Artificial Neural Network Implementation in FPGA: A Case Study

被引：0
|
作者：
Li, Shuai [1 ]
Choi, Ken [1 ]
Lee, Yunsik [2 ]
机构：
[1] IIT, Dept Elect & Comp Engn, Chicago, IL 60616 USA
[2] UNIST, Sch ECE, Ulsan, South Korea
来源：
2016 INTERNATIONAL SOC DESIGN CONFERENCE (ISOCC) | 2016年
关键词：
artificial neural network; parallelism; back propagation; LReLU; FPGA;
D O I：
暂无
中图分类号：
TP3 [计算技术、计算机技术];
学科分类号：
0812 ;
摘要：
Artificial Neural Network (ANN) is very powerful to deal with signal processing, computer vision and many other recognition problems. In this work, we implement basic ANN in FPGA. Compared with software, the FPGA implementation can utilize parallelism to speedup processing time. Additionally, hardware implementation can save more power compared with CPU/GPU. Our ANN in FPGA has a high learning ability, for logical XOR problem, which reduced the error rate from 10(-2) to 10(-4).
引用
收藏
页码：297 / 298
页数：2
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