Parameter Space Representation of Pareto Front to Explore Hardware-Software Dependencies

Embedded systems design requires conflicting objectives to be optimized with an appropriate choice of hardware-software parameters. A simulation campaign can guide the design in finding the best trade-offs, but due to the big number of possible configurations, it is often unfeasible to simulate them all. For these reasons, design space exploration algorithms aim at finding near-optimal system configurations by simulating only a subset of them.

In this work, we present PS, a new multiobjective optimization algorithm, and evaluate it in the context of the embedded system design. The basic idea is to recognize interesting regions—that is, regions of the configuration space that provide better configurations with respect to other ones. PS evaluates more configurations in the interesting regions while less thoroughly exploring the rest of the configuration space. After a detailed formal description of the algorithm and the underlying concepts, we show a case study involving the hardware/software exploration of a VLIW architecture. Qualitative and quantitative comparisons of PS against a well-known multiobjective genetic approach demonstrate that while not outperforming it in terms of Pareto dominance, the proposed approach can balance the uniformity and granularity qualities of the solutions found, obtaining more extended Pareto fronts that provide a wider view of the potentiality of the designed device. Therefore, PS represents a further valid choice for the designer when objective constrains allow it.