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

@unpublished{Kouris:2019,
author = {Kouris, A and Venieris, SI and Rizakis, M and Bouganis, C-S},
publisher = {arXiv},
title = {Approximate LSTMs for time-constrained inference: Enabling fast reaction in self-driving cars},
url = {http://arxiv.org/abs/1905.00689v1},
year = {2019}
}
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RIS format (EndNote, RefMan)

TY  - UNPB
AB  - The need to recognise long-term dependencies in sequential data such as videostreams has made LSTMs a prominent AI model for many emerging applications.However, the high computational and memory demands of LSTMs introducechallenges in their deployment on latency-critical systems such as self-drivingcars which are equipped with limited computational resources on-board. In thispaper, we introduce an approximate computing scheme combining model pruning andcomputation restructuring to obtain a high-accuracy approximation of the resultin early stages of the computation. Our experiments demonstrate that using theproposed methodology, mission-critical systems responsible for autonomousnavigation and collision avoidance are able to make informed decisions based onapproximate calculations within the available time budget, meeting theirspecifications on safety and robustness.
AU  - Kouris,A
AU  - Venieris,SI
AU  - Rizakis,M
AU  - Bouganis,C-S
PB  - arXiv
PY  - 2019///
TI  - Approximate LSTMs for time-constrained inference: Enabling fast reaction in self-driving cars
UR  - http://arxiv.org/abs/1905.00689v1
UR  - http://hdl.handle.net/10044/1/74034
ER  -
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