Abstract: In this paper, we address the problem of the efficient exploration
of the architectural design space for parameterized embedded systems. The
exploration problem is multi-objective (e.g., energy and delay), so the main
goal of this work is to find a good approximation of the Pareto-optimal
configurations representing the best energy/delay trade-offs by varying the
architectural parameters of the target system. In particular, the paper
presents a Design Space Exploration (DSE) framework to simulate the target
system and to dynamically profile the target applications. In the proposed DSE
framework, a set of heuristic algorithms have been analyzed to reduce the
overall exploration time by computing an approximated Pareto set of
configurations with respect to the selected figures of merit. Once the
approximated Pareto set has been built, the designer can quickly select the
best system configuration satisfying the constraints. Experimental results,
derived from the application of the proposed DSE framework to a superscalar
architecture, show that the exploration time can be reduced by three orders of
magnitude with respect to the full search approach, while maintaining a good
level of accuracy.
Keywords: Design space exploration, low-power design, platform-based design, multi-objective optimisation
