Approximations in Deep Learning - Archive ouverte HAL Accéder directement au contenu
Chapitre D'ouvrage Année : 2022

Approximations in Deep Learning

Résumé

The design and implementation of Deep Learning (DL) models is currently receiving a lot of attention from both industrials and academics. However, the computational workload associated with DL is often out of reach for low-power embedded devices and is still costly when run on datacenters. By relaxing the need for fully precise operations, Approximate Computing (AxC) substantially improves performance and energy efficiency. DL is extremely relevant in this context, since playing with the accuracy needed to do adequate computations will significantly enhance performance, while keeping the quality of results in a user-constrained range. This chapter will explore how AxC can improve the performance and energy efficiency of hardware accelerators in DL applications during inference and training.
Fichier principal
Vignette du fichier
chapter15/chapter_15.pdf (1.75 Mo) Télécharger le fichier
Origine : Fichiers produits par l'(les) auteur(s)

Dates et versions

hal-03494874 , version 1 (07-12-2022)

Identifiants

Citer

Etienne Dupuis, Silviu-Ioan Filip, Olivier Sentieys, David Novo, Ian O'Connor, et al.. Approximations in Deep Learning. Approximate Computing Techniques - From Component- to Application-Level, pp.467-512, 2022, 978-3-030-94704-0. ⟨10.1007/978-3-030-94705-7_15⟩. ⟨hal-03494874⟩
163 Consultations
61 Téléchargements

Altmetric

Partager

Gmail Facebook X LinkedIn More