ICES '00 Proceedings of the Third International Conference on Evolvable Systems: From Biology to Hardware
Proceedings of the European Conference on Genetic Programming
Getting Most Out of Evolutionary Approaches
EH '02 Proceedings of the 2002 NASA/DoD Conference on Evolvable Hardware (EH'02)
Evolutionary Fault Recovery in a Virtex FPGA Using a Representation that Incorporates Routing
IPDPS '03 Proceedings of the 17th International Symposium on Parallel and Distributed Processing
WHICH CONCURRENT ERROR DETECTION SCHEME TO CHOOSE?
ITC '00 Proceedings of the 2000 IEEE International Test Conference
Exploring FPGA Structures for Evolving Fault Tolerant Hardware
EH '03 Proceedings of the 2003 NASA/DoD Conference on Evolvable Hardware
IOLTS '04 Proceedings of the International On-Line Testing Symposium, 10th IEEE
A genetic representation for evolutionary fault recovery in Virtex FPGAs
ICES'03 Proceedings of the 5th international conference on Evolvable systems: from biology to hardware
Making use of population information in evolutionary artificialneural networks
IEEE Transactions on Systems, Man, and Cybernetics, Part B: Cybernetics
Evolution of synthetic RTL benchmark circuits with predefined testability
ACM Transactions on Design Automation of Electronic Systems (TODAES)
A multilayer framework supporting autonomous run-time partial reconfiguration
IEEE Transactions on Very Large Scale Integration (VLSI) Systems
Autonomic fault-handling and refurbishment using throughput-driven assessment
Applied Soft Computing
The route to a defect tolerant LUT through artificial evolution
Genetic Programming and Evolvable Machines
Challenges of evolvable hardware: past, present and the path to a promising future
Genetic Programming and Evolvable Machines
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While the fault repair capability of Evolvable Hardware (EH) approaches have been previously demonstrated, further improvements to fault handling capability can be achieved by exploiting population diversity during all phases of the fault handling process. A new paradigm for online EH regeneration using Genetic Algorithms (GAs) called Consensus Based Evaluation (CBE) is developed where the performance of individuals is assessed based on broad consensus of the population instead of a conventional fitness function. Adoption of CBE enables information contained in the population to not only enrich the evolutionary process, but also support fault detection and isolation. On-line regeneration of functionality is achieved without additional test vectors by using the results of competitions between individuals in the population. Relative fitness measures support adaptation of the fitness evaluation procedure to support graceful degredation even in the presence of unpredictable changes in the operational environment, inputs, or the FPGA application. Application of CBE to FPGA-based multipliers demonstrates 100% isolation of randomly injected stuckat faults and evolution of a complete regeneration within 135 repair iterations while precluding the propagation of any discrepant output. The throughput of the system is maintained at 85.35% throughout the repair process.