Tcja-snn: Temporal-channel joint attention for spiking neural networks
Spiking neural networks (SNNs) are attracting widespread interest due to their biological
plausibility, energy efficiency, and powerful spatiotemporal information representation …
plausibility, energy efficiency, and powerful spatiotemporal information representation …
Tensor decomposition based attention module for spiking neural networks
The attention mechanism has been proven to be an effective way to improve the
performance of spiking neural networks (SNNs). However, from the perspective of tensor …
performance of spiking neural networks (SNNs). However, from the perspective of tensor …
Event-driven learning for spiking neural networks
Brain-inspired spiking neural networks (SNNs) have gained prominence in the field of
neuromorphic computing owing to their low energy consumption during feedforward …
neuromorphic computing owing to their low energy consumption during feedforward …
OR Residual Connection Achieving Comparable Accuracy to ADD Residual Connection in Deep Residual Spiking Neural Networks
Spiking Neural Networks (SNNs) have garnered substantial attention in brain-like computing
for their biological fidelity and the capacity to execute energy-efficient spike-driven …
for their biological fidelity and the capacity to execute energy-efficient spike-driven …
When Spiking neural networks meet temporal attention image decoding and adaptive spiking neuron
Spiking Neural Networks (SNNs) are capable of encoding and processing temporal
information in a biologically plausible way. However, most existing SNN-based methods for …
information in a biologically plausible way. However, most existing SNN-based methods for …
Firefly v2: Advancing hardware support for high-performance spiking neural network with a spatiotemporal fpga accelerator
Spiking Neural Networks (SNNs) are expected to be a promising alternative to Artificial
Neural Networks (ANNs) due to their strong biological interpretability and high energy …
Neural Networks (ANNs) due to their strong biological interpretability and high energy …
Learning A Spiking Neural Network for Efficient Image Deraining
Recently, spiking neural networks (SNNs) have demonstrated substantial potential in
computer vision tasks. In this paper, we present an Efficient Spiking Deraining Network …
computer vision tasks. In this paper, we present an Efficient Spiking Deraining Network …
Q-SNNs: Quantized Spiking Neural Networks
W Wei, Y Liang, A Belatreche, Y Xiao, H Cao… - arXiv preprint arXiv …, 2024 - arxiv.org
Brain-inspired Spiking Neural Networks (SNNs) leverage sparse spikes to represent
information and process them in an asynchronous event-driven manner, offering an energy …
information and process them in an asynchronous event-driven manner, offering an energy …
Autonomous Driving with Spiking Neural Networks
Autonomous driving demands an integrated approach that encompasses perception,
prediction, and planning, all while operating under strict energy constraints to enhance …
prediction, and planning, all while operating under strict energy constraints to enhance …
Temporal Reversed Training for Spiking Neural Networks with Generalized Spatio-Temporal Representation
Spiking neural networks (SNNs) have received widespread attention as an ultra-low energy
computing paradigm. Recent studies have focused on improving the feature extraction …
computing paradigm. Recent studies have focused on improving the feature extraction …