An efficient synaptic architecture for artificial neural networks

Document Type

Conference Proceeding

Publication Date

12-8-2017

Abstract

Artificial neural networks (ANN) have revolutionized the field of machine learning by providing impressive human-like performance in solving real-world tasks in computer vision, speech recognition, or complex strategic games. There is a significant interest in developing non-von Neumann coprocessors for the training of ANNs, where resistive memory devices serve as synaptic elements. However, interdevice variability, limited dynamic range and resolution, nonlinearity and asymmetric switching characteristics pose important technical challenges. We investigate the use of multi-memristive synapses to overcome these challenges. We present a detailed experimental characterization of conductance changes using a phase-change memory chip fabricated in the 90nm technology node and show how multi-memrisive synapses can address the limitations of memristive devices for synaptic implementations. Simulations show that an ANN trained with backpropagation can achieve competitive classification accuracies using such a scheme.

Identifier

85046730737 (Scopus)

ISBN

[9781538604779]

Publication Title

2017 17th Non Volatile Memory Technology Symposium Nvmts 2017 Conference Proceedings

External Full Text Location

https://doi.org/10.1109/NVMTS.2017.8171302

First Page

1

Last Page

4

Volume

2017-December

Fund Ref

Schweizerischer Nationalfonds zur Förderung der Wissenschaftlichen Forschung

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