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

@inproceedings{Kouris:2019:10.1109/ISVLSI.2019.00107,
author = {Kouris, A and Venieris, S and Bouganis, C-S},
doi = {10.1109/ISVLSI.2019.00107},
pages = {570--575},
publisher = {IEEE COMPUTER SOC},
title = {Towards efficient on-board deployment of DNNs on intelligent autonomous systems},
url = {http://dx.doi.org/10.1109/ISVLSI.2019.00107},
year = {2019}
}
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RIS format (EndNote, RefMan)

TY  - CPAPER
AB  - With their unprecedented performance in major AI tasks, deep neural networks (DNNs) have emerged as a primary building block in modern autonomous systems. Intelligent systems such as drones, mobile robots and driverless cars largely base their perception, planning and application-specific tasks on DNN models. Nevertheless, due to the nature of these applications, such systems require on-board local processing in order to retain their autonomy and meet latency and throughput constraints. In this respect, the large computational and memory demands of DNN workloads pose a significant barrier on their deployment on the resource-and power-constrained compute platforms that are available on-board. This paper presents an overview of recent methods and hardware architectures that address the system-level challenges of modern DNN-enabled autonomous systems at both the algorithmic and hardware design level. Spanning from latency-driven approximate computing techniques to high-throughput mixed-precision cascaded classifiers, the presented set of works paves the way for the on-board deployment of sophisticated DNN models on robots and autonomous systems.
AU  - Kouris,A
AU  - Venieris,S
AU  - Bouganis,C-S
DO  - 10.1109/ISVLSI.2019.00107
EP  - 575
PB  - IEEE COMPUTER SOC
PY  - 2019///
SN  - 2159-3469
SP  - 570
TI  - Towards efficient on-board deployment of DNNs on intelligent autonomous systems
UR  - http://dx.doi.org/10.1109/ISVLSI.2019.00107
UR  - http://gateway.webofknowledge.com/gateway/Gateway.cgi?GWVersion=2&SrcApp=PARTNER_APP&SrcAuth=LinksAMR&KeyUT=WOS:000538332100098&DestLinkType=FullRecord&DestApp=ALL_WOS&UsrCustomerID=1ba7043ffcc86c417c072aa74d649202
UR  - https://ieeexplore.ieee.org/document/8839367
UR  - http://hdl.handle.net/10044/1/80750
ER  -
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