FPGA Implementation of Convolutional Neural Network Based on Stochastic Computing

被引：0
|
作者：
Kim, Daewoo [1 ]
Moghaddam, Mansureh S. [2 ]
Moradian, Hossein [2 ]
Sim, Hyeonuk [1 ]
Lee, Jongeun [1 ]
Choi, Kiyoung [2 ]
机构：
[1] UNIST, Sch Elect & Comp Engn, Ulsan, South Korea
[2] Seoul Natl Univ, Dept Elect & Comp Engn, Seoul, South Korea
来源：
2017 INTERNATIONAL CONFERENCE ON FIELD PROGRAMMABLE TECHNOLOGY (ICFPT) | 2017年
基金：
新加坡国家研究基金会;
关键词：
D O I：
暂无
中图分类号：
TP [自动化技术、计算机技术];
学科分类号：
0812 ;
摘要：
There has been a body of research to use stochastic computing (SC) for the implementation of neural networks, in the hope that it will reduce the area cost and energy consumption. However, no working neural network system based on stochastic computing has been demonstrated to support the viability of SC-based deep neural networks in terms of both recognition accuracy and cost/energy efficiency. In this demonstration we present an SC-based deep nenural network system that is highly accurate and efficient. Our system takes an input image and processes it with a convolutional neural network implemented on an FPGA using stochastic computing to recognize the input image, with nearly the same accuracy as conventional binary implementations.
引用
收藏
页码：287 / 290
页数：4
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