Estimation of distribution-based multiobjective design space exploration for energy and throughput-optimized MPSoCs

Abstract

Modern multicore architectures comprise a large set of components and parameters that require being matchedto achieve the best balance between power consumption and throughput performance for a particular application domain.The exploration of design space for finding the best power–throughput trade-off is a combinatorial optimization problemwith a large number of combinations, and. in general, its solution is prohibitively difficult to be explored exhaustively.However, fortunately, evolutionary algorithms (EAs) have the potential to efficiently solve this problem with reasonablecomputational complexity. In this paper, we consider a multiobjective design space exploration (DSE) problem with twoconflicting objectives. The first objective corresponds to power consumption minimization while the second objectiverelates to throughput maximization. We approach this problem by employing the estimation of distribution algorithm(EDA), which belongs to the family of EAs. The proposed EDA-based DSE (EDA-DSE) scheme efficiently selects thedesign parameters (i.e. cache size, number of cores, and operating frequency) with an efficient power–throughput ratio.The proposed scheme is verified using cycle-accurate simulations over a set of benchmarks and the simulation results showa significant reduction in energy-delay product for all benchmark applications when compared to the default baselineconfiguration and genetic algorithm.