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

Heidarpur, Moslem, Ahmadi, Arash, Ahmadi, Majid, and Rahimi Azghadi, Mostafa (2019) CORDIC-SNN: on-FPGA STDP learning with Izhikevich neurons. IEEE Transactions on Circuits and Systems I: Regular Papers, 66 (7). pp. 2651-2661.

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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.

Item ID: 57388
Item Type: Article (Research - C1)
ISSN: 1558-0806
Keywords: neurons; computational modeling; biological system modeling; hardware; field programmable gate arays; neuromorphics; Izhikevich neuron; biological neuron model;CORDIC; digital implementation; neuromorphic; STDP; FPGA; online; on-FPGA; spiking neural network
Copyright Information: © 2019 IEEE
Date Deposited: 12 Mar 2019 00:52
FoR Codes: 40 ENGINEERING > 4008 Electrical engineering > 400801 Circuits and systems @ 100%
SEO Codes: 97 EXPANDING KNOWLEDGE > 970109 Expanding Knowledge in Engineering @ 30%
97 EXPANDING KNOWLEDGE > 970110 Expanding Knowledge in Technology @ 40%
97 EXPANDING KNOWLEDGE > 970108 Expanding Knowledge in the Information and Computing Sciences @ 30%
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