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Memristor-Emulating-Integrate-and-Fire Neuron-Based Fully Neuromorphic Framework for Pattern Recognition
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DOI：10.1109/TCSII.2024.3439687.png)
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Abstract
En 中文
The need for low-power, area-efficient, and low-complexity hardware architectures has become an essential axis of emerging neuromorphic frameworks. Moreover, in the prevailing architectures for implementing the neural algorithms, the synaptic weights are digitally stored, which is a major Von-Neumann bottleneck in terms of energy and area. We thus introduce, for the first time, an area-efficient Memristor-Emulating-Integrate-and-Fire (MEIF) neuron-based fully neuromorphic architecture for pattern recognition. This pattern recognition scheme is based on the MEIF neuron circuit that has significantly less hardware complexity. The simulation results are based on a 1.8 V, 180-nm CMOS technology. Simulation-based experimental results show that our neuromorphic system, comprising 48 neurons, has the recognition capability with an average energy consumption of approximate to 5.255 pJ per neuron for the 4X3 pixel patterns.
Keywords:
Memristor
MEIF neuron
neuromorphic
pattern recognition
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