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
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;
Conference_Titel :
Cluster, Cloud and Grid Computing (CCGrid), 2015 15th IEEE/ACM International Symposium on
Conference_Location :
Shenzhen
DOI :
10.1109/CCGrid.2015.114