认知计算新兴硬件专题

IF 2 Q3 COMPUTER SCIENCE, HARDWARE & ARCHITECTURE
Jean Anne C. Incorvia
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引用次数: 0

摘要

新兴材料和物理可以用于新的设备固有行为,可以具有系统级的好处。设备、电路和系统行为的动机可以从人类大脑如何自适应地快速处理某些数据密集型任务(例如规范图像识别)中得出。神经形态计算领域在实现多权突触行为以及神经元行为(如整合-点火和随机切换)以及在深度神经网络(DNN)处理中实现这些行为方面取得了长足的进步。以CMOS、新兴的电阻式存储器和其他器件类型为基础,神经形态计算正在从器件、电路到系统的垂直方向进行创新,重新定义计算的方式。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
Special Topic on Emerging Hardware for Cognitive Computing
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.
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来源期刊
CiteScore
5.00
自引率
4.20%
发文量
11
审稿时长
13 weeks
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