Optimizing a Superscalar System using Multi-objective Design Space Exploration

One way to cope with a huge design space formed by several parameters is using methods for Automatic Design Space Exploration (ADSE). Recently we developed a Framework for Automatic Design Space Explorations focused on micro-architectural optimizations. In this article we evaluate the influence of three different evolutionary algorithms on the performance of design space explorations. More precisely, we selected two genetic algorithms, NSGA-II and SPEA2, as well as the bio-inspired SMPSO algorithm, running a particle swarm optimization. With these algorithms we run FADSE to optimize both the parameters of the Grid ALU Processor (GAP) microarchitecture and of the GAPtimize post-link code optimizer. An analysis of the simulation results showed a very good performance of the SMPSO algorithm during the design space exploration process on the GAP simulator. SPEA2 provided slightly better results than NSGA-II when they were used on the design space exploration of both GAP and GAPtimize.