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t(k)-SA: accelerated simulated annealing algorithm for application mapping on networks-on-chip

Published: 07 July 2012 Publication History

Abstract

Simulated Annealing (SA) algorithm is a promising method for solving combinatorial optimization problems. The only limitation of applying the SA algorithm to application mapping problem on many-core networks-on-chip (NoCs) is its low speed. To alleviate this limitation, an accelerated SA algorithm called tk-SA algorithm is proposed in this work. The tk-SA algorithm starts the annealing process from a lower initial temperature tk with an optimized initial mapping solution. Based on the analysis of the typical behavior of the general SA algorithm, an efficient method is proposed for determining the temperature tk. Quantitative evaluations verify that the method is capable of obtaining an appropriate tk such that the tk-SA algorithm can reproduce the behavior of the full-range SA from temperature tk. Experimental results show that compared with a parameter-optimized SA algorithm, the proposed tk-SA algorithm achieves an average speedup of 1.55 without loss of solution quality.

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Cited By

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  • (2023)Application Mapping Onto Network on Chip Using Genetic Algorithm and Ant Colony Optimisation2023 International Conference on Computer and Applications (ICCA)10.1109/ICCA59364.2023.10401486(1-6)Online publication date: 28-Nov-2023
  • (2023)Genetic Artificial Bee Colony for Mapping onto Network on Chip “GABC”Third Congress on Intelligent Systems10.1007/978-981-19-9225-4_11(131-143)Online publication date: 12-Mar-2023
  • (2023)Application Mapping onto Network on Chip Using Cat Swarm OptimizationInternational Symposium on Intelligent Informatics10.1007/978-981-19-8094-7_34(441-453)Online publication date: 5-Apr-2023
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        cover image ACM Conferences
        GECCO '12: Proceedings of the 14th annual conference on Genetic and evolutionary computation
        July 2012
        1396 pages
        ISBN:9781450311779
        DOI:10.1145/2330163
        Permission to make digital or hard copies of all or part of this work for personal or classroom use is granted without fee provided that copies are not made or distributed for profit or commercial advantage and that copies bear this notice and the full citation on the first page. Copyrights for components of this work owned by others than ACM must be honored. Abstracting with credit is permitted. To copy otherwise, or republish, to post on servers or to redistribute to lists, requires prior specific permission and/or a fee. Request permissions from [email protected]

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        Published: 07 July 2012

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        Author Tags

        1. application mapping
        2. networks-on-chip
        3. simulated annealing

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        GECCO '12: Genetic and Evolutionary Computation Conference
        July 7 - 11, 2012
        Pennsylvania, Philadelphia, USA

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        View all
        • (2023)Application Mapping Onto Network on Chip Using Genetic Algorithm and Ant Colony Optimisation2023 International Conference on Computer and Applications (ICCA)10.1109/ICCA59364.2023.10401486(1-6)Online publication date: 28-Nov-2023
        • (2023)Genetic Artificial Bee Colony for Mapping onto Network on Chip “GABC”Third Congress on Intelligent Systems10.1007/978-981-19-9225-4_11(131-143)Online publication date: 12-Mar-2023
        • (2023)Application Mapping onto Network on Chip Using Cat Swarm OptimizationInternational Symposium on Intelligent Informatics10.1007/978-981-19-8094-7_34(441-453)Online publication date: 5-Apr-2023
        • (2022)Application Mapping Onto Network on Chip Using Particul Swarm Optimisation With Genetic Algorithm “GAPSO”2022 International Conference on Computer and Applications (ICCA)10.1109/ICCA56443.2022.10039542(1-6)Online publication date: 20-Dec-2022
        • (2020)Genetic Node-Mapping Methods for Rapid Collective CommunicationsIEICE Transactions on Information and Systems10.1587/transinf.2018EDP7386E103.D:1(111-129)Online publication date: 1-Jan-2020
        • (2017)Optimal application mapping to 2D-mesh NoCs by using a tabu-based particle swarm methodologyProceedings of the 2nd International Workshop on Advanced Interconnect Solutions and Technologies for Emerging Computing Systems10.1145/3073763.3073766(17-22)Online publication date: 25-Jan-2017

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