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Accelerated artificial neural networks on FPGA for fault detection in automotive systems
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Shreejith, Shanker, Anshuman, Bezborah and Fahmy, Suhaib A. (2016) Accelerated artificial neural networks on FPGA for fault detection in automotive systems. In: Design Automation and Test in Europe Conference (DATE), Dresden, Germany, 14–18 Mar 2016. Published in: 2016 Design, Automation & Test in Europe Conference & Exhibition (DATE) pp. 37-42. ISBN 9783981537062. ISSN 1558-1101.
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Official URL: https://ieeexplore.ieee.org/document/7459277
Abstract
Modern vehicles are complex distributed systems with critical real-time electronic controls that have progressively replaced their mechanical/hydraulic counterparts, for performance and cost benefits. The harsh and varying vehicular environment can induce multiple errors in the computational/communication path, with temporary or permanent effects, thus demanding the use of fault-tolerant schemes. Constraints in location, weight, and cost prevent the use of physical redundancy for critical systems in many cases, such as within an internal combustion engine. Alternatively, algorithmic techniques like artificial neural networks (ANNs) can be used to detect errors and apply corrective measures in computation. Though adaptability of ANNs presents advantages for fault-detection and fault-tolerance measures for critical sensors, implementation on automotive grade processors may not serve required hard deadlines and accuracy simultaneously. In this work, we present an ANN-based fault-tolerance system based on hybrid FPGAs and evaluate it using a diesel engine case study. We show that the hybrid platform outperforms an optimised software implementation on an automotive grade ARM Cortex M4 processor in terms of latency and power consumption, also providing better consolidation.
Item Type: | Conference Item (Paper) | ||||||
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Subjects: | Q Science > QA Mathematics > QA76 Electronic computers. Computer science. Computer software T Technology > TK Electrical engineering. Electronics Nuclear engineering |
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Divisions: | Faculty of Science, Engineering and Medicine > Engineering > Engineering | ||||||
Library of Congress Subject Headings (LCSH): | Neural networks (Computer science) | ||||||
Journal or Publication Title: | 2016 Design, Automation & Test in Europe Conference & Exhibition (DATE) | ||||||
Publisher: | IEEE | ||||||
ISBN: | 9783981537062 | ||||||
ISSN: | 1558-1101 | ||||||
Official Date: | 28 April 2016 | ||||||
Dates: |
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Page Range: | pp. 37-42 | ||||||
Status: | Peer Reviewed | ||||||
Publication Status: | Published | ||||||
Access rights to Published version: | Restricted or Subscription Access | ||||||
Date of first compliant deposit: | 26 January 2016 | ||||||
Date of first compliant Open Access: | 26 January 2016 | ||||||
Conference Paper Type: | Paper | ||||||
Title of Event: | Design Automation and Test in Europe Conference (DATE) | ||||||
Type of Event: | Conference | ||||||
Location of Event: | Dresden, Germany | ||||||
Date(s) of Event: | 14–18 Mar 2016 | ||||||
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