Analog Computing for Deep Learning: Algorithms, Materials & Architectures

被引：0
|
作者：
Haensch, W. [1 ]
机构：
[1] IBM Res, TJ Watson Res Ctr, Yorktown Hts, NY 10598 USA
来源：
2018 IEEE INTERNATIONAL ELECTRON DEVICES MEETING (IEDM) | 2018年
关键词：
D O I：
暂无
中图分类号：
TM [电工技术]; TN [电子技术、通信技术];
学科分类号：
0808 ; 0809 ;
摘要：
Analog, or neuromorphic, computing for Deep Learning (DL) utilizes the fact that matrix manipulations that are inherent in the back-propagation algorithm, can be performed at constant time, in parallel, on arrays with nonvolatile memory (NVM) elements in which the weights are encoded. We discuss the NVM material requirements that need to be met to achieve a classification accuracy on par with the conventional digital approaches, discuss advantages and drawbacks, and highlight opportunities that can take advantage using analog arrays.
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页数：4
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