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Title:

A Low-Power RRAM Memory Block for Embedded, Multi-Level Weight and Bias Storage in Artificial Neural Networks

Document type:
Zeitschriftenaufsatz
Author(s):
Pechmann, Stefan; Mai, Timo; Potschka, Julian; Reiser, Daniel; Reichel, Peter; Breiling, Marco; Reichenbach, Marc; Hagelauer, Amelie
Abstract:
Pattern recognition as a computing task is very well suited for machine learning algorithms utilizing artificial neural networks (ANNs). Computing systems using ANNs usually require some sort of data storage to store the weights and bias values for the processing elements of the individual neurons. This paper introduces a memory block using resistive memory cells (RRAM) to realize this weight and bias storage in an embedded and distributed way while also offering programming and multi-level abil...     »
Journal title:
Micromachines
Year:
2021
Journal volume:
12
Journal issue:
11
Fulltext / DOI:
doi:10.3390/mi12111277
WWW:
https://www.mdpi.com/2072-666X/12/11/1277
Print-ISSN:
2072-666X
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