Diet-snn: Direct input encoding with leakage and threshold optimization in deep spiking neural networks
Bio-inspired spiking neural networks (SNNs), operating with asynchronous binary signals
(or spikes) distributed over time, can potentially lead to greater computational efficiency on …
(or spikes) distributed over time, can potentially lead to greater computational efficiency on …
Diet-snn: A low-latency spiking neural network with direct input encoding and leakage and threshold optimization
Bioinspired spiking neural networks (SNNs), operating with asynchronous binary signals (or
spikes) distributed over time, can potentially lead to greater computational efficiency on …
spikes) distributed over time, can potentially lead to greater computational efficiency on …
[HTML][HTML] Direct learning-based deep spiking neural networks: a review
The spiking neural network (SNN), as a promising brain-inspired computational model with
binary spike information transmission mechanism, rich spatially-temporal dynamics, and …
binary spike information transmission mechanism, rich spatially-temporal dynamics, and …
Incorporating learnable membrane time constant to enhance learning of spiking neural networks
Abstract Spiking Neural Networks (SNNs) have attracted enormous research interest due to
temporal information processing capability, low power consumption, and high biological …
temporal information processing capability, low power consumption, and high biological …
Direct training for spiking neural networks: Faster, larger, better
Spiking neural networks (SNNs) that enables energy efficient implementation on emerging
neuromorphic hardware are gaining more attention. Yet now, SNNs have not shown …
neuromorphic hardware are gaining more attention. Yet now, SNNs have not shown …
Dct-snn: Using dct to distribute spatial information over time for low-latency spiking neural networks
Abstract Spiking Neural Networks (SNNs) offer a promising alternative to traditional deep
learning frameworks, since they provide higher computational efficiency due to event-driven …
learning frameworks, since they provide higher computational efficiency due to event-driven …
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 …
A tandem learning rule for effective training and rapid inference of deep spiking neural networks
Spiking neural networks (SNNs) represent the most prominent biologically inspired
computing model for neuromorphic computing (NC) architectures. However, due to the …
computing model for neuromorphic computing (NC) architectures. However, due to the …
Neuromorphic data augmentation for training spiking neural networks
Developing neuromorphic intelligence on event-based datasets with Spiking Neural
Networks (SNNs) has recently attracted much research attention. However, the limited size …
Networks (SNNs) has recently attracted much research attention. However, the limited size …