Hybrid CPU-GPU Model Based Simulation of Spiking Neural Networks Using a Look-up Table
Sreenivasa.N1, S. Balaji2
1Sreenivasa N, Research Scholar, Department of Computer Science & Engineering, Jain University, Nitte Meenakshi Institute of Technology, Yelahanka Bengaluru, India.
2S. Balaji, Research Scholar, Department of Computer Science & Engineering, Jain University, Nitte Meenakshi Institute of Technology, Yelahanka Bengaluru, India.
Manuscript received on 04 April 2019 | Revised Manuscript received on 11 April 2019 | Manuscript Published on 26 April 2019 | PP: 328-333 | Volume-8 Issue-6S April 2019 | Retrieval Number: F61240486S19/19©BEIESP
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© The Authors. Blue Eyes Intelligence Engineering and Sciences Publication (BEIESP). This is an open-access article under the CC-BY-NC-ND license (http://creativecommons.org/licenses/by-nc-nd/4.0/)
Abstract: Brain is one of the most complex human organs. For long this has intrigued a number of researchers from various disciplines in the world. A lot of research has been done on brain since early days especially from the physiological, psychological angle. However, the advances in the computing technologies especially the High Performance Computing platform has opened several new avenues for researchers to carry out their research. In the in-silico approach of modelling the brain, the simulation is performed by constructing the networks which draw inspiration from the simple neuron model. We have performed a study on the hybrid CPU-GPU model based simulation of Spiking Neural Network. Our current work is to study the scalability by simulating the entire pipeline on a GPU.
Keywords: Izhikevich Model, Spiking Neural Networks, Spiking Neuron, CUDA.
Scope of the Article: Computer Science and Its Applications