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Pedro Trancoso and Jens Hagemeyer
Chalmers University of Technology & Bielefeld University
VEDLIoT
Very Efficient Deep Learning in IoT
18 January 2021
Project Overview

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FUTURE…
▪ Deep Learning: Solve more challenging & complex
problems
▪ Everywhere: transportation & industry & home
▪ Systems: Performance + security & privacy & robustness
Motivation
VEDLIoT offers a framework for the next generation internet
based on IoT devices that collaboratively solve complex DL
applications across a distributed system

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VEDLIoT Overview
Start date: November 2020
Project coordinator

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▪ Improve performance/cost ratio – AI processing hardware distributed over the entire chain
Use case: Automotive

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▪ Edge devices to be used in distributed systems – DL for high-level understanding from sensor
Use case: Industrial IoT

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▪ Increase safety, health and well being of residents – acceleration of AI methods for demand-
oriented user-home interaction
Use case: Smart Home / Assisted Living

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▪ Enabling the rapid convergence of the fast pace innovation on the hardware and software
Deep Learning Toolchain

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▪ End of Moore’s law & dark silicon – Domain Specific Architectures (DSA)
▪ Efficient, flexible, scalable accelerators for the compute continuum
▪ Algotecture – DL algorithm + computer architecture co-design
DL Accelerators

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Cognitive IoT Platform
• Heterogeneous, modular, scalable microserver system
• Different technology concepts for improving: computing power density, cost-effectiveness,
maintainability, and reliability for the full spectrum of IoT, from embedded devices over the edge
towards the cloud
x86
GPU
ML-ASIC
ARM v8
GPU SoC
FPGA
SoC
RISC-V
FPGA
VEDLIOT Cognitive IoT
Platform

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Simulation platform for IoT
• Open source framework for software/hardware co-development with CI-driven testing capabilities, as
well as metrics for measuring efficiency of ML workloads
• Enables development and continuous testing of VEDLIoT’s security features and its robustness
• Renode is available to all project members and future users of VEDLIoT and will include a simulated
model of the RISC-V-based FPGA SoC platform developed as part of the VEDLIoT project

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Expected Impacts
• Scientific progress enabling novel, future semi-autonomous IoT applications
• Long-term evolution of next-generation IoT infrastructures and service platforms technologies –
hardware, platforms, tools, applications
• Human-centred IoT evolution (improving usability and user acceptance), through strengthened security
and user control
• Maintain an active ecosystem of all relevant IoT stakeholders
• Emerging or future standards and pre-normative activities
• Propose and mobilise key IoT players in security and privacy

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The fun has just started!
▪ Follow our work!
 https://twitter.com/VEDLIoT
 https://www.linkedin.com/company/vedliot/
 https://vedliot.eu
▪ Be part of it
 Open call at project mid-term
 Allow early use and evaluation of VEDLIoT
technology