Abstract
This article presents an approach for mapping real-time applications based on particle filters (PFs) to heterogeneous reconfigurable systems, which typically consist of multiple FPGAs and CPUs. A method is proposed to adapt the number of particles dynamically and to utilise runtime reconfigurability of FPGAs for reduced power and energy consumption. A data compression scheme is employed to reduce communication overhead between FPGAs and CPUs. A mobile robot localisation and tracking application is developed to illustrate our approach. Experimental results show that the proposed adaptive PF can reduce up to 99% of computation time. Using runtime reconfiguration, we achieve a 25% to 34% reduction in idle power. A 1U system with four FPGAs is up to 169 times faster than a single-core CPU and 41 times faster than a 1U CPU server with 12 cores. It is also estimated to be 3 times faster than a system with four GPUs.
- Miodrag Bolic, Petar M. Djuric, and Sangjin Hong. 2005. Resampling algorithms and architectures for distributed particle filters. IEEE Transactions on Signal Processing 53, 7, 2442--2450. Google Scholar
Digital Library
- Miodrag Bolic, Sangjin Hong, and Petar M. Djuric. 2002. Performance and complexity analysis of adaptive particle filtering for tracking applications. In Proceedings of the Asilomar Conference on Signals, Systems, and Computers, Vol. 1. 853--857.Google Scholar
- Thomas C. P. Chau, Wayne Luk, Peter Y. K. Cheung, Alison Eele, and Jan Maciejowski. 2012. Adaptive sequential Monte Carlo approach for real-time applications. In Proceedings of the International Conference on Field Programmable Logic and Applications. 527--530.Google Scholar
Cross Ref
- Thomas C. P. Chau, Xinyu Niu, Alison Eele, Wayne Luk, Peter Y. K. Cheung, and Jan Maciejowski. 2013a. Heterogeneous reconfigurable system for adaptive particle filters in real-time applications. In Proceedings of the International Symposium on Applied Reconfigurable Computing. 1--12. Google Scholar
Digital Library
- Thomas C. P. Chau, James S. Targett, Marlon Wijeyasinghe, Wayne Luk, Peter Y. K. Cheung, Benjamin Cope, Alison Eele, and Jan M. Maciejowski. 2013b. Accelerating sequential Monte Carlo method for real-time air traffic Management. In Proceedings of the International Symposium on Highly Efficient Accelerators and Reconfigurable Technologies.Google Scholar
- Arnaud Doucet, Nando de Freitas, and Neil Gordon. 2001. Sequential Monte Carlo methods in practice. Springer.Google Scholar
- Alison Eele and Jan M. Maciejowski. 2011. Comparison of stochastic optimisation methods for control in air traffic management. In Proceedings of the IFAC World Congress.Google Scholar
- Dieter Fox. 2003. Adapting the sample size in particle filters through KLD-sampling. IEEE Transactions on Robotics 22, 12, 985--1003.Google Scholar
- Markus Happe, Enno Lübbers, and Marco Platzner. 2011. A self-adaptive heterogeneous multi-core architecture for embedded real-time video object tracking. Journal of Real-Time Image Processing 8, 1, 1--16. Google Scholar
Digital Library
- Daphne Koller and Raya Fratkina. 1998. Using learning for approximation in stochastic processes. In Proceedings of the International Conference on Machine Learning. 287--295. Google Scholar
Digital Library
- Zhibin Liu, Zongying Shi, Mingguo Zhao, and Wenli Xu. 2007. Mobile robots global localization using adaptive dynamic clustered particle filters. In Proceedings of the International Conference on Intelligent Robots and Systems. 1059--1064.Google Scholar
- Lifeng Miao, Jun Jason Zhang, Chaitali Chakrabarti, and Antonia Papandreou-Suppappola. 2011. Algorithm and parallel implementation of particle filtering and its use in waveform-agile sensing. Journal of Signal Processing Systems 65, 2, 211--227. Google Scholar
Digital Library
- Lyudmila Mihaylova, Rene Boel, and Andreas Hegyi. 2007. Freeway traffic estimation within particle filtering framework. Automatica 43, 2, 290--300. Google Scholar
Digital Library
- Michael Montemerlo, Sebastian Thrun, and William Whittaker. 2002. Conditional particle filters for simultaneous mobile robot localization and people-tracking. In Proceedings of the International Conference on Robotics and Automation. 695--701.Google Scholar
Cross Ref
- Sang-Hyuk Park, Young-Joong Kim, and Myo-Taeg Lim. 2010. Novel adaptive particle filter using adjusted variance and its application. International Journal on Control, Automation, and Systems 8, 4, 801--807.Google Scholar
Cross Ref
- Jaco Vermaak, Christophe Andrieu, Arnaud Doucet, and Simon John Godsill. 2002. Particle methods for Bayesian modeling and enhancement of speech signals. IEEE Transactions on Speech and Audio Processing 10, 3, 173--185.Google Scholar
Cross Ref
Index Terms
Mapping Adaptive Particle Filters to Heterogeneous Reconfigurable Systems
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