Tuning DSE for heterogeneous multi-processor embedded systems by means of a self-equalized weighted sum method

Heterogeneous multi-processor platforms are becoming widely diffused in the embedded system domain, mainly because of the opportunity to improve timing performance and, at the same time, to minimize energy/power consumption and costs. In using such kind of platforms, to be able to consider the trade-offs among different goals, a Design Space Exploration (DSE) is generally adopted. For this, existing DSE approaches typically rely on evolutionary algorithms to solve Multi-Objective Optimization Problems (MOOP) by minimizing a linear combination of weighted objective functions (i.e., Weighted Sum Method, WSM). The problem is then shifted towards the identification of weights able to represent desired tradeoffs. In such a context, this paper focuses on DSE for heterogeneous multi-processor embedded systems and introduces an approach that, while still driven by a "decision maker", is able to self-tune weights to equalize objective functions contribution. In particular, this work presents a self-equalized WSM integrated into a genetic algorithm used to identify sub-optimal implementation alternatives in the context of an Electronic System Level HW/SW Co-Design flow.