Point Estimation in Design Space Exploration Using Local Regression Modeling

Configuration of an application-specific instruction-set processor (ASIP) through an exhaustive search of the design space is computationally prohibitive. To enable further automation, new methods are needed to speed up design space exploration (DSE), since the evaluation of each configuration is very expensive in terms of run-time. One method of speeding up DSE is to simulate a small sample of the design space and then use this information to model the rest of the design space using statistical regression techniques. From this model, unknown points within the space can be estimated. This approach has the potential to speed-up DSE time by several orders of magnitude. In this paper, we study the effectiveness of using local regressions statistics (LOESS) to model the design space. We compare the use of a non-parametric statistics based on LOESS to polynomial regressions in their ability to estimate unknown points. After showing the effectiveness of LEOSS, we apply it to the configuration of the pattern history table (PHT) of a branch predictor when configured to minimize the overall power dissipation of the processor.