Efficient design space exploration for application specific systems-on-a-chip
A reduction in the time-to-market has led to widespread use of pre-designed parametric architectural solutions known as system-on-a-chip (SoC) platforms. A system designer has to configure the platform in such a way as to optimize it for the execution of a specific application. Very frequently, however, the space of possible configurations that can be mapped onto a SoC platform is huge and the computational effort needed to evaluate a single system configuration can be very costly. In this paper we propose an approach which tackles the problem of design space exploration (DSE) in both of the fronts of the reduction of the number of system configurations to be simulated and the reduction of the time required to evaluate (i.e., simulate) a system configuration. More precisely, we propose the use of Multi-objective Evolutionary Algorithms as optimization technique and Fuzzy Systems for the estimation of the performance indexes to be optimized. The proposed approach is applied on a highly parameterized SoC platform based on a parameterized VLIW processor and a parameterized memory hierarchy for the optimization of performance and power dissipation. The approach is evaluated in terms of both accuracy and efficiency and compared with several established DSE approaches. The results obtained for a set of multimedia applications show an improvement in both accuracy and exploration time.
design space exploration multi-objective optimization embedded system design very long instruction word processor fuzzy estimation evolutionary computation