Performance Impacts of Analog ReRAM Non-ideality on Neuromorphic Computing

• Published in: IEEE transactions on electron devices Vol. 66; no. 3; pp. 1289 - 1295
• Main Authors: 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
• Language: English
• Published: New York IEEE 01.03.2019; The Institute of Electrical and Electronics Engineers, Inc. (IEEE)
• Subjects: 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

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...