Imperial College London
Alternate

ProfessorChristos-SavvasBouganis

Faculty of Engineering, Department of Electrical and Electronic Engineering

Professor of Intelligent Digital Systems
 
 
 
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Contact

 

+44 (0)20 7594 6144christos-savvas.bouganis Website

 
 
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Location

 

904Electrical EngineeringSouth Kensington Campus

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Summary

 

Publications

Citation

BibTex format

@article{Venieris:2023:10.1109/MC.2022.3176845,
author = {Venieris, SI and Bouganis, C-S and Lane, ND},
doi = {10.1109/MC.2022.3176845},
journal = {Computer},
pages = {70--79},
title = {Multiple-deep neural network accelerators for next-generation artificial intelligence systems},
url = {http://dx.doi.org/10.1109/MC.2022.3176845},
volume = {56},
year = {2023}
}
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RIS format (EndNote, RefMan)

TY  - JOUR
AB  - 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.
AU  - Venieris,SI
AU  - Bouganis,C-S
AU  - Lane,ND
DO  - 10.1109/MC.2022.3176845
EP  - 79
PY  - 2023///
SN  - 0018-9162
SP  - 70
TI  - Multiple-deep neural network accelerators for next-generation artificial intelligence systems
T2  - Computer
UR  - http://dx.doi.org/10.1109/MC.2022.3176845
UR  - https://www.webofscience.com/api/gateway?GWVersion=2&SrcApp=PARTNER_APP&SrcAuth=LinksAMR&KeyUT=WOS:000966362400001&DestLinkType=FullRecord&DestApp=ALL_WOS&UsrCustomerID=a2bf6146997ec60c407a63945d4e92bb
UR  - https://ieeexplore.ieee.org/document/10058760
UR  - http://hdl.handle.net/10044/1/109722
VL  - 56
ER  -
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