Efficient and robust bitstream processing in binarised neural networks

Autor:	Sercan Aygun, Ece Olcay Gunes, Christophe De Vleeschouwer
Jazyk:	angličtina
Rok vydání:	2021
Předmět:	Logic circuits Neural nets (circuit implementations) Logic elements Neural net devices Electrical engineering. Electronics. Nuclear engineering TK1-9971
Zdroj:	Electronics Letters, Vol 57, Iss 5, Pp 219-222 (2021)
Druh dokumentu:	article
ISSN:	1350-911X 0013-5194
DOI:	10.1049/ell2.12045
Popis:	Abstract In the neural network context, used in a variety of applications, binarised networks, which describe both weights and activations as single‐bit binary values, provide computationally attractive solutions. A lightweight binarised neural network system can be constructed using only logic gates and counters together with a two‐valued activation function unit. However, binarised neural networks represent the weights and the neuron outputs with only one bit, making them sensitive to bit‐flipping errors. Binarised weights and neurons are manipulated by the utilisation of bitstream processing with regard to stochastic computing to cope with this error sensitivity. Stochastic computing is shown to provide robustness for bit errors on data while being built on a hardware structure, whose implementation is simplified by a novel subtraction‐free implementation of the neuron activation.
Databáze:	Directory of Open Access Journals
Externí odkaz:	https://doaj.org/article/4352ef754b1048069bfa8c6870037255 Zobrazit plný text záznamu Plný text View record in DOAJ