DeepPump: Multi-pumping deep Neural Networks
File(s)asap17rzv9.pdf (104.38 KB)
Accepted version
Author(s)
Zhao, R
Todman, T
Luk, W
Niu, X
Type
Conference Paper
Abstract
This paper presents DeepPump, an approach that generates CNN hardware designs with multi-pumping, which have competitive performance when compared with previous designs. Future work includes integrating DeepPump with other optimisations, and providing further evaluations on various FPGA platforms.
Date Issued
2017-07-31
Date Acceptance
2017-07-01
Citation
Proceedings of the International Conference on Application-Specific Systems, Architectures and Processors, 2017, pp.206-206
ISBN
9781509048250
ISSN
1063-6862
Publisher
IEEE
Start Page
206
End Page
206
Journal / Book Title
Proceedings of the International Conference on Application-Specific Systems, Architectures and Processors
Copyright Statement
© 2017 IEEE. Personal use of this material is permitted. Permission from IEEE must be obtained for all other uses, in any current or future media, including reprinting/republishing this material for advertising or promotional purposes, creating new collective works, for resale or redistribution to servers or lists, or reuse of any copyrighted component of this work in other works.
Sponsor
Engineering & Physical Science Research Council (EPSRC)
Commission of the European Communities
Grant Number
EP/I012036/1
671653
Source
2017 IEEE 28th International Conference on Application-specific Systems, Architectures and Processors (ASAP)
Publication Status
Published
Start Date
2017-07-10
Finish Date
2017-07-12
Coverage Spatial
Seattle, WA, USA