Accelerated Artificial Neural Networks on FPGA for fault detection in automotive systems

Shanker Shreejith, Bezborah Anshuman, Suhaib A. Fahmy

Research output: Chapter in Book/Report/Conference proceedingConference contribution

10 Scopus citations


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.
Original languageEnglish (US)
Title of host publicationProceedings of the 2016 Design, Automation and Test in Europe Conference and Exhibition, DATE 2016
PublisherInstitute of Electrical and Electronics Engineers Inc.
Number of pages6
ISBN (Print)9783981537062
StatePublished - Apr 25 2016
Externally publishedYes

Bibliographical note

Generated from Scopus record by KAUST IRTS on 2021-03-16


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