Publication

Abstract : Parallelism and adaptability are two distinct architectural design considerations in embedded processors. Multicore processors accelerate application execution on account of their inherent parallelism and run-time reconfiguration capabilities add adaptability during infield deployment. To benefit from both these features, a reconfigurable multiprocessor architecture − QuadroCore has been developed. A novel reconfigurationmechanism has been incorporated that provides fast run-time adaptability in a 4-processor cluster. In this paper, this scheme of reconfiguration has been used to save energy when using QuadroCore for data-parallel applications. As a proof of concept, a data-intensive neural network application called Self-organising Maps has been implemented on QuadroCore. Via reconfiguration, energy reduction of up to 30% has been observed for animplementation in UMC’s 90nm standard cell technology.