Abstract
The algorithms used for simulating biologically-inspired spiking neural networks (BIANN) often utilize functions which are computationally complex and have to model a large number of neurons - or even a much larger number of synapses in parallel. To use all available computing resources provided by a standard desktop PC is an opportunity to shorten the simulation time and extend the number of simulated neurons and their interconnections. OpenCL offers an open platform for heterogeneous computing to employ CPUs, GPUs, DSP or FPGAs in an uniform way. This paper introduces a handy simulation framework being sufficient to accelerate different kinds of neural networks with off-the-shelf hardware. To illustrate this, different large networks comprising a complex synaptic model in combination with a leaky Integrate-and-Fire neuron model are implemented as standard Matlab code and with OpenCL separately. In comparison to the Matlab model, OpenCL reaches a speedup of \(\backsim83\) on a quad-core processor and of \(\backsim1500\) on a GPU.
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References
Blas, A.D., Jagota, A., Hughey, R.: Optimizing neural networks on simd parallel computers. Parallel Computing 31(1), 97–115 (2005)
Bernhard, F., Keriven, R.: Spiking Neurons on GPUs, pp. 236–243. Springer, Heidelberg (2006)
Khan, M.M., Lester, D.R., Plana, L.A., Rast, A.D., Jin, X., Painkras, E., Furber, S.B.: Spinnaker: Mapping neural networks onto a massively-parallel chip multiprocessor. In: IJCNN, pp. 2849–2856 (2008)
Guzhva, A., Dolenko, S., Persiantsev, I.: Multifold acceleration of neural network computations using gpu. In: Alippi, C., Polycarpou, M., Panayiotou, C., Ellinas, G. (eds.) ICANN 2009. LNCS, vol. 5768, pp. 373–380. Springer, Heidelberg (2009)
Izhikevich, E.M.: Simple model of spiking neurons. IEEE Trans. Neural Networks, 1569–1572 (2003)
El-Laithy, K., Bogdan, M.: Synchrony state generation in artificial neural networks with stochastic synapses. In: Alippi, C., Polycarpou, M., Panayiotou, C., Ellinas, G. (eds.) ICANN 2009. LNCS, vol. 5768, pp. 181–190. Springer, Heidelberg (2009)
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Hoffmann, J., El-Laithy, K., Güttler, F., Bogdan, M. (2010). Simulating Biological-Inspired Spiking Neural Networks with OpenCL. In: Diamantaras, K., Duch, W., Iliadis, L.S. (eds) Artificial Neural Networks – ICANN 2010. ICANN 2010. Lecture Notes in Computer Science, vol 6352. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-15819-3_23
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DOI: https://doi.org/10.1007/978-3-642-15819-3_23
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