Bulk‐Switching Memristor‐Based Compute‐In‐Memory Module for Deep Neural Network Training

The constant drive to achieve higher performance in deep neural networks (DNNs) has led to the proliferation of very large models. Model training, however, requires intensive computation time and energy. Memristor‐based compute‐in‐memory (CIM) modules can perform vector‐matrix multiplication (VMM) i...

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Veröffentlicht in:	Advanced materials (Weinheim) Jg. 35; H. 46; S. e2305465 - n/a
Hauptverfasser:	Wu, Yuting, Wang, Qiwen, Wang, Ziyu, Wang, Xinxin, Ayyagari, Buvna, Krishnan, Siddarth, Chudzik, Michael, Lu, Wei D.
Format:	Journal Article
Sprache:	Englisch
Veröffentlicht:	Weinheim Wiley Subscription Services, Inc 01.11.2023
Schlagworte:	Artificial neural networks Computer memory deep neural network training deep neural networks Hardware Inference in‐memory computing Materials science Memristors mixed precision training Modules Neural networks Switching Training
ISSN:	0935-9648, 1521-4095, 1521-4095
Online-Zugang:	Volltext
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