Indian Journal of Science and Technology
DOI: 10.17485/ijst/2018/v11i39/130826
Year: 2018, Volume: 11, Issue: 39, Pages: 1-7
Original Article
N. Sreenivasa1 and S. Balaji2
1Department of Computer Science and Engineering, Jain University, Nitte Meenakshi Institute of Technology, P.O. Box 6429, Yelahanka, Bengaluru – 560064, Karnataka, India; [email protected]
2 Centre for Incubation, Innovation, Research and Consultancy, Jyothy Institute of Technology, Tataguni, Off Kanakapura Road, Bengaluru – 560082, Karnataka, India; [email protected]
*Author for correspondence
N. Sreenivasa,
Department of Computer Science and Engineering, Jain University, Nitte Meenakshi Institute of Technology, P.O. Box 6429, Yelahanka, Bengaluru – 560064, Karnataka, India; [email protected]
Objectives: To review various tools available for simulating Spiking Neural Networks using heterogeneous parallel processing platforms that help to reduce cost, increase the computational speed and also to document/archive lessons learnt. Methods/Statistical Analysis: The computational speed is a continuing challenge for simulating genuine spiking neural network models. Understanding of the spiking neural networks is significantly simplified by computer simulators like NEST, GeNN, EDLUT and BRIAN. Findings: Simulation is a handy toolkit of scientists and engineers of all disciplines. NEST, GeNN, EDLUT and BRIAN simulators help in achieving better performance not in terms of same kind of processing but with additional special tasks which require more computational power. BRIAN and EDLUT which are hybrid simulators supports both time driven and event driven techniques and outperform when compared to other simulators. Application/Improvements: Using BRIAN and EDLUT simulation techniques we can achieve the high performance when compared to other spiking neural simulation techniques.
Keywords: Graphics Processing Unit (GPU) Accelerators, Massive Parallel Computation, Simulation, Spiking Neural Network
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