A systematic procedure for setting parameters in simulated annealing algorithms
Computers and Operations Research
Route packets, not wires: on-chip inteconnection networks
Proceedings of the 38th annual Design Automation Conference
SUNMAP: a tool for automatic topology selection and generation for NoCs
Proceedings of the 41st annual Design Automation Conference
Energy-aware mapping for tile-based NoC architectures under performance constraints
ASP-DAC '03 Proceedings of the 2003 Asia and South Pacific Design Automation Conference
Thousand core chips: a technology perspective
Proceedings of the 44th annual Design Automation Conference
Parameterizing simulated annealing for distributing Kahn process networks on multiprocessor SoCs
SOC'09 Proceedings of the 11th international conference on System-on-chip
Energy- and performance-aware mapping for regular NoC architectures
IEEE Transactions on Computer-Aided Design of Integrated Circuits and Systems
t(k)-SA: accelerated simulated annealing algorithm for application mapping on networks-on-chip
Proceedings of the 14th annual conference on Genetic and evolutionary computation
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Application mapping is an important issue in designing systems based on many-core networks-on-chip (NoCs). Simulated Annealing (SA) has been often used for searching for the optimized solution of application mapping problem. The parameters applied in the SA algorithm jointly control the annealing schedule and have great impact on the runtime and the quality of the final solution of the SA algorithm. The optimized parameters should be selected in a systematic way for each particular mapping problem, instead of using an identical set of empirical parameters for all problems. In this work, we apply an optimization method, Nelder-Mead simplex method, to obtain optimized parameters of SA. The experiment shows that with optimized parameters, we can get an average 237 times speedup of the SA algorithm, compared to the work where the empirical values are used for setting parameters. For the set of benchmarks, the proposed parameter-optimized SA algorithm achieves comparable communication energy consumption using less than 1% of iterations of that used in the reference work.