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Spiking neural networks for autonomous driving: A review
FS Martínez, J Casas-Roma, L Subirats… - … Applications of Artificial …, 2024 - Elsevier
The rapid progress of autonomous driving (AD) has triggered a surge in demand for safer
and more efficient autonomous vehicles, owing to the intricacy of modern urban …
and more efficient autonomous vehicles, owing to the intricacy of modern urban …
Direct training of snn using local zeroth order method
Spiking neural networks are becoming increasingly popular for their low energy requirement
in real-world tasks with accuracy comparable to traditional ANNs. SNN training algorithms …
in real-world tasks with accuracy comparable to traditional ANNs. SNN training algorithms …
When in-memory computing meets spiking neural networks—A perspective on device-circuit-system-and-algorithm co-design
This review explores the intersection of bio-plausible artificial intelligence in the form of
spiking neural networks (SNNs) with the analog in-memory computing (IMC) domain …
spiking neural networks (SNNs) with the analog in-memory computing (IMC) domain …
Are SNNs truly energy-efficient?—A hardware perspective
Spiking Neural Networks (SNNs) have gained attention for their energy-efficient machine
learning capabilities, utilizing bio-inspired activation functions and sparse binary spike-data …
learning capabilities, utilizing bio-inspired activation functions and sparse binary spike-data …
Efficient processing of spiking neural networks via task specialization
MA Lebdeh, KS Yildirim… - IEEE Transactions on …, 2024 - ieeexplore.ieee.org
Spiking neural networks (SNNs) are considered as a candidate for efficient deep learning
systems: these networks communicate with 0 or 1 spikes and their computations do not …
systems: these networks communicate with 0 or 1 spikes and their computations do not …
S-tllr: Stdp-inspired temporal local learning rule for spiking neural networks
MPE Apolinario, K Roy - arxiv preprint arxiv:2306.15220, 2023 - arxiv.org
Spiking Neural Networks (SNNs) are biologically plausible models that have been identified
as potentially apt for deploying energy-efficient intelligence at the edge, particularly for …
as potentially apt for deploying energy-efficient intelligence at the edge, particularly for …
Efficient Training of Spiking Neural Networks with Multi-parallel Implicit Stream Architecture
Z Cao, M Li, X Wang, H Wang, F Wang, Y Li… - … on Computer Vision, 2024 - Springer
Spiking neural networks (SNNs) are a novel type of bio-plausible neural network with
energy efficiency. However, SNNs are non-differentiable and the training memory costs …
energy efficiency. However, SNNs are non-differentiable and the training memory costs …
On the Intrinsic Structures of Spiking Neural Networks
Recent years have emerged a surge of interest in spiking neural networks (SNNs). The
performance of SNNs hinges not only on searching apposite architectures and connection …
performance of SNNs hinges not only on searching apposite architectures and connection …
Per Layer Specialization for Memory-Efficient Spiking Neural Networks
MA Lebdeh, KS Yildirim… - … on Neuromorphic Systems …, 2024 - ieeexplore.ieee.org
Specializing the dataset to train narrow spiking neural networks (SNNs) was recently
proposed as an efficient approach for processing SNNs. This approach mainly reduces the …
proposed as an efficient approach for processing SNNs. This approach mainly reduces the …
基于自适应时间步脉冲神经网络的高效图像分类
**千鹏， 贾顺程， 张铁林， 陈亮 - 自动化学报, 2024 - aas.net.cn
脉冲神经网络(Spiking neural network, SNN) 由于具有相对人工神经网络(Artifcial neural
network, ANN) 更低的计算能耗而受到广泛关注. 然而, 现有SNN 大多基于同步计算模式且往往 …
network, ANN) 更低的计算能耗而受到广泛关注. 然而, 现有SNN 大多基于同步计算模式且往往 …