Abstract
This paper presents a heterogeneous reconfigurable system for real-time applications applying particle filters. The system consists of an FPGA and a multi-threaded CPU. We propose a method to adapt the number of particles dynamically and utilise the run-time reconfigurability of the FPGA for reduced power and energy consumption. An application is developed which involves simultaneous mobile robot localisation and people tracking. It shows that the proposed adaptive particle filter can reduce up to 99% of computation time. Using run-time reconfiguration, we achieve 34% reduction in idle power and save 26-34% of system energy. Our proposed system is up to 7.39 times faster and 3.65 times more energy efficient than the Intel Xeon X5650 CPU with 12 threads, and 1.3 times faster and 2.13 times more energy efficient than an NVIDIA Tesla C2070 GPU.
This is a preview of subscription content, log in via an institution to check access.
Access this chapter
Tax calculation will be finalised at checkout
Purchases are for personal use only
Preview
Unable to display preview. Download preview PDF.
Similar content being viewed by others
References
Happe, M., et al.: A self-adaptive heterogeneous multi-core architecture for embedded real-time video object tracking. Journal Real-Time Image Processing, 1–16 (2011)
Montemerlo, M., et al.: Conditional particle filters for simultaneous mobile robot localization and people-tracking. In: Proc. IEEE Int. Conf. Robotics and Automation, pp. 695–701 (2002)
Vermaak, J., et al.: Particle methods for bayesian modeling and enhancement of speech signals. IEEE Trans. Speech and Audio Processing 10(3), 173–185 (2002)
Eele, A., Maciejowski, J.: Comparison of stochastic optimisation methods for control in air traffic management. In: Proc. IFAC World Congress (2011)
Doucet, A., et al.: Sequential Monte Carlo methods in practice. Springer (2001)
Bolic, M., et al.: Resampling algorithms and architectures for distributed particle filters. IEEE Trans. Signal Processing 53(7), 2442–2450 (2005)
Koller, D., et al.: Using learning for approximation in stochastic processes. In: Proc. Int. Conf. Machine Learning, pp. 287–295 (1998)
Fox, D.: Adapting the sample size in particle filters through kld-sampling. Int. Trans. Robotics 22(12), 985–1003 (2003)
Park, S.-H., et al.: Novel adaptive particle filter using adjusted variance and its application. Int. Journal Control, Automation, and Systems 8(4), 801–807 (2010)
Bolic, M., et al.: Performance and complexity analysis of adaptive particle filtering for tracking applications. In: Proc. Asilomar Conf. Signals, Systems and Computers, vol. 1, pp. 853–857 (2002)
Chau, T.C., et al.: Adaptive sequential monte carlo approach for real-time applications. In: Proc. Int. Conf. Field Programmable Logic and Applications, pp. 527–530 (2012)
Liu, Z., et al.: Mobile robots global localization using adaptive dynamic clustered particle filters. In: IEEE/RSJ Int. Conf. Intelligent Robots and Systems, pp. 1059–1064 (2007)
Author information
Authors and Affiliations
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2013 Springer-Verlag Berlin Heidelberg
About this paper
Cite this paper
Chau, T.C.P., Niu, X., Eele, A., Luk, W., Cheung, P.Y.K., Maciejowski, J. (2013). Heterogeneous Reconfigurable System for Adaptive Particle Filters in Real-Time Applications. In: Brisk, P., de Figueiredo Coutinho, J.G., Diniz, P.C. (eds) Reconfigurable Computing: Architectures, Tools and Applications. ARC 2013. Lecture Notes in Computer Science, vol 7806. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-36812-7_1
Download citation
DOI: https://doi.org/10.1007/978-3-642-36812-7_1
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-642-36811-0
Online ISBN: 978-3-642-36812-7
eBook Packages: Computer ScienceComputer Science (R0)