Journal Article

FZJ-2021-00229

http://join2-wiki.gsi.de/foswiki/pub/Main/Artwork/join2_logo100x88.png In‐Memory Binary Vector–Matrix Multiplication Based on Complementary Resistive Switches

Ziegler, T.FZJ* ; Waser, R. ; Wouters, D. J. ; Menzel, S. (Corresponding author)FZJ*

2020
Wiley-VCH Verlag GmbH & Co. KGaA Weinheim

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Please use a persistent id in citations: http://hdl.handle.net/2128/26778  doi:10.1002/aisy.202070100

Abstract: In article number 2000134, Stephan Menzel and co‐workers explore a computation in‐memory concept for binary vector‐matrix multiplications based on complementary resistive switches. Experimental results on a small‐scale demonstrator are shown and the influence of device variability is investigated. The simulated inference of a 1‐layer fully connected binary neural network trained on the MNIST data set resulted in an accuracy of nearly 86%.

Note: Cover Picture

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Contributing Institute(s):

1. Elektronische Materialien (PGI-7)
2. JARA Institut Green IT (PGI-10)
3. JARA-FIT (JARA-FIT)

Research Program(s):

1. 524 - Controlling Collective States (POF3-524) (POF3-524)
2. Advanced Computing Architectures (aca_20190115) (aca_20190115)

Appears in the scientific report 2020

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