SenseDSE: Sensitivity-Based Performance Evaluation for Design Space Exploration of Microarchitecture

The design of modern processors is driven by plenty of benchmarks. As processors evolve and applications expand, the complexity of benchmark programs grows, which increases the computational cost of architecture design space exploration (DSE). To accelerate performance evaluations of processors in DSE, we developed a sensitivity-based framework for performance evaluation of a large set of benchmarks. The framework avoids simulating the insensitive benchmarks to the adjusted parameters during the exploration of designs. We developed a sampling algorithm based on evolutionary strategies to provide learning data for the sensitivity analysis and enhance the performance of the fast performance evaluation algorithm. We integrated this framework into a RISe-V processor architecture exploration framework. Our experiments revealed that we could achieve a significant acceleration in runtime with negligible accuracy loss in DSE.