CORDIC-SNN: on-FPGA STDP learning with Izhikevich neurons
Journal Publication ResearchOnline@JCUAbstract
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
Publication Name
N/A
Volume
66
ISBN/ISSN
1558-0806
Edition
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Issue
7
Pages Count
11
Location
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Publisher
Institute of Electrical and Electronics Engineers
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Publisher Location
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Publish Date
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Date
N/A
EISSN
N/A
DOI
10.1109/TCSI.2019.2899356