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

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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Bibliographic Details
Main Authors: Runze Han, Peng Huang, Yachen Xiang, Hong Hu, Sheng Lin, Peiyan Dong, Wensheng Shen, Yanzhi Wang, Xiaoyan Liu, Jinfeng Kang
Format: Article
Language:English
Published: Wiley 2022-12-01
Series:Advanced Intelligent Systems
Subjects:
deep neural networks
flash memory
floating gate transistors
in-memory computing
parallel computing
Online Access:https://doi.org/10.1002/aisy.202200127

Internet

https://doi.org/10.1002/aisy.202200127

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