Neuromorphic models on a GPGPU cluster

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 integrate and fire model. This paper examines the feasibility of using a cluster of NVIDIA General Purpose Graphics Processing Units (GPGPUs) for accelerating a spiking neural network based character recognition network based on the Izhikevich and Hodgkin-Huxley models to enable such large scale systems. We utilized a 32 node cluster at NCSA containing an NVIDIA Tesla S1070 GPGPU on each node. Based on a thorough review of the literature, this is the first study examining the use of a cluster of GPGPUs for accelerating neuromorphic models. Our results show that the GPGPU can provide speedups of 24.6 and 177.0 times over a dual core 2.4 GHz AMD Opteron processor for the Izhikevich and Hodgkin-Huxley models respectively. Additionally, the MPI implementations of the models scaled almost linearly, with 16 GPGPUs providing throughputs of 14.1 and 15.9 times that of a single GPGPU for the Izhikevich and Hodgkin-Huxley models respectively. This indicates that clusters of GPGPUs are well suited for this application domain.