VSCNN: Convolution neural network accelerator with vector sparsity

Chang Kuo-Wei, Tian-Sheuan Chang

研究成果: Conference contribution同行評審

2 引文 斯高帕斯(Scopus)

摘要

Hardware accelerator for convolution neural network (CNNs) enables real time applications of artificial intelligence technology. However, most of the accelerators only support dense CNN computations or suffers complex control to support fine grained sparse networks. To solve above problem, this paper presents an efficient CNN accelerator with 1-D vector broadcasted input to support both dense network as well as vector sparse network with the same hardware and low overhead. The presented design achieves 1.93X speedup over the dense CNN computations.

原文English
主出版物標題2019 IEEE International Symposium on Circuits and Systems, ISCAS 2019 - Proceedings
發行者Institute of Electrical and Electronics Engineers Inc.
ISBN(電子)9781728103976
DOIs
出版狀態Published - 26 五月 2019
事件2019 IEEE International Symposium on Circuits and Systems, ISCAS 2019 - Sapporo, Japan
持續時間: 26 五月 201929 五月 2019

出版系列

名字Proceedings - IEEE International Symposium on Circuits and Systems
2019-May
ISSN(列印)0271-4310

Conference

Conference2019 IEEE International Symposium on Circuits and Systems, ISCAS 2019
國家Japan
城市Sapporo
期間26/05/1929/05/19

指紋 深入研究「VSCNN: Convolution neural network accelerator with vector sparsity」主題。共同形成了獨特的指紋。

引用此