Multi-objective co-exploration of source code transformations and design space architectures for low-power embedded systems

Abstract

The exploration of the architectural design space in terms of energy and performance is of mainly importance for a broad range of embedded platforms based on the System-On-Chip approach. This paper proposes a methodology for the co-exploration of the design space composed of architectural parameters and source program transformations. A heuristic technique based on Pareto Simulated Annealing (PSA) has been used to efficiently span the multi-objective co-design space composed of the product of the parameters related to the selected program transformations and the configurable architecture. The analysis of the proposed framework has been carried out for a parameterized superscalar architecture executing a selected set of benchmarks. The reported results show the effectiveness of the proposed co-exploration with respect to the independent exploration of the transformation and architectural spaces to efficiently derive approximate Pareto curves.