DocumentCode
3079403
Title
A Deep Learning Prediction Process Accelerator Based FPGA
Author
Qi Yu ; Chao Wang ; Xiang Ma ; Xi Li ; Xuehai Zhou
Author_Institution
Sch. of Comput. Sci., Univ. of Sci. & Technol. of China, Hefei, China
fYear
2015
fDate
4-7 May 2015
Firstpage
1159
Lastpage
1162
Abstract
Recently, machine learning is widely used in applications and cloud services. And as the emerging field of machine learning, deep learning shows excellent ability in solving complex learning problems. To give users better experience, high performance implementations of deep learning applications seem very important. As a common means to accelerate algorithms, FPGA has high performance, low power consumption, small size and other characteristics. So we use FPGA to design a deep learning accelerator, the accelerator focuses on the implementation of the prediction process, data access optimization and pipeline structure. Compared with Core 2 CPU 2.3GHz, our accelerator can achieve promising result.
Keywords
field programmable gate arrays; learning (artificial intelligence); complex learning problems; data access optimization; deep learning accelerator design; deep learning prediction process accelerator-based FPGA; field-programmable gate array; machine learning; pipeline structure; prediction process implementation; Clocks; Computer architecture; Field programmable gate arrays; Hardware; Interpolation; Neural networks; Training; FPGA; accelerator; deep learning; prediction process;
fLanguage
English
Publisher
ieee
Conference_Titel
Cluster, Cloud and Grid Computing (CCGrid), 2015 15th IEEE/ACM International Symposium on
Conference_Location
Shenzhen
Type
conf
DOI
10.1109/CCGrid.2015.114
Filename
7152611
Link To Document