128 32768 25.0 1126 1.13 628 0.63 1.79
1 262144 30.0 22023 22.02 11809 11.81 1.86
2 262144 31.0 14029 14.03 7879 7.88 1.78
4 262144 33.0 9037 9.04 4790 4.79 1.89
8 262144 36.0 6053 6.05 3258 3.26 1.86
16 262144 41.0 5910 5.91 3303 3.30 1.79
32 262144 52.0 5166 5.17 2917 2.92 1.77
64 262144 67.0 5332 5.33 2927 2.93 1.82
128 262144 109.0 5634 5.63 3093 3.09 1.82
1 884736 89.0 30031 30.03 16901 16.90 1.78
2 884736 92.0 22046 22.05 12165 12.16 1.81
4 884736 96.0 18074 18.07 9818 9.82 1.84
8 884736 104.0 17142 17.14 9620 9.62 1.78
16 884736 116.0 16278 16.28 9184 9.18 1.77
Table 1. Performance results for the scalar quantity training.
9. CFD Suite (AI-accelerated CFD): AI Training Benchmark Jul-22 9
The best performance results for each mesh are included in the table below:
1xA100 2xA100
batch mesh
[cells]
host memory
[GiB]
time [s] time/epoch
[s]
time [s] time/epoch
[s]
Speedup
128 32768 25 1126 1.13 628 0.63 1.79
32 262144 52 5166 5.17 2917 2.92 1.77
16 884736 116 16278 16.28 9184 9.18 1.77
Table 2. Best performance results for each mesh (scalar quantity training).
The performance results for a single A100 are plotted in the figure below (the lower the better):
Figure 3. A100 performance results (scalar quantity training).
0.00
5.00
10.00
15.00
20.00
25.00
30.00
1 2 4 8 16 32 64 128
time/epoch
[s]
batch
Execution time/bach size for 3 meshes (scalar)
32768
262144
884736
10. CFD Suite (AI-accelerated CFD): AI Training Benchmark Jul-22 10
The performance results for 2 x A100 are plotted in the figure below (the lower the better):
Figure 4. Two A100 performance results (scalar quantity training).
The host memory requirements depending on a batch size are listed below (the lower the better):
Figure 5. Host memory requirements for various batch sizes (scalar quantity training).
0.00
5.00
10.00
15.00
20.00
25.00
30.00
1 2 4 8 16 32 64 128
time/epoch
[s]
batch
Execution time/bach size for 3 meshes (scalar)
32768
262144
884736
0.0
20.0
40.0
60.0
80.0
100.0
1 2 4 8 16 32 64 128
host
memory
[GiB]
batch
Host memory/bach size for 3 meshes (scalar)
32768
262144
884736
11. CFD Suite (AI-accelerated CFD): AI Training Benchmark Jul-22 11
The performance comparison for each mesh between 1x and 2x A100 is plotted below (the lower the
better):
Figure 6. Performance comparison: one vs. two A100 GPUs (scalar quantity training).
Conclusions:
• The speedup between 1xA100 and 2xA100 is stable for all 3 meshes and varies from 1.76 to
1.89.
• It gives the efficiency of up to 0.95 which shows good scalability of the framework.
• The best performance is achieved using a batch of sizes 128, 32, and 16 for meshes of sizes
32768, 262144, and 884736, respectively.
• We observe that memory requirements increase when the batch increases and for the mesh of
size 884 736, the batch >16 exceeds the available host memory.
0.00
5.00
10.00
15.00
32768 262144 884736
time/epoch
[s]
mesh size [cells]
Performance comparison between 1x and
2xA100 (scalar)
1xA100 2xA100
12. CFD Suite (AI-accelerated CFD): AI Training Benchmark Jul-22 12
A100 performance results and host memory requirements for a vector quantity
In the table below, we included the performance results of the vector quantity training. The
performance results include a different batch and mesh size. We examined here the host memory
requirements, and execution time using a single A100 GPU and 2xA100 GPUs. As before, to train the
network we used 1000 epochs. We have also included here the average execution time per epoch and
speedup of 2xA100 over 1xA100 GPU.
1xA100 2xA100
batch mesh
[cells]
host memory
[GiB]
time [s] time/epoch
[s]
time [s] time/epoch
[s]
Speedup
1 32768 15 20021 20.02 10980 10.98 1.82
2 32768 15 12025 12.03 6825 6.83 1.76
4 32768 16 7028 7.03 3884 3.88 1.81
8 32768 17 4033 4.03 2238 2.24 1.80
16 32768 17 3048 3.05 1736 1.74 1.76
32 32768 23 2076 2.08 1140 1.14 1.82
64 32768 28 2141 2.14 1162 1.16 1.84
128 32768 48 2315 2.32 1235 1.24 1.87
1 262144 79 28030 28.03 15697 15.70 1.79
2 262144 82 19040 19.04 10552 10.55 1.80
4 262144 87 16067 16.07 8940 8.94 1.80
8 262144 93 15125 15.13 8425 8.42 1.80
16 262144 109 14237 14.24 8072 8.07 1.76
Table 3. Performance results for the vector quantity training
The best performance results for each mesh are included in the table below:
1xA100 2xA100
batch mesh
[cells]
host memory
[GiB]
time [s] time/epoch
[s]
time [s] time/epoch
[s]
Speedup
32 32768 23 2076 2.08 1140 1.14 1.82
16 262144 109 14237 14.24 8072 8.07 1.76
Table 4. Best performance results for each mesh (vector quantity training)
13. CFD Suite (AI-accelerated CFD): AI Training Benchmark Jul-22 13
The performance results for a single A100 are plotted in the figure below (the lower the better):
Figure 7. A100 performance results (vector quantity training)
The performance results for 2 x A100 are plotted in the figure below (the lower the better):
Figure 8. Two A100 performance results (vector quantity training)
0.00
5.00
10.00
15.00
20.00
25.00
30.00
1 2 4 8 16 32 64 128
time/epoch
[s]
batch
Execution time/bach size for 2 meshes (vector)
32768
262144
0.00
2.00
4.00
6.00
8.00
10.00
12.00
14.00
16.00
1 2 4 8 16 32 64 128
time/epoch
[s]
batch
Execution time/bach size for 2 meshes (vector)
32768
262144
14. CFD Suite (AI-accelerated CFD): AI Training Benchmark Jul-22 14
The host memory requirements depending on a batch size are listed below (the lower the better):
Figure 9. Host memory requirements for various batch sizes (vector quantity training).
The performance comparison for each mesh between 1x and 2x A100 is plotted below (the lower the
better):
Figure 10. Performance comparison: one vs. two A100 GPUs (vector quantity training).
0
20
40
60
80
100
120
1 2 4 8 16 32 64 128
host
memory
[GiB]
batch
Host memory/bach size for 2 meshes (vector)
32768
262144
0.00
2.00
4.00
6.00
8.00
10.00
12.00
14.00
16.00
32768 262144
time/epoch
[s]
mesh size [cells]
Performance comparison between 1x and
2xA100 (vector)
1xA100
2xA100
15. CFD Suite (AI-accelerated CFD): AI Training Benchmark Jul-22 15
Conclusions:
• The speedup between 1xA100 and 2xA100 is stable for all 3 meshes and varies from 1.76 to
1.87.
• It gives the efficiency up to 0.95 which confirms good scalability of the framework regardless
of the type of quantity (scalar, vector).
• The best performance is achieved using a batch of sizes 32, and 16 for meshes of sizes 32768,
and 262144, respectively.
• The tested scenario requires more than 128GB of the host memory to train the network for the
mesh of size 884736 with the vector quantity.
• Comparing this benchmark with our previous one (available here:
https://marcrojek.medium.com/bytelakes-cfd-suite-ai-accelerated-cfd-hpc-scalability-report-
25f9786e6123) or by downloading the full report from byteLAKE's website
www.byteLAKE.com/en/CFDSuite here: https://www.bytelake.com/en/download/4013/), we
observe that in the current version of the byteLAKE’s CFD Suite the scalability within a node is
much more profitable. This has resulted from the fact, that the current AI model is much more
compute-intensive - includes more layers. In this version of our framework, we provided a
mechanism that dynamically generates a set of training samples from a single input
simulation, which also improves the dataset size and reduces the memory transfer.
16. CFD Suite (AI-accelerated CFD): AI Training Benchmark Jul-22 16
Comparison of performance and memory requirements between vector and scalar
quantities
The table below contains the host memory requirements between vector and scalar quantity
depending on batch size. This comparison is performed for a mesh of size 32 768 cells.
host memory [GiB]
batch scalar vector Ratio
1 9.7 15.0 1.55
2 9.7 15.0 1.55
4 9.8 16.0 1.63
8 10.0 17.0 1.70
16 11.0 17.0 1.55
32 12.0 23.0 1.92
64 16.0 28.0 1.75
128 25.0 48.0 1.92
Table 5. Host memory requirements. Mesh size: 32 768 cells.
The table below contains the host memory requirements between vector and scalar quantity
depending on batch size. This comparison is performed for a mesh of size 262 144 cells.
host memory [GiB]
batch scalar vector Ratio
1 30.0 79.0 2.63
2 31.0 82.0 2.65
4 33.0 87.0 2.64
8 36.0 93.0 2.58
16 41.0 109.0 2.66
32 52.0
64 67.0
128 109.0
Table 6. Host memory requirements. Mesh size: 262 144 cells.
The performance comparison between the vector and scalar quantities is shown in the figure below
(the lower the better):
17. CFD Suite (AI-accelerated CFD): AI Training Benchmark Jul-22 17
Figure 11. AI Training performance for various mesh sizes (vector and scalar @ 1*A100 vs. 2*A100)
The host memory requirements for a scenario with the vector and scalar quantities are shown in the
figure below. The results include the mesh of size 32768 cells (the lower the better).
Figure 12. Host memory requirements (vector, scalar) for AI Training. Mesh size: 32 768.
The host memory requirements for a scenario with the vector and scalar quantities are shown in the
figure below. The results include the mesh of size 262144 cells (the lower the better).
0.00
5.00
10.00
15.00
32768 262144 884736
time/epoch
[s]
mesh size [cells]
Performance comparison between vector and
scalar fields
1xA100 (vector) 2xA100 (vector) 1xA100 (scalar) 2xA100 (scalar)
0.0
10.0
20.0
30.0
40.0
50.0
60.0
1 2 4 8 16 32 64 128
host
memory
[GiB]
batch
Host memory/batch size for mesh of size 32768
scalar vector
18. CFD Suite (AI-accelerated CFD): AI Training Benchmark Jul-22 18
Figure 13. Host memory requirements (vector, scalar) for AI Training. Mesh size 262 144.
Conclusions:
• Both, scalar and vector quantities, are efficiently distributed across 2 x A100 GPUs with an
efficiency of up to 0.95.
• Vector quantity is executed from 1.81x to 2.77x slower than the scalar quantity, depending on
the mesh size.
• The memory requirements of the vector quantity are from 1.55x to 1.92x higher than the
scalar quantity for the mesh of size 32768 cells.
• The memory requirements of the vector quantity are more than 2.6x than of the scalar
quantity for the mesh of size 262144 cells.
• Based on byteLAKE’s case study, the full AI Training, assuming a single vector quantity, and 6
scalar quantities takes ~1h36min (1140s+791s*6) for a mesh of size 32 768, ~7h6min
(8072s+2917s*6) for a mesh of size 262 144, and approximately up to 24h (30000s+9184s*6)
for a mesh of size 884 736 (the execution time of the vector quantity is approximated based on
other results) using 2 x A100 GPUs.
0.0
20.0
40.0
60.0
80.0
100.0
120.0
1 2 4 8 16 32 64 128
host
memory
[GiB]
batch
Host memory/batch size for mesh of size 262144
scalar vector
19. CFD Suite (AI-accelerated CFD): AI Training Benchmark Jul-22 19
Platforms comparison: A100 vs. V100 vs. CPU
In this section, we compare the performance results of A100 GPUs with other platforms (V100 GPU,
Gold CPU). The first experiment allowed us to determine the best batch size for each platform. The
results are listed in the table below.
batch CPU [s] V100 [s] Speedup
(V100 vs
CPU)
A100 [s] Speedup
(A100 vs
CPU)
1 121.13 15.02 8.07 22.02 5.50
2 95.20 11.02 8.64 14.03 6.79
4 85.36 12.05 7.08 9.04 9.45
8 71.59 11.09 6.46 6.05 11.83
16 61.00 10.17 6.00 5.91 10.32
32 60.00 9.31 6.44 5.17 11.61
64 56.00 13.90 4.03 5.33 10.50
128 51.01 13.00 3.92 5.63 9.05
Table 7. Best batch size for each platform: Gold CPU, V100, A100
In the figure below we plot the performance comparison between A100, V100, and CPU gold
depending on the batch size (the lower the better). In the red rectangles, we marked the best batch
size for each platform.
Figure 14. Performance results
0.00
20.00
40.00
60.00
80.00
100.00
120.00
140.00
1 2 4 8 16 32 64 128
time/epoch
[s]
batch
Performance comparison for mesh of size 262144
1xV100 [s] 1xA100 [s] CPU [s]
20. CFD Suite (AI-accelerated CFD): AI Training Benchmark Jul-22 20
We selected the best batch size for each platform and each mesh size and compared the
performance results. In other words, we compared the performance of each platform’s best
configuration. The table below includes the platform name, selected batch size, and mesh size end
execution time for each test. It also contains speedups of the GPUs over the CPU execution.
1xGPU 2xGPU
platform batch mesh
[cells]
time [s] time/epoch
[s]
Speedup time [s] time/epoch
[s]
Speedup
A100 128 32768 1126 1.13 5.91 628 0.63 10.60
V100 16 32768 2031 2.03 3.28 1095 1.09 6.08
Gold 128 32768 6658 6.66 1
A100 32 262144 5166 5.17 9.87 2917 2.92 17.49
V100 32 262144 9312 9.31 5.48 4967 4.97 10.27
Gold 128 262144 51006 51.01 1
A100 16 884736 16278 16.28 10.87 9184 9.18 19.27
V100 4 884736 29121 29.12 6.08 16576 16.58 10.68
Gold 128 884736 177022 177.02 1
Table 8. Performance results.
The performance comparison between A100, V100, and CPU gold for a mesh of size 32768 is listed
below (the lower the better):
Figure 15. Performance comparison. Mesh size: 32 768.
0.00
1.00
2.00
3.00
4.00
5.00
6.00
7.00
A100x32768 V100x32768 Goldx32768
time/epoch
[s]
mesh size [cells]
Performance comparison for mesh of size 32768
1xGPU 2xGPU
21. CFD Suite (AI-accelerated CFD): AI Training Benchmark Jul-22 21
The performance comparison between A100, V100, and CPU gold for a mesh of size 262144 is listed
below (the lower the better):
Figure 16. Performance comparison. Mesh size: 262 144.
The performance comparison between A100, V100, and CPU gold for a mesh of size 884736 is listed
below (the lower the better):
Figure 17. Performance comparison. Mesh size: 884 736.
0.00
10.00
20.00
30.00
40.00
50.00
60.00
A100x262144 V100x262144 Goldx262144
time/epoch
[s]
mesh size [cells]
Performance comparison for mesh of size 262144
1xGPU 2xGPU
0.00
20.00
40.00
60.00
80.00
100.00
120.00
140.00
160.00
180.00
200.00
A100x884736 V100x884736 Goldx884736
time/epoch
[s]
mesh size [cells]
Performance comparison for mesh of size 884736
1xGPU 2xGPU
22. CFD Suite (AI-accelerated CFD): AI Training Benchmark Jul-22 22
Conclusions:
• Different platforms used individually optimized configurations for each mesh size to measure
the performance of byteLAKE’s CFD Suite.
• For some batch sizes (size 1 and 2 – see Table 7) the V100 GPU outperforms the A100 (1.5x
speedup for a batch of size 1), which is resulted from the facts, that:
o A100 requires a higher batch size than V100 to fully utilize the GPU compute resources;
o A100 has a higher number of SMs (Streaming Multiprocessors) than V100, so the
number of parallel tasks to utilize the resources needs to be higher;
The base frequency of V100 is higher than of A100, so for a low GPU utilization V100
outperforms A100.
• Overall, with the optimized configs, the A100 GPU is ~1.8x faster than V100 GPU.
• With the optimized configs, the A100 GPU gives a speedup from 5.9x to 10.9x compared to the
CPU gold, while the V100 is from 3.3x to 6x faster. This is as expected and aligned with our
previous benchmarks where we concluded that GPUs were preferred for AI Training workloads.
• Using an entire SE450 node, 2 x A100 gives from 10.6x to 19.3x speedup over CPU gold, while
the V100 is from 6x to 10.7x faster than CPU gold.
• The higher mesh the higher speedup is achieved using GPUs over the CPU platform.
23. CFD Suite (AI-accelerated CFD): AI Training Benchmark Jul-22 23
Memory limits
The goal of the final test was to measure the maximum mesh that could be trained using A100 80GB
GPU with our framework. To perform this test the following assumptions were made:
• The host memory requirements are not taken into account, since we generated an artificial
dataset for GPU purposes only;
• We generate a single CFD simulation, for which 20 input packages were generated;
• We used a single A100 GPU (parallelization using 2xGPUs does not enable sharing a single
batch, so it does not make it possible to train with larger meshes);
• We executed as big as possible mesh up to reducing the batch size from 8 to 1.
The results are listed in the table below:
mesh
[cells]
batch execution
11239424 8 OK
16777216 8 OOM
16777216 4 OK
21952000 4/2 OOM
21952000 1 OK
Table 9. Maximum mesh size test. OOM = out of memory.
Conclusions:
• In theory, we are able to train the model for a mesh of size 21 952 000 using A100 80GB GPU.
• With a batch of size 4 we can train a model for a mesh of size 16 777 216.
• With a batch of size 8 we can train the model for a mesh of size 11 239 424.
24. CFD Suite (AI-accelerated CFD): AI Training Benchmark Jul-22 24
Key takeaways
• Lenovo ThinkEdge SE450 Edge Server (Product Guide, Press Release) powered by 2 NVIDIA
A100 80GB GPUs (Learn More) is byteLAKE’s recommended hardware configuration to
perform CFD Suite’s AI Training at the Edge.
• A100 GPU turned out to be ~1.8x faster than V100 GPU in the scenarios benchmarked by
byteLAKE and described in this report.
• CFD Suite’s AI Training’s performance improves if we add more NVIDIA GPUs per node. The
speedup between 1xA100 and 2xA100 was stable for all benchmarked meshes and varied
from 1.76 to 1.87.
• Efficiency of the AI Training was 0.95 which confirmed the good scalability of CFD Suite.
• SE450 node, powered by 2 x A100 gave from 10.6x to 19.3x speedup over CPU gold, while the
V100 was from 6x to 10.7x faster than CPU gold. The higher the mesh size, the higher speedup
was achieved using GPUs over the CPU platform. Again, the results are based on scenarios
described in this report.
• In theory, we are able to train the model for a mesh of size 21 952 000 using a single A100
80GB GPU. This is based on byteLAKE’s CFD Suite’s current architecture and as a research in
that space is ongoing, this will change in the future.
25. CFD Suite (AI-accelerated CFD): AI Training Benchmark Jul-22 25
About byteLAKE
byteLAKE is a software company that builds Artificial Intelligence products for the chemical industry,
paper industry and manufacturing. byteLAKE's CFD Suite leverages AI to reduce CFD (Computational
Fluid Dynamics) chemical mixing simulations’ time from hours to minutes. byteLAKE's Cognitive
Services offer AI-assisted Visual Inspection and Big Data analytics. For the paper industry, it can detect
and visually inspect the so-called Water Line. For manufacturing, it helps automate complex tasks
related to visual quality inspection and perform sound analytics helping efficiently detect and identify
faulty engines, bearings, etc. The company also offers custom AI software development for real-time
data analytics (image / video / sound / time-series). To learn more about byteLAKE’s innovations, go
to www.byteLAKE.com.
Lenovo and the Lenovo logo are trademarks or registered trademarks of Lenovo. NVIDIA and the NVIDIA logo are trademarks and/or
registered trademarks of NVIDIA Corporation in the U.S. and other countries.
byteLAKE
Artificial Intelligence for Chemical Industry, Paper Industry
and Manufacturing.
We build AI products and help design custom AI software.