An efficient GPU-based parallel tabu search algorithm for hardware/software co-design

De Michell G, Gupta R K. Hardware/software co-design. Proceedings of the IEEE, 1997, 85(3): 349–365

Wolf W. A decade of hardware/software co-design. Computer, 2003, 6(4): 38–43

Teich J. Hardware/software co-design: the past, the present, and predicting the future. Proceedings of the IEEE, 2012, 100: 1411–1430

Ouyang A, Peng X, Liu J, Sallam A. Hardware/software partitioning for heterogeneous MPSoC considering communication overhead. International Journal of Parallel Programming, 2017, 45(4): 899–922

Hou N, Yan X, He F. A survey on partitioning models, solution algorithms and algorithm parallelization for hardware/software co-design. Design Automation for Embedded Systems, 2019, 23(1–2): 57–77

Shi W, Wu J, Lam S, Srikanthan T. Algorithms for bi-objective multiple-choice hardware/software partitioning. Computers & Electrical Engineering, 2016, 50: 127–142

Dick R P, Rhodes D L, Wolf W. TGFF: task graphs for free. In: Proceedings of the 6th International Workshop on Hardware/Software Co-design. 1998, 97–101

Henkel J, Ernst R. An approach to automated hardware/software partitioning using a flexible granularity that is driven by high-level estimation techniques. IEEE Transactions on Very Large Scale Integration Systems, 2001, 9(2): 273–289

Jiang G, Wu J, Lam S, Srikanthan T, Sun J. Algorithmic aspects of graph reduction for hardware/software partitioning. The Journal of Supercomputing, 2015, 71(6): 2251–2274

Arató P, Juhász S, Mann Z, Orbán A, Papp D. Hardware-software partitioning in embedded system design. In: Proceedings of IEEE International Symposium on Intelligent Signal Processing. 2003, 197–202

Arató P, Mann Z, Orbán A. Algorithmic aspects of hardware/software partitioning. ACM Transactions on Design Automation of Electronic Systems, 2005, 10(1): 136–156

Zhou Y, He F, Hou N, Qiu Y. Parallel ant colony optimization on multi-core SIMD CPUs. Future Generation Computer Systems, 2018, 79(2): 473–487

Wang R, Hung W, Yang G, Song X. Uncertainty model for configurable hardware/software and resource partitioning. IEEE Transactions on Computers, 2016, 66(10): 3217–3223

Yan X, He F, Hou N, Ai H. An efficient particle swarm optimization for large scale hardware/software co-design system. International Journal of Cooperative Information Systems, 2018, 27(1): 1741001

Trindade A, Cordeiro L. Applying SMT-based verification to hardware/software partitioning in embedded systems. Design Automation for Embedded Systems, 2016, 20(1): 1–19

Li H, He F, Yan X. IBEA-SVM: an indicator-based evolutionary algorithm based on pre-selection with classification guided by SVM. Applied Mathematics—A Journal of Chinese Universities, 2019, 34(1): 1–26

Luo J, He F, Yong J. An efficient and robust bat algorithm with fusion of opposition-based learning and whale optimization algorithm. Intelligent Data Analysis, 2020, 24(3): 500–519

Yong J, He F, Li H, Zhou W. A novel bat algorithm based on cross boundary learning and uniform explosion strategy. Applied Mathematics—A Journal of Chinese Universities, 2019, DOI: https://doi.org/10.1007/s11766-019-3714-1

Gupta R, Micheli G. Hardware-software co-synthesis for digital systems. IEEE Design & Test of Computers, 1993, 10(3): 29–41

Ernst R, Henkel J, Benner T. Hardware — software co-synthesis for microcontrollers. IEEE Design & Test of Computers, 1993, 10(4): 64–75

Dick R, Jha N. MOGAC: a multi-objective genetic algorithm for hardware-software co-synthesis of distributed embedded systems. IEEE Transactions on Computer-Aided Design of Integrated Circuits and Systems, 1998, 17(10): 920–935

Wang G, Gong W, Kastner R. Application partitioning on programmable platforms using the ant colony optimization. Journal of Embedded Computing, 2006, 2(1): 119–136

Ferrandi F, Lanzi P, Pilato C, Sciuto D, Tumeo A. Ant colony optimization for mapping, scheduling and placing in reconfigurable systems. In: Proceedings of IEEE NASA/ESA Conference on Adaptive Hardware and Systems. 2013, 47–54

Koudil M, Benatchba K, Tarabet A. Using artificial bees to solve partitioning and scheduling problems in co-design. Applied Mathematics and Computation, 2007, 186(2): 1710–1722

Abdelhalim M, Habib S. An integrated high-level hardware/software partitioning methodology. Design Automation for Embedded Systems, 2011, 15(1): 19–50

Garg K, Aung Y, Lam S. Knapsim-run-time efficient hardwaresoftware partitioning technique for FPGAs. In: Proceedings of the 28th IEEE International Conference on System-on-Chip. 2015, 64–69

Zhang Y, Luo W, Zhang Z, Li B, Wang X. A hardware/software partitioning algorithm based on artificial immune principles. Applied Soft Computing, 2008, 8(1): 383–391

Jiang Y, Zhang H, Jiao X, Song X, Hung W, Gu M, Sun J. Uncertain model and algorithm for hardware/software partitioning. In: Proceedings of IEEE Computer Society Annual Symposium on VLSI. 2012, 243–248

Li G, Feng J, Wang C, Wang J. Hardware/software partitioning algorithm based on the combination of genetic algorithm and tabu search. Engineering Review, 2014, 34(2): 151–160

Yan X, He F, Chen Y. A novel hardware/software partitioning method based on position disturbed particle swarm optimization with invasive weed optimization. Journal of Computer Science and Technology, 2017, 32(2): 340–355

Kalavade A, Subrahmanyam P. Hardware/software partitioning for multi-function systems. IEEE Transactions on Computer-Aided Design of Integrated Circuits and Systems, 1998, 17(9): 819–837

Govil N, Shrestha R, Chowdhury S. PGMA: an algorithmic approach for multi-objective hardware software partitioning. Microprocessors and Microsystems, 2017, 54: 83–96

Farahani A, Kamal M, Salmani-Jelodar M. Parallel genetic algorithm based HW/SW partitioning. In: Proceedings of International Symposium on Parallel Computing in Electrical Engineering. 2006, 337–342

Wu Y, Zhang H, Yang H. Research on parallel HW/SW partitioning based on hybrid PSO algorithm. In: Proceedings of International Conference on Algorithms and Architectures for Parallel Processing. 2009, 449–459

Pan Y, He F, Yu H, Li H. Learning adaptive trust strength with user roles of truster and trustee for trust-aware recommender systems. Applied Intelligence, 2019, DOI: https://doi.org/10.1007/s10489-019-01542-0

Lv X, He F, Cai W, Cheng Y. An optimized RGA supporting selective undo for collaborative text editing systems. Journal of Parallel and Distributed Computing, 2019, 132: 310–330

Li K, He F, Yu H. Robust visual tracking based on convolutional features with illumination and occlusion handing. Journal of Computer Science and Technology, 2018, 33(1): 223–236

Yu H, He F, Pan Y. A novel region-based active contour model via local patch similarity measure for image segmentation. Multimedia Tools and Applications, 2018, 77(18): 24097–24119

Van Luong T, Melab N, Talbi E. GPU computing for parallel local search meta-heuristic algorithms. IEEE Transactions on Computers, 2013, 62(1): 173–185

Zhou Y, He F, Qiu Y. Dynamic strategy based parallel ant colony optimization on GPUs for TSPs. Science China Information Sciences, 2017, 60(6): 068102.

Zhu W, Curry J, Marquez A. SIMD tabu search for the quadratic assignment problem with graphics hardware acceleration. International Journal of Production Research, 2010, 48(4): 1035–1047

Wei K, Sun X, Chu H, Wu C. Reconstructing permutation table to improve the tabu search for the PFSP on GPU. The Journal of Supercomputing, 2017, 73(11): 4711–4738

Bukata L, š˙cha P, Hanzálek Z. Solving the resource constrained project scheduling problem using the parallel tabu search designed for the CUDA platform. Journal of Parallel and Distributed Computing, 2015, 77: 58–68

Hou N, He F, Chen Y, Zhou Y. An adaptive neighborhood taboo search on GPU for hardware/software co-design. In: Proceedings of the 20th International Conference on Computer Supported Cooperative Work in Design. 2016, 239–244

Hou N, He F, Zhou Y, Ai H. A GPU-based tabu search for very large hardware/software partitioning with limited resource usage. Journal of Advanced Mechanical Design, Systems, and Manufacturing, 2017, 11(5): JAMDSM0060

Wu J, Srikanthan T, Chen G. Algorithmic aspects of hardware/software partitioning: 1D search algorithms. IEEE Transactions on Computers, 2010, 59(4): 532–544

Wu J, Wang P, Lam S, Srikanthan T. Efficient heuristic and tabu search for hardware/software partitioning. The Journal of Supercomputing, 2013, 66(1): 118–134

Chen Z, Wu J, Song G, Chen J. Noderank: an efficient algorithm for hardware/software partitioning. Chinese Journal of Computers, 2013, 36(10): 2033–2040

Quan H, Zhang T, Liu Q, Guo J, Wang X, Hu R. Comments on algorithmic aspects of hardware/software partitioning: 1D search algorithms. IEEE Transactions on Computers, 2014, 4(63): 1055–1056

Billeter M, Olsson O, Assarsson U. Efficient stream compaction on wide SIMD many-core architectures. In: Proceedings of the Conference on High Performance Graphics. 2009, 159–166

Wilt N. The Cuda Handbook: a Comprehensive Guide to GPU Programming. Pearson Education, 2013

Gupta K, Stuart J, Owens J. A study of persistent threads style GPU programming for GPGPU workloads. In: Proceedings of Innovative Parallel Computing. 2012, 1–14

Guthaus M, Ringenberg J, Ernst D, Austin T, Mudge T, Brown R. MiBench: a free, commercially representative embedded benchmark suite. In: Proceedings of IEEE International Workshop on Workload Characterization. 2001, 3–14

Pan Y, He F, Yu H. A novel enhanced collaborative autoencoder with knowledge distillation for top-n recommender systems. Neurocomputing, 2019, 332: 137–148

Zhang S, He F, Ren W, Yao J. Joint learning of image detail and transmission map for single image dehazing. The Visual Compute, 2018, DOI: https://doi.org/10.1007/s00371-018-1612-9

Chen X, He F, Yu H. A matting method based on full feature coverage. Multimedia Tools and Applications, 2019, 78(9): 11173–11201

Yu H, He F, Pan Y. A novel segmentation model for medical images with intensity inhomogeneity based on adaptive perturbation. Multimedia Tools and Applications, 2019, 78(9), 11779–11798

Fang F, Yi M, Feng, H, Hu S, Xiao C. Narrative collage of I mage collections by scene graph recombination. IEEE Transactions on Visualization and Computer Graphics, 2018, 24(9): 2559–2572

Wu Y, He F, Zhang D, Li X. Service-oriented feature-based data exchange for cloud-based design and manufacturing. IEEE Transactions on Services Computing, 2018, 11(2): 341–353

Pan Y, He F, Yu H. A correlative denoising autoencoder to model social influence for top-N recommender system. Frontiers of Computer Science, 2020, 14(3): 143301

Lv X, He F, Yan X, Wu Y, Cheng Y. Integrating selective undo of feature-based modeling operations for real-time collaborative CAD systems. Future Generation Computer Systems, 2019, 100: 473–497

Li K, He F, Yu H, Chen X. A parallel and robust object tracking approach synthesizing adaptive Bayesian learning and improved incremental subspace learning. Frontiers of Computer Science, 2019, 13(5): 1116–1135

Yang L, Yan Q, Fu Y, Xiao C. Surface reconstruction via fusing sparse-sequence of depth images. IEEE Transactions on Visualization and Computer Graphics, 2018, 24 (2): 1190–1203