FPGA Implementation of Simplified Spiking Neural Network

• URL: http://arxiv.org/abs/2010.01200v1
• Date: Fri, 2 Oct 2020 21:02:35 GMT
• Title: FPGA Implementation of Simplified Spiking Neural Network
• Authors: Shikhar Gupta, Arpan Vyas, Gaurav Trivedi
• Abstract summary: Spiking Neural Networks (SNN) are third-generation Artificial Neural Networks (ANN) which are close to the biological neural system. SNNs are more powerful than their predecessors because they encode temporal information and use biologically plausible plasticity rules. The proposed model is validated on a Xilinx Virtex 6 FPGA and analyzes a fully connected network which consists of 800 neurons and 12,544 synapses in real-time.
• Score: 0.6445605125467573
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Spiking Neural Networks (SNN) are third-generation Artificial Neural Networks (ANN) which are close to the biological neural system. In recent years SNN has become popular in the area of robotics and embedded applications, therefore, it has become imperative to explore its real-time and energy-efficient implementations. SNNs are more powerful than their predecessors because they encode temporal information and use biologically plausible plasticity rules. In this paper, a simpler and computationally efficient SNN model using FPGA architecture is described. The proposed model is validated on a Xilinx Virtex 6 FPGA and analyzes a fully connected network which consists of 800 neurons and 12,544 synapses in real-time.

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