This title appears in the Scientific Report :
2020
Please use the identifier:
http://hdl.handle.net/2128/26778 in citations.
Please use the identifier: http://dx.doi.org/10.1002/aisy.202070100 in citations.
In‐Memory Binary Vector–Matrix Multiplication Based on Complementary Resistive Switches
In‐Memory Binary Vector–Matrix Multiplication Based on Complementary Resistive Switches
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. T...
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Personal Name(s): | Ziegler, Tobias |
---|---|
Waser, Rainer / Wouters, Dirk J. / Menzel, Stephan (Corresponding author) | |
Contributing Institute: |
Elektronische Materialien; PGI-7 JARA-FIT; JARA-FIT JARA Institut Green IT; PGI-10 |
Published in: | Advanced intelligent systems, 2 (2020) 10, S. 2070100 - |
Imprint: |
Weinheim
Wiley-VCH Verlag GmbH & Co. KGaA
2020
|
DOI: |
10.1002/aisy.202070100 |
Document Type: |
Journal Article |
Research Program: |
Advanced Computing Architectures Controlling Collective States |
Link: |
OpenAccess |
Publikationsportal JuSER |
Please use the identifier: http://dx.doi.org/10.1002/aisy.202070100 in citations.
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%. |