Towards in-circuit tuning of deep learning designs
File(s)
Accepted version
Author(s)
Que, Zhiqiang
Noronha, Daniel Holanda
Zhao, Ruizhe
Wilton, Steven JE
Luk, Wayne
Type
Conference Paper
Abstract
This paper presents InTune, a novel approach for in-circuit tuning of deep learning designs targeting implementations in field-programmable gate array technology. This approach combines two promising techniques: domain-specific adaptation and in-circuit tuning. Domain-specific adaptation exploits domain-specific information in adapting pre-trained models to specific application domains, replacing standard convolution layers with efficient convolution blocks; the effects of such adaptation are then assessed by in-circuit tuning instruments to provide information to application builders for tuning the design. This approach is illustrated by its deployment in tuning deep neural networks, and its potential for a new generation of domain-specific tools with tight integration of synthesis and in-circuit tuning is explored.
Date Issued
2019-12-26
Date Acceptance
2019-12-01
Citation
2019 IEEE/ACM INTERNATIONAL CONFERENCE ON COMPUTER-AIDED DESIGN (ICCAD), 2019, pp.1-6
ISSN
1933-7760
Publisher
IEEE
Start Page
1
End Page
6
Journal / Book Title
2019 IEEE/ACM INTERNATIONAL CONFERENCE ON COMPUTER-AIDED DESIGN (ICCAD)
Copyright Statement
© 2019 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.
Identifier
http://gateway.webofknowledge.com/gateway/Gateway.cgi?GWVersion=2&SrcApp=PARTNER_APP&SrcAuth=LinksAMR&KeyUT=WOS:000524676400075&DestLinkType=FullRecord&DestApp=ALL_WOS&UsrCustomerID=1ba7043ffcc86c417c072aa74d649202
Source
38th IEEE/ACM International Conference on Computer-Aided Design (ICCAD)
Subjects
Science & Technology
Technology
Computer Science, Theory & Methods
Engineering, Electrical & Electronic
Computer Science
Engineering
Publication Status
Published
Start Date
2019-11-04
Finish Date
2019-11-10
Coverage Spatial
Westminster, CO
Date Publish Online
2019-12-26