A memory-based simulated annealing algorithm and a new auxiliary function for the fixed-outline floorplanning with soft blocks

A memory-based simulated annealing (MSA) algorithm is proposed for the fixed-outline floorplanning with soft blocks. MSA constructs a memory pool to store some historical best solutions. Moreover, it adopts a real-time monitoring strategy to check whether a solution has been trapped in a local optimum. In case a solution encounters this predicament, it will be replaced by the one from the memory pool, and the current temperature will be regenerated by continuously perturbing the new solution several times. To meet the fixed-outline requirements, a new auxiliary function is formulated based on the geometric structure of the current floorplan, and it is very helpful in driving MSA to search towards potential solution space. Concretely, the area information of all violated blocks is utilized to construct an auxiliary function. Moreover, the excessive area of a violated block can be weighted by three different coefficients, which depend on the relative position of the block and the fixed-outline. Additionally, due to its simple topology and strong applicability, B$$^{\star }$$⋆-tree representation is employed to perturb a solution in each generation. The efficiency of the proposed method is demonstrated on six GSRC floorplan benchmark examples with various white space and aspect ratios. Two groups of Matlab simulations show that our approach can achieve better floorplanning results and satisfy both the fixed-outline and non-overlapping constraints while optimizing circuit performance.

[1]  Thomas W. Williams,et al.  An industrial view of electronic design automation , 2000, IEEE Trans. Comput. Aided Des. Integr. Circuits Syst..

[2]  Evangeline F. Y. Young,et al.  Multivoltage Floorplan Design , 2010, IEEE Transactions on Computer-Aided Design of Integrated Circuits and Systems.

[3]  Ting-Chi Wang,et al.  Through-Silicon Via Planning in 3-D Floorplanning , 2011, IEEE Transactions on Very Large Scale Integration (VLSI) Systems.

[4]  C. D. Gelatt,et al.  Optimization by Simulated Annealing , 1983, Science.

[5]  Jzau-Sheng Lin,et al.  An image reconstruction algorithm for electrical capacitance tomography based on simulated annealing particle swarm optimization , 2015 .

[6]  Amir Hossein Gandomi,et al.  Stud krill herd algorithm , 2014, Neurocomputing.

[7]  Susmita Sur-Kolay,et al.  Floorplanning for Partially Reconfigurable FPGAs , 2011, IEEE Transactions on Computer-Aided Design of Integrated Circuits and Systems.

[8]  Gai-Ge Wang,et al.  An Improved Simulated Annealing Algorithm and Area Model for the Fixed-Outline Floorplanning with Hard Modules , 2015, 2015 3rd International Symposium on Computational and Business Intelligence (ISCBI).

[9]  Leandro dos Santos Coelho,et al.  Earthworm optimisation algorithm: a bio-inspired metaheuristic algorithm for global optimisation problems , 2018, Int. J. Bio Inspired Comput..

[10]  Amir Hossein Alavi,et al.  An effective krill herd algorithm with migration operator in biogeography-based optimization , 2014 .

[11]  Takeshi Yoshimura,et al.  An O-tree representation of non-slicing floorplan and its applications , 1999, DAC '99.

[12]  Chris C. N. Chu,et al.  SDS: An Optimal Slack-Driven Block Shaping Algorithm for Fixed-Outline Floorplanning , 2013, IEEE Transactions on Computer-Aided Design of Integrated Circuits and Systems.

[13]  Amir Hossein Alavi,et al.  A comprehensive review of krill herd algorithm: variants, hybrids and applications , 2017, Artificial Intelligence Review.

[14]  Yoji Kajitani,et al.  VLSI module placement based on rectangle-packing by the sequence-pair , 1996, IEEE Trans. Comput. Aided Des. Integr. Circuits Syst..

[15]  Amir Hossein Gandomi,et al.  Chaotic Krill Herd algorithm , 2014, Inf. Sci..

[16]  William R. Heller,et al.  On finding Most Optimal Rectangular Package Plans , 1982, DAC 1982.

[17]  Amir Hossein Gandomi,et al.  Opposition-based krill herd algorithm with Cauchy mutation and position clamping , 2016, Neurocomputing.

[18]  Sara Ceschia,et al.  Feature-based tuning of simulated annealing applied to the curriculum-based course timetabling problem , 2014, Comput. Oper. Res..

[19]  Harikrishnan Ramiah,et al.  Enumeration technique in very large-scale integration fixed-outline floorplanning , 2014, IET Circuits Devices Syst..

[20]  Seyedali Mirjalili,et al.  Three-dimensional path planning for UCAV using an improved bat algorithm , 2016 .

[21]  A. A. Kagalwalla,et al.  Design-Aware Defect-Avoidance Floorplanning of EUV Masks , 2013, IEEE Transactions on Semiconductor Manufacturing.

[22]  Yao-Wen Chang,et al.  B*-Trees: a new representation for non-slicing floorplans , 2000, DAC.

[23]  Zhihua Cui,et al.  Monarch butterfly optimization , 2015, Neural Computing and Applications.

[24]  Leandro dos Santos Coelho,et al.  A new metaheuristic optimisation algorithm motivated by elephant herding behaviour , 2017 .

[25]  Sam Kwong,et al.  Efficient Motion and Disparity Estimation Optimization for Low Complexity Multiview Video Coding , 2015, IEEE Transactions on Broadcasting.

[26]  Dalila Boughaci,et al.  A self-adaptive harmony search combined with a stochastic local search for the 0-1 multidimensional knapsack problem , 2016, Int. J. Bio Inspired Comput..

[27]  Jai-Ming Lin,et al.  F-FM: Fixed-Outline Floorplanning Methodology for Mixed-Size Modules Considering Voltage-Island Constraint , 2014, IEEE Transactions on Computer-Aided Design of Integrated Circuits and Systems.

[28]  Igor L. Markov,et al.  Fixed-outline floorplanning: enabling hierarchical design , 2003, IEEE Trans. Very Large Scale Integr. Syst..

[29]  Anil Kumar,et al.  Study of ABC and PSO algorithms as optimised adaptive noise canceller for EEG/ERP , 2016, Int. J. Bio Inspired Comput..

[30]  Yao-Wen Chang,et al.  TCG: a transitive closure graph-based representation for non-slicing floorplans , 2001, DAC '01.

[31]  Olav Tirkkonen,et al.  Simulated annealing variants for self-organized resource allocation in small cell networks , 2016, Appl. Soft Comput..

[32]  Bin Gu,et al.  A Robust Regularization Path Algorithm for $\nu $ -Support Vector Classification , 2017, IEEE Transactions on Neural Networks and Learning Systems.

[33]  Bin Gu,et al.  Incremental Support Vector Learning for Ordinal Regression , 2015, IEEE Transactions on Neural Networks and Learning Systems.

[34]  Xiaxia Yu,et al.  The two-dimensional double-entropy threshold based on the parallel genetic simulated annealing algorithms , 2016 .

[35]  Bin Gu,et al.  Incremental learning for ν-Support Vector Regression , 2015, Neural Networks.

[36]  William R. Heller,et al.  The Planar Package Planner for System Designers , 1982, DAC 1982.

[37]  Suash Deb,et al.  Solving 0–1 knapsack problem by a novel binary monarch butterfly optimization , 2017, Neural Computing and Applications.

[38]  Gaige Wang,et al.  Moth search algorithm: a bio-inspired metaheuristic algorithm for global optimization problems , 2016, Memetic Computing.

[39]  Yu Xue,et al.  A novel oriented cuckoo search algorithm to improve DV-Hop performance for cyber-physical systems , 2017, J. Parallel Distributed Comput..

[40]  Xiang-Jun Zhao,et al.  Solving 0–1 knapsack problems by chaotic monarch butterfly optimization algorithm with Gaussian mutation , 2018, Memetic Comput..

[41]  Takeshi Yoshimura,et al.  Fixed-Outline Floorplanning: Block-Position Enumeration and a New Method for Calculating Area Costs , 2008, IEEE Transactions on Computer-Aided Design of Integrated Circuits and Systems.

[42]  Xingming Sun,et al.  Effective and Efficient Global Context Verification for Image Copy Detection , 2017, IEEE Transactions on Information Forensics and Security.

[43]  Xingming Sun,et al.  Segmentation-Based Image Copy-Move Forgery Detection Scheme , 2015, IEEE Transactions on Information Forensics and Security.

[44]  Yao-Wen Chang,et al.  Voltage-Island Partitioning and Floorplanning Under Timing Constraints , 2009, IEEE Transactions on Computer-Aided Design of Integrated Circuits and Systems.

[45]  Amir Hossein Gandomi,et al.  Hybridizing harmony search algorithm with cuckoo search for global numerical optimization , 2014, Soft Computing.

[46]  Yu Xue,et al.  A self-adaptive artificial bee colony algorithm based on global best for global optimization , 2017, Soft Computing.

[47]  Yao-Wen Chang,et al.  Modern floorplanning based on B/sup */-tree and fast simulated annealing , 2006, IEEE Transactions on Computer-Aided Design of Integrated Circuits and Systems.