OSCAR: an Optimization Methodology Exploiting Spatial Correlation in Multi-core Design Space
This paper presents OSCAR, an Optimization methodology exploiting Spatial CorrelAtion of multi-coRe design space. The paper builds upon the observation that power consumption and performance metrics of spatially close design configurations (or points) are statistically correlated. We propose to exploit the correlation by using a Response Surface Model (RSM), i.e., a closed-form expression suitable for predicting the quality of non-simulated design points. This model is useful during the design space exploration (DSE) phase to quickly converge to the Pareto set of the multi-objective problem without executing lengthy simulations. We compare the proposed heuristic with state-of-the-art approaches (conventional, RSM-based and structured DOEs). Experimental results show that OSCAR is a faster heuristic with respect to state of the art techniques such as Response-Surface Pareto Iterative Refinement - ReSPIR and Nondominated Sorting Genetic Algorithm - NSGA-II. Reported results also show that OSCAR can significantly improve structured DOE approaches by slightly increasing the number of experiments.
IEEE Transactions on Computer-Aided Design
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chip multi processor, correlation based design, design space exploration, multi-core, multi-objective optimization, OSCAR