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Evaluation of Histogram of Oriented Gradients Soft Errors Criticality for Automotive Applications

Authors:

Fernando Fernandes,

Philippe Navaux,

Paolo RechAuthors Info & Claims

ACM Transactions on Architecture and Code Optimization (TACO), Volume 13, Issue 4

Article No.: 38, Pages 1 - 25

https://doi.org/10.1145/2998573

Published: 15 November 2016 Publication History

Abstract

Pedestrian detection reliability is a key problem for autonomous or aided driving, and methods that use Histogram of Oriented Gradients (HOG) are very popular. Embedded Graphics Processing Units (GPUs) are exploited to run HOG in a very efficient manner. Unfortunately, GPUs architecture has been shown to be particularly vulnerable to radiation-induced failures. This article presents an experimental evaluation and analytical study of HOG reliability. We aim at quantifying and qualifying the radiation-induced errors on pedestrian detection applications executed in embedded GPUs.

We analyze experimental results obtained executing HOG on embedded GPUs from two different vendors, exposed for about 100 hours to a controlled neutron beam at Los Alamos National Laboratory. We consider the number and position of detected objects as well as precision and recall to discriminate critical erroneous computations. The reported analysis shows that, while being intrinsically resilient (65% to 85% of output errors only slightly impact detection), HOG experienced some particularly critical errors that could result in undetected pedestrians or unnecessary vehicle stops.

Additionally, we perform a fault-injection campaign to identify HOG critical procedures. We observe that Resize and Normalize are the most sensitive and critical phases, as about 20% of injections generate an output error that significantly impacts HOG detection. With our insights, we are able to find those limited portions of HOG that, if hardened, are more likely to increase reliability without introducing unnecessary overhead.

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Evaluation of Histogram of Oriented Gradients Soft Errors Criticality for Automotive Applications

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cover image ACM Transactions on Architecture and Code Optimization

ACM Transactions on Architecture and Code Optimization Volume 13, Issue 4

December 2016

648 pages

ISSN:1544-3566

EISSN:1544-3973

DOI:10.1145/3012405

Editor:
Koen De Bosschere
Ghent University

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Copyright © 2016 ACM.

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Publication History

Published: 15 November 2016

Accepted: 01 September 2016

Revised: 01 August 2016

Received: 01 June 2016

Published in TACO Volume 13, Issue 4

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Bolchini CCassano LMiele AToschi A(2023)Fast and Accurate Error Simulation for CNNs Against Soft ErrorsIEEE Transactions on Computers10.1109/TC.2022.318427472:4(984-997)Online publication date: 1-Apr-2023
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Laskar SRahman MZhang BLi G(2022)Characterizing Deep Learning Neural Network Failures Between Algorithmic Inaccuracy and Transient Hardware Faults2022 IEEE 27th Pacific Rim International Symposium on Dependable Computing (PRDC)10.1109/PRDC55274.2022.00020(54-67)Online publication date: Nov-2022
https://doi.org/10.1109/PRDC55274.2022.00020
Laskar SRahman MLi G(2022)TensorFI+: A Scalable Fault Injection Framework for Modern Deep Learning Neural Networks2022 IEEE International Symposium on Software Reliability Engineering Workshops (ISSREW)10.1109/ISSREW55968.2022.00074(246-251)Online publication date: Oct-2022
https://doi.org/10.1109/ISSREW55968.2022.00074
Bolchini CBosio ACassano LDeveautour BNatale GMiele AO'Connor IVatajelu I(2022)Dependability of Alternative Computing Paradigms for Machine Learning: hype or hope?2022 25th International Symposium on Design and Diagnostics of Electronic Circuits and Systems (DDECS)10.1109/DDECS54261.2022.9770138(7-13)Online publication date: 6-Apr-2022
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Bolchini CCassano LMiele ABiasielli M(2020)Lightweight Fault Detection and Management for Image Restoration2020 IEEE International Symposium on Defect and Fault Tolerance in VLSI and Nanotechnology Systems (DFT)10.1109/DFT50435.2020.9250844(1-6)Online publication date: 19-Oct-2020
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