Decision-theoretic design space exploration of multiprocessor platforms
IEEE Transactions on Computer-Aided Design of Integrated Circuits and Systems
Boosting design space explorations with existing or automatically learned knowledge
MMB'12/DFT'12 Proceedings of the 16th international GI/ITG conference on Measurement, Modelling, and Evaluation of Computing Systems and Dependability and Fault Tolerance
The COMPLEX methodology for UML/MARTE Modeling and design space exploration of embedded systems
Journal of Systems Architecture: the EUROMICRO Journal
A comparative evaluation of multi-objective exploration algorithms for high-level design
ACM Transactions on Design Automation of Electronic Systems (TODAES)
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Platform-based design represents the most widely used approach to design System-On-Chip (SOC) applications. In this context, the Design Space Exploration (DSE) phase consists of optimally configure a parameterized SOC platform in terms of system-level requirements depending on the target application. In this paper, we introduce the Discrete Particle Swarm Optimization methodology (DPSO) for supporting the DSE of an hardware platform. The proposed technique aims at efficiently profiling the target application and deriving an approximated Pareto set of system 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 show that the proposed DPSO technique can speed up the design space exploration time up to 5X with an accuracy of up to 70\% with respect to a full search exploration for the selected benchmarks. This work was supported in part by the ECunder grant for FP7-216693-MULTICUBE Project.