Name
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. |
Year | DOI | Venue |
|---|---|---|
2015 | 10.1109/CCGrid.2015.114 | Cluster, Cloud and Grid Computing |
Keywords | Field | DocType |
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,FPGA,accelerator,deep learning,prediction process | Computer architecture,Computer science,Field-programmable gate array,Real-time computing,Implementation,Artificial intelligence,Deep learning,Artificial neural network,Data access,Cloud computing,Power consumption | Conference |
ISSN | Citations | PageRank |
2376-4414 | 15 | 1.01 |
References | Authors | |
6 | 4 | |