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Found 4 papers, 2 papers with code
High-Performance Temporal Reversible Spiking Neural Networks with $O(L)$ Training Memory and $O(1)$ Inference Cost
This work is expected to break the technical bottleneck of significantly increasing memory cost and training time for large-scale SNNs while maintaining high performance and low inference energy cost.
Gated Attention Coding for Training High-performance and Efficient Spiking Neural Networks
Experiments on CIFAR10/100 and ImageNet datasets demonstrate that GAC achieves state-of-the-art accuracy with remarkable efficiency.
Ranked #4 on
Image Classification
on CIFAR-10
(Accuracy metric)
Deep Directly-Trained Spiking Neural Networks for Object Detection
Spiking neural networks (SNNs) are brain-inspired energy-efficient models that encode information in spatiotemporal dynamics.
Probabilistic Modeling: Proving the Lottery Ticket Hypothesis in Spiking Neural Network
LTH opens up a new path for network pruning.
