Performance Impacts of Analog ReRAM Non-ideality on Neuromorphic Computing

Resistive random access memory (ReRAM) is often considered as a strong candidate for storing the weights in non-von Neumann neuromorphic computing systems. This paper studies how nonideal memory characteristics, which include programing error, read fluctuation, and retention, affect the inference ac...

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Veröffentlicht in:	IEEE transactions on electron devices Jg. 66; H. 3; S. 1289 - 1295
Hauptverfasser:	Lin, Yu-Hsuan, Wang, Chao-Hung, Lee, Ming-Hsiu, Lee, Dai-Ying, Lin, Yu-Yu, Lee, Feng-Min, Lung, Hsiang-Lan, Wang, Keh-Chung, Tseng, Tseung-Yuen, Lu, Chih-Yuan
Format:	Journal Article
Sprache:	Englisch
Veröffentlicht:	New York IEEE 01.03.2019 The Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Schlagworte:	Accuracy Analog memory Artificial neural networks Computation Computer simulation Error reduction Inference Mapping Memory management Multilayers Neural networks Neuromorphic computing Neuromorphics noise Random access memory reliability Resistance resistive random access memory (ReRAM) stability Synapses Training Variation Weight
ISSN:	0018-9383, 1557-9646
Online-Zugang:	Volltext
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