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 …
Training energy-efficient deep spiking neural networks with single-spike hybrid input encoding
Spiking Neural Networks (SNNs) have emerged as an attractive alternative to traditional
deep learning frameworks, since they provide higher computational efficiency in event …
deep learning frameworks, since they provide higher computational efficiency in event …
Deep spiking neural network: Energy efficiency through time based coding
Abstract Spiking Neural Networks (SNNs) are promising for enabling low-power event-
driven data analytics. The best performing SNNs for image recognition tasks are obtained by …
driven data analytics. The best performing SNNs for image recognition tasks are obtained by …
Going deeper with directly-trained larger spiking neural networks
Spiking neural networks (SNNs) are promising in a bio-plausible coding for spatio-temporal
information and event-driven signal processing, which is very suited for energy-efficient …
information and event-driven signal processing, which is very suited for energy-efficient …
Gated attention coding for training high-performance and efficient spiking neural networks
Spiking neural networks (SNNs) are emerging as an energy-efficient alternative to traditional
artificial neural networks (ANNs) due to their unique spike-based event-driven nature …
artificial neural networks (ANNs) due to their unique spike-based event-driven nature …
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 …
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 …
One timestep is all you need: Training spiking neural networks with ultra low latency
SS Chowdhury, N Rathi, K Roy - arXiv preprint arXiv:2110.05929, 2021 - arxiv.org
Spiking Neural Networks (SNNs) are energy efficient alternatives to commonly used deep
neural networks (DNNs). Through event-driven information processing, SNNs can reduce …
neural networks (DNNs). Through event-driven information processing, SNNs can reduce …
Recdis-snn: Rectifying membrane potential distribution for directly training spiking neural networks
Y Guo, X Tong, Y Chen, L Zhang… - Proceedings of the …, 2022 - openaccess.thecvf.com
The brain-inspired and event-driven Spiking Neural Network (SNN) aims at mimicking the
synaptic activity of biological neurons, which transmits binary spike signals between network …
synaptic activity of biological neurons, which transmits binary spike signals between network …