Abstract
This paper presents a novel method for implementing Factor Graphs in a SpiNNaker neural computing system. The SpiNNaker system provides resources for fine-grained parallelism, designed for implementing a distributed computing system. We present a framework which utilizes available SpiNNaker resources to implement a discrete Factor Graph: a powerful graphical model for probabilistic inference. Our framework allows mapping and routing a Factor Graph on the SpiNNaker hardware using SpiNNaker’s event-based communication system. An example application of the proposed framework in a real-world robotics scenario is given and the result shows that the framework can handle computation of 26.14 MFLOPS only in 30.5ms. We demonstrate that the framework easily extends for larger Factor Graph networks in a bigger SpiNNaker system, which makes it suitable for complex and challenging computational intelligence tasks.
This is a preview of subscription content, log in via an institution.
Buying options
Tax calculation will be finalised at checkout
Purchases are for personal use only
Learn about institutional subscriptionsPreview
Unable to display preview. Download preview PDF.
References
Kschischang, F.R., Frey, B.J., Loeliger, H.-A.: Factor Graphs and the Sum-Product Algorithm. IEEE Transactions on Information Theory 47(2), 498–519 (2001)
Frey, B.J.: Extending Factor Graphs so as to Unify Directed and Undirected Graph-ical Models. In: Proc. The 19th Conference on Uncertainty in Artificial Intelligence (2003)
Abbeel, P., Koller, D., Andrew, Y.N.: Learning Factor Graphs in Polynomial Time & Sample Complexity. In: Proc. the 21th Conference on Uncertainty in Artificial Intelligence, UAI 2005 (2005)
Khan, M., Lester, D., Plana, L.A., Rast, A., Jin, X., Painkras, E., Furber, S.B.: SpiNNaker: Mapping Neural Networks onto a Massively-Parallel Chip Multiprocessor. In: Proc. IEEE International Joint Conference on Neural Networks (IJCNN), pp. 2849–2856 (2008)
Plana, L.A., Bainbridge, J., Furber, S., Salisbury, S., Shi, Y., Wu, J.: An on-Chip and Inter-Chip Communications Network for the SpiNNaker Massively-Parallel Neural Net Simulator. In: Proc. 2nd ACM/IEEE NoCS, pp. 215–216. IEEE (2008)
Galluppi, F., Davies, S., Rast, A., Sharp, T., Plana, L.A., Furber, S.: A Hierarchical Configuration System for a Massively Parallel Neural Hardware Platform. In: Proc. 9th Conference of Computing Frontiers, pp. 183–192. ACM, New York (2012)
Sugiarto, I., Maier, P., Conradt, J.: Reasoning with discrete factor graph. In: Proc. International Conference on Robotics, Biomimetics, Intelligent Computational Systems 2013 (Robionetics), Yogyakarta, Indonesia (2013)
Sugiarto, I., Conradt, J.: Discrete belief propagation network using population coding and factor graph for kinematic control of a mobile robot. In: Proc. International Conference on Computational Intelligence and Cybernetics 2013 (Cyberneticscom 2013), Indonesia,
Denk, C., Llobet-Blandino, F., Gallupi, F., Plana, L., Furber, S., Conradt, J.: Real-Time In-terface Board for Closed-Loop Robotic Tasks on the SpiNNaker Neural Computing System. In: Proc. International Conference on Artificial Neural Networks (ICANN), Sofia, Bulgaria, pp. 467–474 (2013)
Lin, M., Lebedev, I., Wawrzynek, J.: High-Throughput Bayesian Computing Machine with Reconfigurable Hardware. In: Proc. The 18th Annual ACM/SIGDA International Symposium on Field Programmable Gate Arrays (FPGA 210), Monterey, USA, pp. 73–82 (2010)
Author information
Authors and Affiliations
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2014 Springer International Publishing Switzerland
About this paper
Cite this paper
Sugiarto, I., Conradt, J. (2014). Factor Graph Inference Engine on the SpiNNaker Neural Computing System. In: Wermter, S., et al. Artificial Neural Networks and Machine Learning – ICANN 2014. ICANN 2014. Lecture Notes in Computer Science, vol 8681. Springer, Cham. https://doi.org/10.1007/978-3-319-11179-7_21
Download citation
DOI: https://doi.org/10.1007/978-3-319-11179-7_21
Publisher Name: Springer, Cham
Print ISBN: 978-3-319-11178-0
Online ISBN: 978-3-319-11179-7
eBook Packages: Computer ScienceComputer Science (R0)