CORDIC-SNN: on-FPGA STDP learning with Izhikevich neurons

Journal Publication ResearchOnline@JCU
Heidarpur, Moslem;Ahmadi, Arash;Ahmadi, Majid;Rahimi Azghadi, Mostafa
Abstract

This paper proposes a neuromorphic platform for on-FPGA online spike timing dependant plasticity (STDP) learning, based on the COordinate Rotation DIgital Computer (CORDIC) algorithms. The implemented platform comprises two main components. First, the Izhikevich neuron model is modified for implementation using the CORDIC algorithm, simulated to ensure the model accuracy, described as hardware, and implemented on FPGA. Second, the STDP learning algorithm is adapted and optimized using the CORDIC method, synthesized for hardware, and implemented to perform on-FPGA online learning on a network of CORDIC Izhikevich neurons to demonstrate competitive Hebbian learning. The implementation results are compared with the original model and state-of-the-art to verify accuracy, effectiveness, and higher speed of the system. These comparisons confirm that the proposed neuromorphic system offers better performance and higher accuracy while being straightforward to implement and suitable to scale.

Journal

IEEE Transactions on Circuits and Systems I: Regular Papers

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Volume

66

ISBN/ISSN

1558-0806

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Issue

7

Pages Count

11

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Publisher

Institute of Electrical and Electronics Engineers

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Date

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EISSN

N/A

DOI

10.1109/TCSI.2019.2899356