Device Variation Effects on Neural Network Inference Accuracy in Analog In‐Memory Computing Systems

Device Variation Effects on Neural Network Inference Accuracy in Analog In‐Memory Computing Systems

In analog in‐memory computing systems based on nonvolatile memories such as resistive random‐access memory (RRAM), neural network models are often trained offline and then the weights are programmed onto memory devices as conductance values. The programmed weight values inevitably deviate from the t...

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Bibliographic Details
Main Authors: Qiwen Wang, Yongmo Park, Wei D. Lu
Format: Article
Language:English
Published: Wiley 2022-08-01
Series:Advanced Intelligent Systems
Subjects:
analog computing
deep neural networks
emerging memory
in-memory computing
process-in-memory
RRAM
Online Access:https://doi.org/10.1002/aisy.202100199

Internet

https://doi.org/10.1002/aisy.202100199

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