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 …
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 …
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 …
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 …
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 …
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 …
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 …
Dct-snn: Using dct to distribute spatial information over time for learning low-latency spiking neural networks
Spiking Neural Networks (SNNs) offer a promising alternative to traditional deep learning
frameworks, since they provide higher computational efficiency due to event-driven …
frameworks, since they provide higher computational efficiency due to event-driven …