Training deep spiking neural networks
E Ledinauskas, J Ruseckas, A Juršėnas… - arXiv preprint arXiv …, 2020 - arxiv.org
Computation using brain-inspired spiking neural networks (SNNs) with neuromorphic
hardware may offer orders of magnitude higher energy efficiency compared to the current …
hardware may offer orders of magnitude higher energy efficiency compared to the current …
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 …
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 …
Training spiking neural networks using lessons from deep learning
The brain is the perfect place to look for inspiration to develop more efficient neural
networks. The inner workings of our synapses and neurons provide a glimpse at what the …
networks. The inner workings of our synapses and neurons provide a glimpse at what the …
Learning to be efficient: Algorithms for training low-latency, low-compute deep spiking neural networks
Recent advances have allowed Deep Spiking Neural Networks (SNNs) to perform at the
same accuracy levels as Artificial Neural Networks (ANNs), but have also highlighted a …
same accuracy levels as Artificial Neural Networks (ANNs), but have also highlighted a …
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 …
Optimizing the energy consumption of spiking neural networks for neuromorphic applications
In the last few years, spiking neural networks (SNNs) have been demonstrated to perform on
par with regular convolutional neural networks. Several works have proposed methods to …
par with regular convolutional neural networks. Several works have proposed methods to …
Training much deeper spiking neural networks with a small number of time-steps
Abstract Spiking Neural Network (SNN) is a promising energy-efficient neural architecture
when implemented on neuromorphic hardware. The Artificial Neural Network (ANN) to SNN …
when implemented on neuromorphic hardware. The Artificial Neural Network (ANN) to SNN …
Backpropagation-based learning techniques for deep spiking neural networks: A survey
M Dampfhoffer, T Mesquida… - IEEE Transactions on …, 2023 - ieeexplore.ieee.org
With the adoption of smart systems, artificial neural networks (ANNs) have become
ubiquitous. Conventional ANN implementations have high energy consumption, limiting …
ubiquitous. Conventional ANN implementations have high energy consumption, limiting …
Quantization framework for fast spiking neural networks
Compared with artificial neural networks (ANNs), spiking neural networks (SNNs) offer
additional temporal dynamics with the compromise of lower information transmission rates …
additional temporal dynamics with the compromise of lower information transmission rates …