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.

Multi-Objective Co-Exploration of Source Code Transformations and Design Space Architecture for Low-Power Embedded Systems

AGOSTA, GIOVANNI;PALERMO, GIANLUCA;SILVANO, CRISTINA
2004-01-01

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.
2004
Proceedings of the 2004 ACM symposium on Applied computing
1581138121
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Utilizza questo identificativo per citare o creare un link a questo documento: https://hdl.handle.net/11311/252607
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