IEEE Transactions on Computers · 2016 · 27 citations · 46 references
EngineeringComputer ArchitectureDependable System ArchitectureHardware SecurityReliability EngineeringHigh-performance ArchitectureSystems EngineeringParallel ComputingComputer EngineeringComputer ScienceReconfigurable ArchitectureFpga DesignReconfigurabilityRuntime Reconfigurable ArchitecturesHardware AccelerationProgram AnalysisParallel ProgrammingRuntime Reconfigurable AcceleratorsAccelerator Diversification Method
Runtime reconfigurable architectures based on Field-Programmable Gate Arrays (FPGAs) allow areaand power-efficient acceleration of complex applications. However, being manufactured in latest semiconductor process technologies, FPGAs are increasingly prone to aging effects, which reduce the reliability and lifetime of such systems. Aging mitigation and fault tolerance techniques for the reconfigurable fabric become essential to realize dependable reconfigurable architectures. This article presents an accelerator diversification method that creates multiple configurations for runtime reconfigurable accelerators that are diversified in their usage of Configurable Logic Blocks (CLBs). In particular, it creates a minimal number of configurations such that all single-CLB and some multi-CLB faults can be tolerated. For each fault we ensure that there is at least one configuration that does not use that CLB. Second, a novel runtime accelerator placement algorithm is presented that exploits the diversity in resource usage of these configurations to balance the stress imposed by executions of the accelerators on the reconfigurable fabric. By tracking the stress due to accelerator usage at runtime, the stress is balanced both within a reconfigurable region as well as over all reconfigurable regions of the system. The accelerator placement algorithm also considers faulty CLBs in the regions and selects the appropriate configuration such that the system maintains a high performance in presence of multiple permanent faults. Experimental results demonstrate that our methods deliver up to 3.7× higher performance in presence of faults at marginal runtime costs and 1.6× higher MTTF than state-ofthe-art aging mitigation methods.
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