CORDIC-SNN: On-FPGA STDP Learning With Izhikevich Neurons
作者： Moslem Heidarpur , Arash Ahmadi , Majid Ahmadi , Mostafa Rahimi Azghadi
DOI: 10.1109/TCSI.2019.2899356
关键词:
摘要: 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 comprises two main components. First, Izhikevich neuron model is modified implementation using CORDIC algorithm, simulated to ensure accuracy, described as hardware, and FPGA. Second, STDP learning algorithm adapted optimized method, synthesized perform network of neurons demonstrate competitive Hebbian learning. results are compared with original state-of-the-art verify effectiveness, higher speed system. These comparisons confirm that proposed system offers better performance accuracy while being straightforward implement suitable scale.
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