Rescuing RRAM-Based Computing From Static and Dynamic Faults

Emerging resistive random access memory (RRAM) has shown the great potential of in-memory processing capability, and thus attracts considerable research interests in accelerating memory-intensive applications, such as neural networks (NNs). However, the accuracy of RRAM-based NN computing can degrad...

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Veröffentlicht in:	IEEE transactions on computer-aided design of integrated circuits and systems 2021-10, Vol.40 (10), p.2049-2062
Hauptverfasser:	Lin, Jilan, Wen, Cheng-Da, Hu, Xing, Tang, Tianqi, Lin, Ing-Chao, Wang, Yu, Xie, Yuan
Format:	Artikel
Sprache:	eng
Schlagworte:	Algorithms Computation Computer architecture Dynamic random access memory Electrodes Fault tolerance Fault tolerant systems Kernel Neural network (NN) Neural networks Quantization (signal) Random access memory Reliability Resistance resistive random access memory (RRAM) Static random access memory Visual perception
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