Design of a Safe Convolutional Neural Network Accelerator

被引：3
|
作者：
Xu, Zheng [1 ]
Abraham, Jacob [1 ]
机构：
[1] Univ Texas Austin, Dept Elect & Comp Engn, Austin, TX 78712 USA
来源：
2019 IEEE COMPUTER SOCIETY ANNUAL SYMPOSIUM ON VLSI (ISVLSI 2019) | 2019年
关键词：
Convolutional Neural Network; Safety; Concurrent Error Detection; Availability; FAULT-TOLERANCE;
D O I：
10.1109/ISVLSI.2019.00053
中图分类号：
TP3 [计算技术、计算机技术];
学科分类号：
0812 ;
摘要：
Recently Machine Learning (ML) accelerators have grown into prominence with significant power and performance efficiency improvements over CPU and GPU. In this paper, we developed an Algorithm Based Error Checker (ABEC) for Concurrent Error Detection (CED) based on an industry quality Convolution Neural Network (CNN) accelerator with priority to meet high safety Diagnostic Coverage (DC) requirement and enhanced area and power efficiency. Furthermore, we developed an Algorithm Based Cluster Checker (ABCC) with coarse-grained error localization to improve run-time availability. Experimental results showed that we could achieve above 99% DC with only 30% area and power overhead for a selected configuration.
引用
收藏
页码：248 / 253
页数：6
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