BS4NN: Binarized spiking neural networks with temporal coding and learning
SR Kheradpisheh, M Mirsadeghi… - Neural Processing …, 2022 - Springer
We recently proposed the S4NN algorithm, essentially an adaptation of backpropagation to
multilayer spiking neural networks that use simple non-leaky integrate-and-fire neurons and …
multilayer spiking neural networks that use simple non-leaky integrate-and-fire neurons and …
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 …
Tensor decomposition based attention module for spiking neural networks
The attention mechanism has been proven to be an effective way to improve the
performance of spiking neural networks (SNNs). However, from the perspective of tensor …
performance of spiking neural networks (SNNs). However, from the perspective of tensor …
A free lunch from ANN: Towards efficient, accurate spiking neural networks calibration
Abstract Spiking Neural Network (SNN) has been recognized as one of the next generation
of neural networks. Conventionally, SNN can be converted from a pre-trained ANN by only …
of neural networks. Conventionally, SNN can be converted from a pre-trained ANN by only …
Training high-performance low-latency spiking neural networks by differentiation on spike representation
Abstract Spiking Neural Network (SNN) is a promising energy-efficient AI model when
implemented on neuromorphic hardware. However, it is a challenge to efficiently train SNNs …
implemented on neuromorphic hardware. However, it is a challenge to efficiently train SNNs …
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 …
Joint a-snn: Joint training of artificial and spiking neural networks via self-distillation and weight factorization
Emerged as a biology-inspired method, Spiking Neural Networks (SNNs) mimic the spiking
nature of brain neurons and have received lots of research attention. SNNs deal with binary …
nature of brain neurons and have received lots of research attention. SNNs deal with binary …
Eqspike: spike-driven equilibrium propagation for neuromorphic implementations
Finding spike-based learning algorithms that can be implemented within the local
constraints of neuromorphic systems, while achieving high accuracy, remains a formidable …
constraints of neuromorphic systems, while achieving high accuracy, remains a formidable …
Toward the optimal design and FPGA implementation of spiking neural networks
The performance of a biologically plausible spiking neural network (SNN) largely depends
on the model parameters and neural dynamics. This article proposes a parameter …
on the model parameters and neural dynamics. This article proposes a parameter …
Toward scalable, efficient, and accurate deep spiking neural networks with backward residual connections, stochastic softmax, and hybridization
Spiking Neural Networks (SNNs) may offer an energy-efficient alternative for implementing
deep learning applications. In recent years, there have been several proposals focused on …
deep learning applications. In recent years, there have been several proposals focused on …