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Conference Papers Year : 2021

AdequateDL: Approximating Deep Learning Accelerators

Abstract

The design and implementation of Convolutional Neural Networks (CNNs) for deep learning (DL) is currently receiving a lot of attention from both industrials and academics. However, the computational workload involved with CNNs is often out of reach for low power embedded devices and is still very costly when running on datacenters. By relaxing the need for fully precise operations, approximate computing substantially improves performance and energy efficiency. Deep learning is very relevant in this context, since playing with the accuracy to reach adequate computations will significantly enhance performance, while keeping quality of results in a user-constrained range. AdequateDL is a project aiming to explore how approximations can improve performance and energy efficiency of hardware accelerators in DL applications. This paper presents the main concepts and techniques related to approximation of CNNs and preliminary results obtained in the AdequateDL framework.
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Dates and versions

hal-03266861 , version 1 (23-06-2021)

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Olivier Sentieys, Silviu-Ioan Filip, David Briand, David Novo, Etienne Dupuis, et al.. AdequateDL: Approximating Deep Learning Accelerators. DDECS 2021 - 24th International Symposium on Design and Diagnostics of Electronic Circuits and Systems, Apr 2021, Vienna (virtual), Austria. pp.37-40, ⟨10.1109/DDECS52668.2021.9417026⟩. ⟨hal-03266861⟩
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