Multiple-deep neural network accelerators for next-generation artificial intelligence systems
File(s)2205.09376.pdf (652.09 KB)
Accepted version
Author(s)
Venieris, Stylianos I
Bouganis, Christos-Savvas
Lane, Nicholas D
Type
Journal Article
Abstract
The next generation of artificial intelligence (AI) systems will have multi-deep neural network (multi-DNN) workloads as their core. Large-scale deployment of AI services and integration across mobile devices require additional breakthroughs in the computer architecture front, with processors that can maintain high performance as the number of DNNs increase, giving rise to the topic of multi-DNN accelerator design.
Date Issued
2023-03
Date Acceptance
2023-03-01
Citation
Computer, 2023, 56 (3), pp.70-79
ISSN
0018-9162
Publisher
IEEE
Start Page
70
End Page
79
Journal / Book Title
Computer
Volume
56
Issue
3
Copyright Statement
Copyright © 2023 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
https://www.webofscience.com/api/gateway?GWVersion=2&SrcApp=PARTNER_APP&SrcAuth=LinksAMR&KeyUT=WOS:000966362400001&DestLinkType=FullRecord&DestApp=ALL_WOS&UsrCustomerID=a2bf6146997ec60c407a63945d4e92bb
Subjects
Artificial intelligence
Computer architecture
Computer Science
Computer Science, Hardware & Architecture
Computer Science, Software Engineering
Mobile handsets
Neural networks
Next generation networking
Performance evaluation
Program processors
Science & Technology
Technology
Publication Status
Published
Date Publish Online
2023-03-03