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arxiv: 2207.00675 · v1 · pith:2FOQJIPK · submitted 2022-07-01 · cs.AR · cs.CR· cs.DC· cs.PF

VEDLIoT: Very Efficient Deep Learning in IoT

Martin Kaiser , Rene Griessl , Nils Kucza , Carola Haumann , Lennart Tigges , Kevin Mika , Jens Hagemeyer , Florian Porrmann
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Ulrich R\"uckert Micha vor dem Berge Stefan. Krupop Mario Porrmann Marco Tassemeier Pedro Trancoso Fareed Quararyah Stavroula Zouzoula Antonio Casimiro Alysson Bessani Jose Cecilio Stefan Andersson Oliver Brunnegard Olof Eriksson Roland Weiss Franz Meierh\"ofer Hans Salomonsson Elaheh Malekzadeh Daniel \"Odman Anum Khurshid Pascal Felber Marcelo Pasin Valerio Schiavoni J\"ames M\'en\'etrey Karol Gugula Piotr Zierhoffer Eric Knauss Hans-Martin Heyn
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classification cs.AR cs.CRcs.DCcs.PF
keywords vedliotapplicationsdeeplearningapproachdistributedhardwaremodular
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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.

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