Special Topic on Emerging Hardware for Cognitive Computing

被引:0
|
作者
Incorvia, Jean Anne C. [1 ]
机构
[1] Univ Texas Austin, Dept Elect & Comp Engn, Austin, TX 78712 USA
来源
IEEE JOURNAL ON EXPLORATORY SOLID-STATE COMPUTATIONAL DEVICES AND CIRCUITS | 2021年 / 7卷 / 02期
关键词
D O I
10.1109/JXCDC.2021.3135681
中图分类号
TP3 [计算技术、计算机技术];
学科分类号
0812 ;
摘要
Emerging materials and physics can be leveraged for new device-inherent behavior that can have system-level benefits. Motivation for device, circuit, and system behavior can be drawn from how the human brain processes certain data-intensive tasks adaptively and quickly, such as canonical image recognition. The field of neuromorphic computing has made great strides in implementing multi-weight synaptic behavior, as well as neuronal behavior such as integrate-and-fire and stochastic switching, and implementation of such behaviors in deep neural network (DNN) processing. Using CMOS, emerging resistive memories, and other device types as the basis, neuromorphic computing is innovating vertically from devices, to circuits, to systems, to redefine how computation can be done. This work is licensed under a Creative Commons Attribution 4.0 License. For more information, see https://creativecommons.org/licenses/by/4.0/
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页数:2
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