Title
Architectural exploration of multilayer perceptron models for on-chip and real-time road sign classification.
Abstract
Road sign recognition is part of the automatic driver assistance systems implemented on the dashboard of vehicles. The recognition task is often carried out based on a classification procedure manipulating the detected signs. Classification tasks can be resolved by the use of multilayer artificial neural network systems. This article proposes an optimized real-time on-chip hardware implementation of multilayer perceptron system used for road sign classification. Four architectural approaches were described: on the one hand, the classic and the serial optimized architectures that offer a very significant reduction in hardware resources, and, on the other hand, the parallel and the optimized architectures, which offer a much reduced, time execution. In order to benefit from the advantages of the allocated hardware resources and the classification of the runtime process, these four architectures have been implemented on field programmable gate array Virtex-6 devices and their performances were quantified and evaluated according to a cost criterion. The energy dissipated by each of these architectures was measured; the achieved results have allowed us to conclude that the serial optimized architecture is the optimal solution, since it creates a tradeoff between the low cost, and the energy efficiency, and still real-time for the considered application.
Year
DOI
Venue
2018
10.1177/0959651818778478
PROCEEDINGS OF THE INSTITUTION OF MECHANICAL ENGINEERS PART I-JOURNAL OF SYSTEMS AND CONTROL ENGINEERING
Keywords
Field
DocType
Multilayer perceptron,real time,hardware implementation,road sign classification
Pattern recognition,Computer science,Multilayer perceptron,Artificial intelligence
Journal
Volume
Issue
ISSN
232
SP6
0959-6518
Citations 
PageRank 
References 
0
0.34
0
Authors
5
Name
Order
Citations
PageRank
Hassene Faiedh1213.25
Sabrine Hamdi200.34
Safa Bouguezzi321.38
Wajdi Farhat400.34
Chokri Souani5418.75