Self-Reconfigurable Evolvable Hardware System for Adaptive Image Processing

This paper presents an evolvable hardware system, fully contained in an FPGA, which is capable of autonomously generating digital processing circuits, implemented on an array of processing elements (PEs). Candidate circuits are generated by an embedded evolutionary algorithm and implemented by means...

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Veröffentlicht in:IEEE transactions on computers Jg. 62; H. 8; S. 1481 - 1493
Hauptverfasser: Salvador, R., Otero, A., Mora, J., de la Torre, E., Riesgo, T., Sekanina, L.
Format: Journal Article
Sprache:Englisch
Veröffentlicht: New York IEEE 01.08.2013
The Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Schlagworte:
adaptable architectures
Adaptive systems
Arrays
autonomous systems
Circuits
Evolutionary computing
evolutionary computing and genetic algorithms
Evolvable hardware
Filtering
Filtration
FPGAs
Genetic algorithms
Logic
reconfigurable hardware
Reconfiguration
self-adaptive systems
Self-organizing networks
Studies
ISSN:0018-9340, 1557-9956
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Zusammenfassung:This paper presents an evolvable hardware system, fully contained in an FPGA, which is capable of autonomously generating digital processing circuits, implemented on an array of processing elements (PEs). Candidate circuits are generated by an embedded evolutionary algorithm and implemented by means of dynamic partial reconfiguration, enabling evaluation in the final hardware. The PE array follows a systolic approach, and PEs do not contain extra logic such as path multiplexers or unused logic, so array performance is high. Hardware evaluation in the target device and the fast reconfiguration engine used yield smaller reconfiguration than evaluation times. This means that the complete evaluation cycle is faster than software-based approaches and previous evolvable digital systems. The selected application is digital image filtering and edge detection. The evolved filters yield better quality than classic linear and nonlinear filters using mean absolute error as standard comparison metric. Results do not only show better circuit adaptation to different noise types and intensities, but also a nondegrading filtering behavior. This means they may be run iteratively to enhance filtering quality. These properties are even kept for high noise levels (40 percent). The system as a whole is a step toward fully autonomous, adaptive systems.
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ISSN:0018-9340
1557-9956
DOI:10.1109/TC.2013.78