Abstract
Various custom hardware solutions for simulation of neural circuitry have recently been developed, each focusing on particular aspects such as low power operation, high computation speed, or biologically detailed simulations. The SpiNNaker computing system has been developed to simulate large spiking neural circuits in real-time in a network of parallel operating microcontrollers, interconnected by a high-speed asynchronous interface. A potential application area is autonomous mobile robotics, which would tremendously benefit from on-board simulations of networks of tens of thousands of spiking neurons in real-time. Currently, the SpiNNaker hardware circuit boards provide a single Ethernet interface for booting, debug, and input and output of data, which results in a severe bottleneck for sensory perception and motor control signals. This paper describes a small and flexible real-time I/O-hardware interface to connect external devices such as robotic sensors and actuators directly to the fast asynchronous internal communication infrastructure of the SpiNNaker neural computing system. We evaluate performance in terms of package throughput and present a simple application demonstration of a closed loop mobile robot interpreting visual data to approach the most salient stimulus.
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.
References
Pfeil, T., Grübl, A., Jeltsch, S., Müller, E., Müller, P., Petrovici, M.A., Schmuker, M., Brüderle, D., Schemmel, J., Meier, K.: Six Networks on a Universal Neuromorphic Computing Substrate. Frontiers in Neuroscience 7 (2013)
Choudhary, S., Sloan, S., Fok, S., Neckar, A., Trautmann, E., Gao, P., Stewart, T., Eliasmith, C., Boahen, K.: Silicon Neurons That Compute. In: Villa, A.E.P., Duch, W., Érdi, P., Masulli, F., Palm, G. (eds.) ICANN 2012, Part I. LNCS, vol. 7552, pp. 121–128. Springer, Heidelberg (2012)
Badoni, D., Giulioni, M., Dante, V., Del Giudice, P.: An Avlsi Recurrent Network of Spiking Neurons with Reconfigurable and Plastic Synapses. In: IEEE (ISCAS), pp. 1227–1230 (2006)
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: IEEE International Joint Conference on Neural Networks (IJCNN), pp. 2849–2856. IEEE (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: 2nd ACM/IEEE NoCS, pp. 215–216. IEEE (2008)
Galluppi, F., Davies, S., Rast, A., Sharp, T., Plana, L.A., Furber, S.: A Hierachical Configuration System for a Massively Parallel Neural Hardware Platform. In: Proceedings of the 9th conference on Computing Frontiers, pp. 183–192. ACM (2012)
Galluppi, F., Davies, S., Furber, S., Stewart, T., Eliasmith, C.: Real Time on-Chip Implementation of Dynamical Systems with Spiking Neurons. In: IJCNN, pp. 1–8. IEEE (2012)
Oster, M., Douglas, R., Liu, S.-C.: Computation with Spikes in a Winner-Take-All Network. Neural Computation 21, 2437–2465 (2009)
Lichtsteiner, P., Posch, C., Delbruck, T.: A 128× 128 120dB 15ms Latency Asynchronous Temporal Contrast Vision Sensor. IEEE Solid-State Circuits 43, 566–576 (2008)
Conradt, J., Berner, R., Cook, M., Delbruck, T.: An Embedded AER Dynamic Vision Sensor for Low-Latency Pole Balancing. In: IEEE ECV, pp. 780–785. IEEE (2009)
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
Denk, C., Llobet-Blandino, F., Galluppi, F., Plana, L.A., Furber, S., Conradt, J. (2013). Real-Time Interface Board for Closed-Loop Robotic Tasks on the SpiNNaker Neural Computing System. In: Mladenov, V., Koprinkova-Hristova, P., Palm, G., Villa, A.E.P., Appollini, B., Kasabov, N. (eds) Artificial Neural Networks and Machine Learning – ICANN 2013. ICANN 2013. Lecture Notes in Computer Science, vol 8131. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-40728-4_59
Download citation
DOI: https://doi.org/10.1007/978-3-642-40728-4_59
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-642-40727-7
Online ISBN: 978-3-642-40728-4
eBook Packages: Computer ScienceComputer Science (R0)