AdequateDL explores how approximate computing can improve the performance of deep-learning hardware accelerators. Deep learning is very relevant in this context, since playing with the accuracy to reach adequate computations will significantly enhance energy efficiency, while keeping quality of results in a user-constrained range. Outcomes include a framework for accuracy exploration and evaluation of gains in performance per watt of the proposed adequate accelerators over CPU/GPU platforms.

AdequateDL Project on the ANR website: https://anr.fr/Project-ANR-18-CE23-0012

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