Acceleration of spiking neural network on general purpose graphics processors

There is currently a strong push in the research community to develop biological scale implementations of neuron based vision models. Systems at this scale are computationally demanding and have generally utilized more accurate neuron models, such as the Izhikevich and Hodgkin-Huxley models, in favor of the more popular integrate and fire model. This thesis examines the feasibility of using GPGPUs for accelerating a spiking neural network based character recognition network to enable large scale neural systems. Two versions of the network utilizing the Izhikevich and Hodgkin-Huxley models are implemented. Three NVIDIA GPGPU platforms and one GPGPU cluster were examined. These include the GeForce 9800 GX2, the Tesla C1060, the Tesla S1070 platforms, and the 32-node Tesla S1070 GPGPU cluster. Our results show that the GPGPUs can provide significant speedups over conventional processors. In particular, the fastest GPGPU utilized, the Tesla S1070, provided speedups of 5.6 and 84.4 time over highly optimized implementations on the fastest CPU tested, a quad core 2.67 GHz Xeon processor, for the Izhikevich and Hodgkin Huxley models respectively. The CPU implementation utilized all four cores and the vector data parallelism offered by the processor. The results indicate that GPGPUs are well suited for this application domain. A large portion of the results presented in this thesis have been published in the April 2010 issue of Applied Optics [1]