Floating Gate Transistor‐Based Accurate Digital In‐Memory Computing for Deep Neural Networks

To improve the computing speed and energy efficiency of deep neural network (DNN) applications, in‐memory computing with nonvolatile memory (NVM) is proposed to address the time‐consuming and energy‐hungry data shuttling issue. Herein, a digital in‐memory computing method for convolution computing,...

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Veröffentlicht in:	Advanced intelligent systems Jg. 4; H. 12
Hauptverfasser:	Han, Runze, Huang, Peng, Xiang, Yachen, Hu, Hong, Lin, Sheng, Dong, Peiyan, Shen, Wensheng, Wang, Yanzhi, Liu, Xiaoyan, Kang, Jinfeng
Format:	Journal Article
Sprache:	Englisch
Veröffentlicht:	Weinheim John Wiley & Sons, Inc 01.12.2022 Wiley
Schlagworte:	Accuracy Artificial neural networks Central processing units Chips (memory devices) Computer architecture Computer memory Convolution CPUs deep neural networks Energy efficiency flash memory floating gate transistors in-memory computing Internet of Things Neural networks parallel computing Parameters Semiconductor devices Software Transistors
ISSN:	2640-4567, 2640-4567
Online-Zugang:	Volltext
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