Access Restriction

Author Chabi, Djaafar ♦ Zhao, Weisheng ♦ Querlioz, Damien ♦ Klein, Jacques-Olivier
Source ACM Digital Library
Content type Text
Publisher Association for Computing Machinery (ACM)
File Format PDF
Copyright Year ©2015
Language English
Subject Domain (in DDC) Computer science, information & general works ♦ Data processing & computer science
Subject Keyword Memristive nanodevices ♦ Nanoscale crossbar ♦ Neural network ♦ On-chip learning ♦ Supervised learning
Abstract Scaling down beyond CMOS transistors requires the combination of new computing paradigms and novel devices. In this context, neuromorphic architecture is developed to achieve robust and ultra-low power computing systems. Memristive nanodevices are often associated with this architecture to implement efficiently synapses for ultra-high density. In this article, we investigate the design of a neuro-inspired logic block (NLB) dedicated to on-chip function learning and propose learning strategy. It is composed of an array of memristive nanodevices as synapses associated to neuronal circuits. Supervised learning methods are proposed for different type of memristive nanodevices and simulations are performed to demonstrate the ability to learn logic functions with memristive nanodevices. Benefiting from a compact implementation of neuron circuits and the optimization of learning process, this architecture requires small number of nanodevices and moderate power consumption.
ISSN 15504832
Age Range 18 to 22 years ♦ above 22 year
Educational Use Research
Education Level UG and PG
Learning Resource Type Article
Publisher Date 2015-04-01
Publisher Place New York
e-ISSN 15504840
Journal ACM Journal on Emerging Technologies in Computing Systems (JETC)
Volume Number 11
Issue Number 4
Page Count 20
Starting Page 1
Ending Page 20

Open content in new tab

   Open content in new tab
Source: ACM Digital Library