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Applying Deep Learning Vision Technology to low-cost/power Embedded Systems
- 1. © 2016 Synopsys, Inc. 1 Applying Deep Learning Vision Technology to Low-cost, Low-power Embedded Systems: An Industrial Perspective Pierre Paulin Director of R&D 16 January 2016
- 2. © 2016 Synopsys, Inc. 2 Agenda • Embedded Vision application trends and challenges • Synopsys Embedded Vision Processor Overview • Convolution Neural Networks – Applications, requirements – Dedicated CNN engine for EV – Competitive analysis • Summary & Final Thoughts
- 3. © 2016 Synopsys, Inc. 3 Embedded Vision is Coming Fast • Embedded Vision is the use of computer vision in embedded systems to interpret meaning from images or video • In cars to improve safety • Surveillance for detection and tracking • In industrial automation to improve quality and control • Estimated $300B+ market in 2020, 35% CAGR 0 50 100 150 200 250 300 350 2013 2014 2015 2016 2017 2018 2019 2020 BillionsofDollars Vision Systems Shipments Sources: ABI Research, Insight Media, Transparency Market Research, Markets And Markets, Synopsys
- 4. Wide Variety of Vision Applications Cameras Drones Home AutomationRetailGaming Infotainment Augmented RealityMobile SurveillanceADAS
- 5. © 2016 Synopsys, Inc. 5 Autonomous Driving Buzz 1/14/2016 – U.S. Proposes Spending $4 Billion to Encourage Driverless Cars Obama administration aims to remove hurdles to making autonomous cars more widespread Wall Street Journal 8/17/2016 – Ford's self-driving car 'coming in 2021’ (BBC News) 8/24/2016 – Self-driving taxis roll out in Singapore - beating Uber to it (The Guardian) 10/20/2016 – Elon Musk: You'll be able to summon your driverless Tesla from cross-country (CNN Money) 10/25/2016 – Uber's Self-Driving Truck Makes Its First Delivery: 50000 Beers (Wired)
- 6. © 2016 Synopsys, Inc. 6 Largest Embedded Vision Application Segment Advanced Driver Assistance Systems Driven By Safety Concerns Source: IC Market Drivers, IC Insights, January 2015 & Trends and Opportunities in Driver Assistance and Automated Driving, IHS Automotive Sep 2015
- 7. © 2016 Synopsys, Inc. 7 Video Surveillance Markets Growing Rapidly • Global IP Video Surveillance Market expected to grow at CAGR of 37.3% from 2012-20 • Demand driven by – Growing installations of IP cameras – Need for surveillance cameras with better video quality – Limited ability for real-time human analysis http://www.alliedmarketresearch.com/IP-video-surveillance-VSaaS-market 3X Growth Forecast 2013 - 2019 Security (Airports, Govt, Banks, Casinos), Home Surveillance, Retail, Healthcare
- 8. © 2016 Synopsys, Inc. 8 Less Efficient EV Options Dedicated Embedded Vision Processors EV Challenges Require Embedded Vision Processors Performance Power Area CPUs don’t have math horsepower for fast 2D vision processing GPUs have high performance but large areas and higher power DSPs are designed for low power audio and speech applications, not 2D video FPGAs are good for prototyping but are expensive and performance limited Higher performance Lower power Smaller area Can include a dedicated deep learning (CNN) engine
- 9. © 2016 Synopsys, Inc. 9 Embedded Vision Applications and Power, Performance and Area (PPA) Requirements
- 10. © 2016 Synopsys, Inc. 10 Vision Pipeline Example Object detection pipeline Grayscale Conversion Image Pyramid Detecting Areas of Interest in a Frame Non-max Suppression Draw Box
- 11. © 2016 Synopsys, Inc. 11 Vision Pipeline Example Video surveillance pipeline Grayscale & Image Pyramid Face Detection Tracking & Detection Cascade Fusion & Learning
- 12. © 2016 Synopsys, Inc. 12 Vision Algorithm Computation • Object detection • Background subtraction • Feature extraction • Image segmentation • Connected comp. labeling • Noise reduction • Color space conversion • Gamma correction • Image scaling • Gaussian pyramid Simple Data-Level Parallelism (DLP) • Good spatial locality • Good compute intensity • Small context More Complex DLP • Complex data structures • Irregular compute intensity • Larger context Scalar Processing • General purpose compute • Thread level parallelism Pre-processing Selecting Areas of Interest Precise Processing of Selected Areas Decision Making • Object recognition • Tracking • Feature matching • Gesture recognition • Motion analysis • Match/no match • Flag events CNN RISC scalar Multi-core Gen2 EV SIMD processorMulti-core Gen1 EV SIMD processor Multi-core CNN Engine
- 13. © 2016 Synopsys, Inc. 13 Sample Power, Performance and Area Targets • Intelligent video surveillance applications – Face detection & tracking, pose detection, gaze estimation, gender recognition, age estimation – People detection & counting for video surveillance – Driver fatigue detection – Advanced detection and tracking – Implementation on GPP and GP-GPU – Typical customer targets for HD @30 fps Based on 28 nm process node <500 mW 1-2 mm2 10-500 GOP/s 1-10 W 50-100 mm2
- 14. © 2016 Synopsys, Inc. 14 Sample Power, Performance and Area Targets • ADAS – Pedestrian, vehicle, traffic sign, lane detections – Scene segmentation – Implementation on GPP and GP-GPU – Typical customer targets for HD @30 fps Based on 28 nm process node 100-2000 GOP/s 1-2 W 2-5 mm2 >100 W >100 mm2
- 15. © 2016 Synopsys, Inc. 15 DesignWare® ARC EV6 Processor and CNN - Vision-specific wide SIMD engine - Optimized CNN engine - Programming tools
- 16. © 2016 Synopsys, Inc. 16 EV6x Processor Objectives Low power: Over 1000 GMAC/s/W in CNN engine High productivity Highly Scalable Vector Engine 100 GOP/s 620 GOP/s Low area High-performance CNN: Up to 880 MAC/cycle Scalar Vector CNN Standard Programming model Accelerator OpenCL C Most Integrated Solution C/C++ Embedded Vision Libraries Preliminary – Subject to Change
- 17. © 2016 Synopsys, Inc. 17 EV Processor Solution: EV6x with CNN Engine Embedded Vision Programming Tools Vision CPU (1 to 4 cores) CNN Engine Option Convolution ALU Conv. 2D AGUs CC MEMs Cluster Comm. Shared Mem.DMA Classification AXI Interconnect User kernels Ui Uk C/C++ OpenCL C K1 Kn… Kernel Lib OpenCL C compiler, with whole function vectorization C/C++ compiler Lib Ui Uj Uk Kn Uk Um graph CNN Graph Mapping Tools HAPS® Rapid Prototyping Board Virtual Prototype ALU Conv. 1D AGUs CC MEMs Coherency ARConnect Sync Debug Power Mgmt. Up to 880 MAC/cycle Up to 620 GOP/s at 800 MHz Core 4 Core 3 32b Scalar 512b Vector DSP Core 2 Core 1 32b Scalar 512b Vector DSP VCCMD$I$ VCCMD$I$ CNN graph Cn CNN graph node
- 18. © 2016 Synopsys, Inc. 18 CNN – Convolution Neural Networks Deep Learning Approach to Embedded Vision
- 19. © 2016 Synopsys, Inc. 19 CNN for a Wide Range of Vision Applications • Image classification, search similar images • Object detection, classification & localization – Any type of object(s), depending on training phase • Face recognition • Visual attention • Facial expression recognition • Gesture recognition / hand tracking • Resolution upscaling • Scene recognition and labelling, semantic segmentation – Sky, mountain, road, tree, building, … • Recent advocates – Nvidia, Microsoft, Google, Baidu, Adobe, Qualcomm, Yahoo … – Mobileye for autonomous driving car car sky building building road
- 20. © 2016 Synopsys, Inc. 20 Pedestrian Detection: HoG vs. CNN
- 21. © 2016 Synopsys, Inc. 21 Computation Requirements for CNN Accuracy Computationalcomplexity Lenet (1994) 4 layers AlexNet (2012) 8 layers 100MByte VGG-19 (2014) 19 layers 270MByte GoogleNet (2014) 22 layer 20MByte ResNet (2015) 152 layers! 10MByte 1 GOPs/frame 10 GOPs/frame
- 22. © 2016 Synopsys, Inc. 22 Scene Segmentation Source: Press Release by Toshiba and Denso, 17 Oct. 2016
- 23. © 2016 Synopsys, Inc. 23 Super resolution using CNN Source Bicubic Interpolation CNN Reference Source Bicubic Interpolation CNN Reference “Image Super-Resolution Using Deep Convolutional Networks (2016), C. Dong et al.”
- 24. © 2016 Synopsys, Inc. 24 Super-Resolution using Convolutional Neural Networks • CNN’s deliver superior Super-Resolution for single image and video • CNN’s for Super-Resolution require dedicated compute engine with high compute capacity • Example “Image Super-Resolution Using Deep Convolutional Networks (2016), C. Dong et al.” Requires 600 GMAC for one 4K frame
- 25. © 2016 Synopsys, Inc. 25 CNN Graph Training and Porting Image labeling Graph explore, training GPU farm Code vectorization TrainingPorting coeff. Code Object detection executable CNN graph GPP CNN-optimized processor GP-GPU
- 26. © 2016 Synopsys, Inc. 26 CNN Computation • Convolution of multiple inputs together – Fixed kernel size • Optional subsampling – 1x, 2x, 4x • Optional max-pooling • Very regular, repetitive computation – Dominated by MAC – Deterministic • Non-linear activation function – Rectifier, Sigmoid, Hyperbolic tangent I0 IM-1 I1 O0 ON-1 M inputs (XI * YI) Z kernels (K * K) with associated weights N outputs (XO * YO) Oj = act(Bj+ (Iv x Kw) + …) Convolution (x) act act Activation (tanh, ReLU) …
- 27. © 2016 Synopsys, Inc. 27 EV6x Second Generation CNN Engine for Object Detection and Semantic Segmentation - High performance, low power and area - Fully programmable car car sky building building
- 28. © 2016 Synopsys, Inc. 28 High-Performance EV6x CNN Engine • Dedicated EV6x CNN Engine with performance equal or better than GP-GPU • Programmable to support full range of fixed point CNN graphs • State-of-the-art power-efficiency • Real-time, high quality image classification, object recognition, semantic segmentation • Supports resolutions up to 4K • Operates in parallel with Vision CPUs increasing efficiency and throughput AXIInterconnect Vision CPU Core 32 bit RISC 512-bit Vector DSP Cluster Shared Memory DMA ARConnect CNN Engine Convolution Classification Preliminary – Subject to Change ALU Conv. 2D AGUs CC MEMs ALU Conv. 1D AGUs CC MEMs
- 29. © 2016 Synopsys, Inc. 29 AlexNet on ImageNet Quantization opportunities for recognition tasks 32-bit floating point 16-bit fixed point vs [Moons WACV2016] Recognitionaccuracy Fixed-point word length • 12 bit good compromise between CNN recognition performance and hardware cost – 8 bit will cause recognition rate loss on existing graphs – 12 bit multiplier is almost half the area of a 16 bit multiplier 12-bit
- 30. © 2016 Synopsys, Inc. 30 CNN data precision – Qualcomm data
- 31. © 2016 Synopsys, Inc. 31 CNN Competitive Analysis
- 32. © 2016 Synopsys, Inc. 32 CNN Performance and Area Efficiency Comparison Preliminary – Subject to Change GMAC/s/mm2 10 1000 1 10 100 1000 300X 2X 100 GMAC/s 20X 14X First gen vision processors GP/GPU EV6x Embedded Vision Processor w/integrated CNN Circle area proportional to logic area
- 33. © 2016 Synopsys, Inc. 33 CNN Performance and Power Efficiency Comparison Preliminary – Subject to Change GMAC/s/W 10 100 1000 10 100 1000 10000 11X 30X GMAC/s EV6x Embedded Vision Processor w/integrated CNN First gen vision processors GP/GPU Circle area proportional to logic area
- 34. © 2016 Synopsys, Inc. 34 Less Efficient EV Options Dedicated Embedded Vision Processors EV Challenges Require Embedded Vision Processors Performance Power Area CPUs don’t have math horsepower for fast 2D vision processing GPUs have high performance but large areas and higher power DSPs are designed for low power audio and speech applications, not 2D video FPGAs are good for prototyping but are expensive and performance limited High performance Lower power Smaller area Dedicated deep learning (CNN) engine provides PPA numbers compatible with surveillance, ADAS and mobile targets 1000 GMACs/W 100-1000 GOP/s Few mm2
- 35. Thank You Contact me at: pierre.paulin@synopsys.com