Multi-Partner Project: A Deep Learning Platform Targeting Embedded Hardware for Edge-AI Applications (NEUROKIT2E)
R. bishnoi (TU Delft - Computer Engineering)
Mohammad Amin Yaldagard (TU Delft - Computer Engineering)
Said Hamdioui (TU Delft - Computer Engineering)
Kanishkan Vadivel (Imec)
Manolis Sifalakis (Imec)
Nicolas Daniel Rodriguez (Silicon Austria Labs GmbH)
Pedro Julian (Universidad Nacional del Sur)
Lothar Ratschbacher (Silicon Austria Labs GmbH)
Maen Mallah (Fraunhofer-IISB, Erlangen)
G.B. More Authors (External organisation)
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Abstract
The goal of the NEUROKIT2E project is to create an open-source Deep Learning framework for edge and embedded AI built around an established European value chain. This framework, called AIDGE, supports a wide range of application areas that operate independently and serve a global user community. It provides easy and fast full-stack solutions from Neural Network design and optimization to AI application development all the way down to hardware implementations while enabling code generation for application-specific targets. This platform provides flexibility for academic users in the AI domain to explore and innovate while allowing them the possibility to prototype systems, ensuring their work aligns well with industrial needs. This paper presents the results and achievements of the first part of this three-year project, along with its roadmap and expected outcomes.
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