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Paper detail

VEDLIoT: Very Efficient Deep Learning in IoT

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.

preprint2022arXivOpen access
Martin KaiserRene GriesslNils KuczaCarola HaumannLennart TiggesKevin MikaJens HagemeyerFlorian PorrmannUlrich RückertMicha vor dem BergeStefan. KrupopMario PorrmannMarco TassemeierPedro TrancosoFareed QuararyahStavroula ZouzoulaAntonio CasimiroAlysson BessaniJose CecilioStefan AnderssonOliver BrunnegardOlof ErikssonRoland WeissFranz MeierhöferHans SalomonssonElaheh MalekzadehDaniel ÖdmanAnum KhurshidPascal FelberMarcelo PasinValerio SchiavoniJämes MénétreyKarol GugulaPiotr ZierhofferEric KnaussHans-Martin Heyn
PerformanceHardware ArchitectureCryptography and SecurityDistributed, Parallel, and Cluster Computing
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Martin KaiserRene GriesslNils KuczaCarola HaumannLennart TiggesKevin MikaJens HagemeyerFlorian PorrmannUlrich RückertMicha vor dem BergeStefan. KrupopMario PorrmannMarco TassemeierPedro TrancosoFareed QuararyahStavroula ZouzoulaAntonio CasimiroAlysson BessaniJose CecilioStefan AnderssonOliver BrunnegardOlof ErikssonRoland WeissFranz MeierhöferHans SalomonssonElaheh MalekzadehDaniel ÖdmanAnum KhurshidPascal FelberMarcelo PasinValerio SchiavoniJämes MénétreyKarol GugulaPiotr ZierhofferEric KnaussHans-Martin Heyn

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