VEDLIoT: Very Efficient Deep Learning in IoT
Martin Kaiser, René Griessl, Nils Kucza, C. Haumann, Lennart Tigges, Kevin Mika, Jens Hagemeyer, Florian Porrmann, Ulrich Rückert, Micha vor dem Berge, Stefan Krupop, Mario Porrmann, Marco Tassemeier, Pedro Trancoso, Fareed Mohammad Qararyah, Stavroula Zouzoula, António Casimiro, Alysson Bessani, José Cecílio, Sia Andersson · 2022
The VEDLIoT project targets the development of energy-efficient Deep Learning for distributed AIoT applications. A holistic approach is used to optimize algorithms while also dealing with safety and security challenges. The approach is based on a modular and scalable cognitive IoT hardware platform. Using modular microserver technology enables the user to configure the hardware to satisfy a wide range of applications. VEDLIoT offers a complete design flow for Next-Generation IoT devices required for collaboratively solving complex Deep Learning applications across distributed systems. The methods are tested on various use-cases ranging from Smart Home to Automotive and Industrial IoT appliances. VEDLIoT is an H2020 EU project which started in November 2020. It is currently in an intermediate stage with the first results available.