Optimization of Projected Phase Change Memory for Analog In‐Memory Computing Inference

Optimization of Projected Phase Change Memory for Analog In‐Memory Computing Inference

Abstract Phase change memory (PCM) is one of the most promising candidates for non‐von Neumann based analog in‐memory computing–particularly for inference of previously‐trained deep neural networks (DNN). It is shown that PCM electrical properties can be tuned systematically using a projection liner...

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Bibliographic Details
Main Authors: Ning Li, Charles Mackin, An Chen, Kevin Brew, Timothy Philip, Andrew Simon, Iqbal Saraf, Jin‐Ping Han, Syed Ghazi Sarwat, Geoffrey W. Burr, Malte Rasch, Abu Sebastian, Vijay Narayanan, Nicole Saulnier
Format: Article
Language:English
Published: Wiley-VCH 2023-06-01
Series:Advanced Electronic Materials
Subjects:
deep neural network
in‐memory computing
non‐volatile memory
phase change memory
resistance drift
Online Access:https://doi.org/10.1002/aelm.202201190

Internet

https://doi.org/10.1002/aelm.202201190

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