Spikingformer: Spike-driven residual learning for transformer-based spiking neural network
Spiking neural networks (SNNs) offer a promising energy-efficient alternative to artificial
neural networks, due to their event-driven spiking computation. However, state-of-the-art …
neural networks, due to their event-driven spiking computation. However, state-of-the-art …
Spikeformer: a novel architecture for training high-performance low-latency spiking neural network
Spiking neural networks (SNNs) have made great progress on both performance and
efficiency over the last few years, but their unique working pattern makes it hard to train a …
efficiency over the last few years, but their unique working pattern makes it hard to train a …
SpikingResformer: Bridging ResNet and Vision Transformer in Spiking Neural Networks
The remarkable success of Vision Transformers in Artificial Neural Networks (ANNs) has led
to a growing interest in incorporating the self-attention mechanism and transformer-based …
to a growing interest in incorporating the self-attention mechanism and transformer-based …
Real spike: Learning real-valued spikes for spiking neural networks
Y Guo, L Zhang, Y Chen, X Tong, X Liu… - … on Computer Vision, 2022 - Springer
Brain-inspired spiking neural networks (SNNs) have recently drawn more and more
attention due to their event-driven and energy-efficient characteristics. The integration of …
attention due to their event-driven and energy-efficient characteristics. The integration of …
Masked spiking transformer
Abstract The combination of Spiking Neural Networks (SNNs) and Transformers has
attracted significant attention due to their potential for high energy efficiency and high …
attracted significant attention due to their potential for high energy efficiency and high …
Enabling deep spiking neural networks with hybrid conversion and spike timing dependent backpropagation
Spiking Neural Networks (SNNs) operate with asynchronous discrete events (or spikes)
which can potentially lead to higher energy-efficiency in neuromorphic hardware …
which can potentially lead to higher energy-efficiency in neuromorphic hardware …
Optimal ANN-SNN conversion for high-accuracy and ultra-low-latency spiking neural networks
Spiking Neural Networks (SNNs) have gained great attraction due to their distinctive
properties of low power consumption and fast inference on neuromorphic hardware. As the …
properties of low power consumption and fast inference on neuromorphic hardware. As the …
Deep residual learning in spiking neural networks
Abstract Deep Spiking Neural Networks (SNNs) present optimization difficulties for gradient-
based approaches due to discrete binary activation and complex spatial-temporal dynamics …
based approaches due to discrete binary activation and complex spatial-temporal dynamics …
Spikformer v2: Join the high accuracy club on imagenet with an snn ticket
Spiking Neural Networks (SNNs), known for their biologically plausible architecture, face the
challenge of limited performance. The self-attention mechanism, which is the cornerstone of …
challenge of limited performance. The self-attention mechanism, which is the cornerstone of …
Rmp-snn: Residual membrane potential neuron for enabling deeper high-accuracy and low-latency spiking neural network
Abstract Spiking Neural Networks (SNNs) have recently attracted significant research
interest as the third generation of artificial neural networks that can enable low-power event …
interest as the third generation of artificial neural networks that can enable low-power event …