A General Framework for Accelerating Swarm Intelligence Algorithms on FPGAs, GPUs and Multi-Core CPUs
Open accessAbstract
Swarm intelligence algorithms (SIAs) have demonstrated excellent performance when solving optimization problems including many real-world problems. However, because of their expensive computational cost for some complex problems, SIAs need to be accelerated effectively for better performance. This paper presents a high-performance general framework to accelerate SIAs (FASI). Different from the previous work which accelerates SIAs through enhancing the parallelization only, FASI considers both the memory architectures of hardware platforms and the dataflow of SIAs, and it reschedules the framework of SIAs as a converged dataflow to improve the memory access efficiency. FASI achieves higher acceleration ability by matching the algorithm framework to the hardware architectures. We also design deep optimized structures of the parallelization and convergence of FASI based on the characteristics of specific hardware platforms. We take the quantum behaved particle swarm optimization algorithm as a case to evaluate FASI. The results show that FASI improves the throughput of SIAs and provides better performance through optimizing the hardware implementations. In our experiments, FASI achieves a maximum of 290.7 Mb/s throughput which is higher than several existing systems, and FASI on FPGAs achieves a better speedup than that on GPUs and multi-core CPUs. FASI is up to 123 times and not less than 1.45 times faster in terms of optimization time on Xilinx Kintex Ultrascale xcku040 when compares to Intel Core i7-6700 CPU/NVIDIA GTX1080 GPU. Finally, we compare the differences of deploying FASI on hardware platforms and provide some guidelines for promoting the acceleration performance according to the hardware architectures.
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Full pipelined FASI in FPGA allows the map dimension, map particle, combiner and reducer global to be pipelined together for higher throughput.
“In addition, full pipelined FASI in FPGA, where the reductions and combinations are pipelined together with the threads, as shown in Figure 13, allows the map dimension, map particle, combiner and reducer global to be pipelined together for higher throughput.”
From Embedded Intelligence on FPGA: Survey, Applications and Challenges (2021) · cites this paper as [50]
Cites
Particle swarm optimization: An overview
Poli, R., Kennedy, J., & Blackwell, T. (2007) · Swarm Intelligence · doi:10.1007/s11721-007-0002-0 · Cited by 21,101 · see citing works
Ant colony system: a cooperative learning approach to the traveling salesman problem
Dorigo, M., & Gambardella, L. M. (1997) · IEEE Transactions on Evolutionary Computation · doi:10.1109/4235.585892 · Cited by 8,081 · see citing works
Ant colony optimization theory: A survey
Dorigo, M., & Blum, C. (2005) · Theoretical Computer Science · doi:10.1016/j.tcs.2005.05.020 · Cited by 2,299 · see citing works
Fundamentals of Computational Swarm Intelligence
Engelbrecht, A. P. (2005) · Medical Entomology and Zoology · Cited by 1,879 · see citing works
Defining a Standard for Particle Swarm Optimization
Bratton, D. J., & Kennedy, J. (2007) · IEEE Swarm Intelligence Symposium · doi:10.1109/sis.2007.368035 · Cited by 1,356 · see citing works
Particle swarm optimization with particles having quantum behavior
Sun, J., Feng, B., & Xu, W. (2004) · IEEE Congress on Evolutionary Computation (CEC) · doi:10.1109/cec.2004.1330875 · Cited by 1,315 · see citing works
A global search strategy of quantum-behaved particle swarm optimization
Sun, J. J., Xu, W., & Feng, B. (2005) · IEEE Conference on Cybernetics and Intelligent Systems · doi:10.1109/iccis.2004.1460396 · Cited by 716 · see citing works
Accelerating CUDA graph algorithms at maximum warp
Hong, S., Kim, S. K., Oguntebi, T., et al. (2011) · doi:10.1145/1941553.1941590 · Cited by 341 · see citing works
1.26 - Particle Swarm Optimization
Marini, F., & Walczak, B. (2020) · IRIS Research product catalog (Sapienza University of Rome) · doi:10.1016/b978-0-12-409547-2.14581-0 · Cited by 284 · see citing works
A novel two-stage hybrid swarm intelligence optimization algorithm and application
Deng, W., Chen, R., Bing, H., Liu, Y., Yin, L., & Guo, J. (2012) · Soft Computing · doi:10.1007/s00500-012-0855-z · Cited by 186 · see citing works
Solving the multi-objective path planning problem in mobile robotics with a firefly-based approach
Hidalgo‐Paniagua, A., Vega-Rodríguez, M. A., Ferruz, J., et al. (2015) · Soft Computing · doi:10.1007/s00500-015-1825-z · Cited by 134 · see citing works
Tan, Y. (2015) · Springer eBooks · doi:10.1007/978-3-662-46353-6 · Cited by 110 · see citing works
Muslim, F. B., Ma, L., Roozmeh, M., et al. (2017) · IEEE Access · doi:10.1109/access.2017.2671881 · Cited by 94 · see citing works
A Survey on GPU-Based Implementation of Swarm Intelligence Algorithms
Tan, Y., & Ding, K. (2015) · IEEE Transactions on Cybernetics · doi:10.1109/tcyb.2015.2460261 · Cited by 93 · see citing works
Energy-Efficient Hierarchical Routing for Wireless Sensor Networks: A Swarm Intelligence Approach
Mann, P. S., & Singh, S. (2016) · Wireless Personal Communications · doi:10.1007/s11277-016-3577-1 · Cited by 75 · see citing works
Hardware/software co-design for particle swarm optimization algorithm
Li, S., Hsu, C.-C. J., Wong, C., et al. (2010) · Information Sciences · doi:10.1016/j.ins.2010.07.017 · Cited by 49 · see citing works
A survey on hardware‐aware and heterogeneous computing on multicore processors and accelerators
Buchty, R., Heuveline, V., Karl, W., & Weiß, J.-P. (2011) · Concurrency and Computation Practice and Experience · doi:10.1002/cpe.1904 · Cited by 44 · see citing works
Polat, Ö., & Yıldırım, T. (2009) · Digital Signal Processing · doi:10.1016/j.dsp.2009.10.013 · Cited by 40 · see citing works
A hardware accelerator for Particle Swarm Optimization
Calazan, R. M., Nedjah, N., & Mourelle, L. D. M. (2013) · Applied Soft Computing · doi:10.1016/j.asoc.2012.12.034 · Cited by 37 · see citing works
Using simplified swarm optimization on path planning for intelligent mobile robot
Ever, Y. K. (2017) · Procedia Computer Science · doi:10.1016/j.procs.2017.11.213 · Cited by 33 · see citing works
Multi-swarm parallel PSO: Hardware implementation
Tewolde, G., Hanna, D. M., & Haskell, R. E. (2009) · IEEE Swarm Intelligence Symposium · doi:10.1109/sis.2009.4937845 · Cited by 30 · see citing works
Muñoz, D. M., Llanos, C. H., Coelho, L. D. S., et al. (2010) · doi:10.1109/bicta.2010.5645256 · Cited by 28 · see citing works
Hardware Architecture for Particle Swarm Optimization Using Floating-Point Arithmetic
Muñoz, D. M., Llanos, C. H., Coelho, L. D. S., et al. (2009) · International Conference on Intelligent Systems Design and Applications (ISDA) · doi:10.1109/isda.2009.107 · Cited by 27 · see citing works
Valdez, F., Melín, P., & Castillo, O. (2012) · Studies in computational intelligence · doi:10.1007/978-3-642-33021-6_25 · Cited by 21 · see citing works
Simultaneous Perturbation Particle Swarm Optimization Using FPGA
Maeda, Y., & Matsushita, N. (2007) · IEEE International Conference on Neural Networks/IEEE ... International Conference on Neural Networks · doi:10.1109/ijcnn.2007.4371384 · Cited by 21 · see citing works
Efficient parallel implementation of three‐point viterbi decoding algorithm on CPU, GPU, and FPGA
Li, R., Dou, Y., & Zou, D. (2013) · Concurrency and Computation Practice and Experience · doi:10.1002/cpe.3093 · Cited by 20 · see citing works
Parallel Genetic Algorithms on Multiple FPGAs
Guo, L., Funie, A.-I., Thomas, D. B., et al. (2016) · ACM SIGARCH Computer Architecture News · doi:10.1145/2927964.2927980 · Cited by 20 · see citing works
Souza, D. L., Monteiro, G. D., Martins, T., et al. (2011) · Genetic and Evolutionary Computation Conference Companion (GECCO Companion) · doi:10.1145/2001858.2002114 · Cited by 19 · see citing works
A modular and efficient hardware architecture for particle swarm optimization algorithm
Tewolde, G., Hanna, D. M., & Haskell, R. E. (2012) · Microprocessors and Microsystems · doi:10.1016/j.micpro.2012.02.001 · Cited by 17 · see citing works
A GPU accelerated PSO with application to Economic Dispatch problem
Papadakis, S. E., & Bakrtzis, A. G. (2011) · International Conference on Intelligent System Applications to Power Systems · doi:10.1109/isap.2011.6082162 · Cited by 16 · see citing works
Li, G., Liu, Q., Yang, Y., et al. (2017) · Chinese Control Conference (CCC) · doi:10.23919/chicc.2017.8027746 · Cited by 16 · see citing works
Population-Based MCMC on Multi-Core CPUs, GPUs and FPGAs
Mingas, G., & Bouganis, C.-S. (2015) · IEEE Transactions on Computers · doi:10.1109/tc.2015.2439256 · Cited by 15 · see citing works
An FPGA-Based Hardware Accelerator for Energy-Efficient Bitmap Index Creation
Nguyen, X.-T., Hoang, T.-T., Nguyen, H.-T., et al. (2018) · IEEE Access · doi:10.1109/access.2018.2816039 · Cited by 15 · see citing works
Acceleration by Inline Cache for Memory-Intensive Algorithms on FPGA via High-Level Synthesis
Ma, L., Lavagno, L., Lazarescu, M. T., et al. (2017) · IEEE Access · doi:10.1109/access.2017.2750923 · Cited by 13 · see citing works
FPGA implementation of improved ant colony optimization algorithm for path planning
Hsu, C.-C. J., Wang, W., Chien, Y.-H., et al. (2016) · IEEE Congress on Evolutionary Computation (CEC) · doi:10.1109/cec.2016.7744364 · Cited by 12 · see citing works
FPGA realization of Particle Swarm Optimization algorithm using floating point arithmetic
Rathod, A., & Thakker, R. A. (2014) · doi:10.1109/ichpca.2014.7045338 · Cited by 10 · see citing works
A High-Throughput FPGA Architecture for Joint Source and Channel Decoding
Brejza, M. F., Maunder, R. G., Al‐Hashimi, B. M., et al. (2016) · IEEE Access · doi:10.1109/access.2016.2633441 · Cited by 10 · see citing works
Chai, Z., Sun, J. J., Cai, R., et al. (2009) · International Conference on Computer Sciences and Convergence Information Technology · doi:10.1109/iccit.2009.21 · Cited by 8 · see citing works
A review on swarm intelligence based routing algorithms in mobile adhoc network
Srivastava, N., & Raghav, P. (2017) · International Conference on Computing, Communication and Networking Technologies (ICCCNT) · doi:10.1109/icccnt.2017.8203988 · Cited by 7 · see citing works
Duan, S. M., Mao, J., Li, J. L., et al. (2015) · doi:10.1142/9789814740104_0039 · Cited by 4 · see citing works
Design of very deep pipelined multipliers for FPGAs
Panato, A., Silva, S., Wagner, F., et al. (2004) · Design, Automation, and Test in Europe · doi:10.1109/date.2004.1269200 · Cited by 4 · see citing works
Yan, S., Wang, W., Wu, S., et al. (2017) · Chinese Control and Decision Conference (CCDC) · doi:10.1109/ccdc.2017.7978915 · Cited by 3 · see citing works
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Embedded Intelligence on FPGA: Survey, Applications and Challenges
Seng, K. P., Lee, P. J., & Ang, L. M. (2021) · Electronics · doi:10.3390/electronics10080895 · Cited by 105 · see citing works
“Figure 12 shows the rescheduled dataflow of quantum-behaved particle swarm optimization (QPSO).”
BackgroundMethodInfluential
- Authors
- Li, D., Huang, L., Wang, K., Pang, W., Zhou, Y., & Zhang, R.
- Affiliations
- Jilin University (CN)
- University of Aberdeen (GB)
- Venue
- IEEE Access
- Published
- 2018
- Volume
- 6
- Pages
- 72327-72344
- ISSN
- 2169-3536 (ISSN-L)
- OpenAlex
- W2901663594
- Cited by
- 11
- References
- 42
- Citations by year
- 2025: 2 · 2023: 1 · 2022: 1 · 2021: 4 · 2020: 2 · 2019: 1
- FWCI
- 0.98
- Type
- journal article
- Language
- en
- Open access
- gold
- doi.org
- Locations
- IEEE Access (publishedVersion)
- Aberdeen University Research Archive (Aberdeen University) (submittedVersion)
- DOAJ (DOAJ: Directory of Open Access Journals) (submittedVersion, cc-by-sa)
- Funders
- National Natural Science Foundation of China (61472159, 61572227, 61772227)
- Development Project of Jilin Province of China (20160204022GX, 20170101006JC, 20170203002GX, 2017C030-1, 2017C033)
- Premier-Discipline Enhancement Scheme through Zhuhai Government
- Premier Key-Discipline Enhancement Scheme through the Guangdong Government Funds
- Jilin Provincial Key Laboratory of Big Data Intelligent Computing (20180622002JC)
- Topics
- Physical Sciences › Computer Science › Artificial Intelligence › Metaheuristic Optimization Algorithms Research
- Physical Sciences › Computer Science › Hardware and Architecture › Parallel Computing and Optimization Techniques
- Physical Sciences › Computer Science › Hardware and Architecture › Embedded Systems Design Techniques
- Keywords
- swarm intelligence algorithms, hardware acceleration, FPGA, graphics processing units, multi-core processors, quantum-behaved particle swarm optimization, memory access efficiency, parallelization, throughput
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