Accéder directement au contenu Accéder directement à la navigation
Chapitre d'ouvrage

PNeuro: A scalable energy-efficient programmable hardware accelerator for neural networks

Abstract : Artificial intelligence and especially Machine Learning recently gained a lot of interest from the industry. Indeed, new generation of neural networks built with a large number of successive computing layers enables a large amount of new applications and services implemented from smart sensors to data centers. These Deep Neural Networks (DNN) can interpret signals to recognize objects or situations to drive decision processes. However, their integration into embedded systems remains challenging due to their high computing needs. This paper presents PNeuro, a scalable energy-efficient hardware accelerator for the inference phase of DNN processing chains. Simple programmable processing elements architectured in SIMD clusters perform all the operations needed by DNN (convolutions, pooling, non-linear functions, etc.). An FDSOI 28 nm prototype shows an energy efficiency of 700 GMACS/s/W at 800 MHz. These results open important perspectives regarding the development of smart energy-efficient solutions based on Deep Neural Networks.
Type de document :
Chapitre d'ouvrage
Liste complète des métadonnées

https://hal-univ-bourgogne.archives-ouvertes.fr/hal-01949772
Contributeur : Lead - Université de Bourgogne <>
Soumis le : lundi 10 décembre 2018 - 13:42:04
Dernière modification le : lundi 10 février 2020 - 18:12:35

Identifiants

  • HAL Id : hal-01949772, version 1

Citation

A. Carbon, J.-M. Philippe, O. Bichler, R. Schmit, B. Tain, et al.. PNeuro: A scalable energy-efficient programmable hardware accelerator for neural networks. 2018 Design, Automation & Test in Europe Conference & Exhibition (DATE 2018) :, Institute of Electrical and Electronics Engineers ( IEEE ), 2018, 9781538669426. ⟨hal-01949772⟩

Partager

Métriques

Consultations de la notice

135