AccML, co-located with HiPEAC 2021_Pedro Trancoso presentation
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VEDLIoT is a project that aims to develop a framework for the next generation internet of things (IoT) based on IoT devices that collaboratively solve complex deep learning applications across distributed systems. The project will improve the performance and cost ratio of AI processing by distributing hardware across the entire chain from embedded devices to the cloud. It will also increase the safety, health and well-being of users through accelerating AI methods for user-home interaction. The project will develop a cognitive IoT platform, deep learning toolchain, and DL accelerators to enable this vision over its three year timeline starting in November 2020.
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AccML, co-located with HiPEAC 2021_Pedro Trancoso presentation
- 1. Pedro Trancoso and Jens Hagemeyer Chalmers University of Technology & Bielefeld University VEDLIoT Very Efficient Deep Learning in IoT 18 January 2021 Project Overview
- 2. 2 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
- 3. 3 VEDLIoT Overview Start date: November 2020 Project coordinator
- 4. 4 ▪ Improve performance/cost ratio – AI processing hardware distributed over the entire chain Use case: Automotive
- 5. 5 ▪ Edge devices to be used in distributed systems – DL for high-level understanding from sensor Use case: Industrial IoT
- 6. 6 ▪ Increase safety, health and well being of residents – acceleration of AI methods for demand- oriented user-home interaction Use case: Smart Home / Assisted Living
- 7. 7 ▪ Enabling the rapid convergence of the fast pace innovation on the hardware and software Deep Learning Toolchain
- 8. 8 ▪ 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
- 9. 9 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
- 10. 10 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
- 11. 11 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
- 12. 12 12 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