Neural Architecture Search with In‐Memory Multiply–Accumulate and In‐Memory Rank Based on Coating Layer Optimized C‐Doped Ge2Sb2Te5 Phase Change Memory

Neural architecture search (NAS), as a subfield of automated machine learning, can design neural network models with better performance than manual design. However, the energy and time consumptions of conventional software‐based NAS are huge, hindering its development and applications. Herein, 4 Mb...

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Veröffentlicht in:	Advanced functional materials Jg. 34; H. 15
Hauptverfasser:	Yan, Longhao, Wu, Qingyu, Li, Xi, Xie, Chenchen, Zhou, Xilin, Li, Yuqi, Shi, Daijing, Yu, Lianfeng, Zhang, Teng, Tao, Yaoyu, Yan, Bonan, Zhong, Min, Song, Zhitang, Yang, Yuchao, Huang, Ru
Format:	Journal Article
Sprache:	Englisch
Veröffentlicht:	Hoboken Wiley Subscription Services, Inc 10.04.2024
Schlagworte:	Chips (memory devices) Cognitive tasks Computation Computer architecture Computer memory Graphics processing units in‐memory computing in‐memory rank Machine learning neural architecture search Neural networks operators phase change memory
ISSN:	1616-301X, 1616-3028
Online-Zugang:	Volltext
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