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The paper presents a hybrid Convolutional Spiking Neural Network (CSNN) architecture that integrates digital Non-Leaky Integrate-and-Fire neurons with Knowm Self-Directed Channel memristor-based synapses for energy-efficient edge computing. By utilizing a streamlined readout circuit and a temporal coding scheme, the proposed system achieves high classification accuracy on the MNIST dataset while significantly reducing power consumption compared to conventional methods. This approach addresses the challenges

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gqzhang166

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EN