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Found 5 papers, 0 papers with code
SLSSNN: High energy efficiency spike-train level spiking neural networks with spatio-temporal conversion
Brain-inspired spiking neuron networks (SNNs) have attracted widespread research interest due to their low power features, high biological plausibility, and strong spatiotemporal information processing capability.
Ultra-low Latency Adaptive Local Binary Spiking Neural Network with Accuracy Loss Estimator
Spiking neural network (SNN) is a brain-inspired model which has more spatio-temporal information processing capacity and computational energy efficiency.
Ultra-low Latency Spiking Neural Networks with Spatio-Temporal Compression and Synaptic Convolutional Block
We evaluate the proposed method for event streams classification tasks on neuromorphic N-MNIST, CIFAR10-DVS, DVS128 gesture datasets.
Direct Training via Backpropagation for Ultra-low Latency Spiking Neural Networks with Multi-threshold
In our proposed training method, we proposed three approximated derivative for spike activity to solve the problem of the non-differentiable issue which cause difficulties for direct training SNNs based on BP.
Machine Learning Regression based Single Event Transient Modeling Method for Circuit-Level Simulation
A multilayer feedfordward neural network is used to build the SET pulse current model by learning the data from TCAD simulation.
