Spatio-Temporal Pruning and Quantization for Low-latency Spiking Neural Networks

Chowdhury, Sayeed Shafayet; Garg, Isha; Roy, Kaushik

doi:10.1109/ijcnn52387.2021.9534111

Cited by 17 publications

(5 citation statements)

References 22 publications

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“…SNNs, unlike their non-spiking counterparts, consist of a temporal dimension. Along with considering temporal information, a spatial and temporal pruning of SNNs is proposed in Chowdhury et al (2021). Generally speaking, pruning will cause accuracy degradation to some extent.…”

Section: Parameter Pruningmentioning

confidence: 99%

Direct learning-based deep spiking neural networks: a review

Guo

Huang

2023

Front. Neurosci.

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The spiking neural network (SNN), as a promising brain-inspired computational model with binary spike information transmission mechanism, rich spatially-temporal dynamics, and event-driven characteristics, has received extensive attention. However, its intricately discontinuous spike mechanism brings difficulty to the optimization of the deep SNN. Since the surrogate gradient method can greatly mitigate the optimization difficulty and shows great potential in directly training deep SNNs, a variety of direct learning-based deep SNN works have been proposed and achieved satisfying progress in recent years. In this paper, we present a comprehensive survey of these direct learning-based deep SNN works, mainly categorized into accuracy improvement methods, efficiency improvement methods, and temporal dynamics utilization methods. In addition, we also divide these categorizations into finer granularities further to better organize and introduce them. Finally, the challenges and trends that may be faced in future research are prospected.

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Section: Parameter Pruningmentioning

confidence: 99%

Direct learning-based deep spiking neural networks: a review

Guo

Huang

2023

Front. Neurosci.

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“…Schaefer and Joshi ( 2020 ) propose integer fixed-point representations for neural dynamics, weights, loss, and gradients. The recent work (Chowdhury et al, 2021a ) performs quantization through temporal dimension for low-latency SNNs. Lui and Neftci propose a quantization technique based on the Hessian of weights (Lui and Neftci, 2021 ).…”

Section: Related Workmentioning

confidence: 99%

Sharing leaky-integrate-and-fire neurons for memory-efficient spiking neural networks

Kim,

Li,

Moitra

et al. 2023

Front. Neurosci.

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Spiking Neural Networks (SNNs) have gained increasing attention as energy-efficient neural networks owing to their binary and asynchronous computation. However, their non-linear activation, that is Leaky-Integrate-and-Fire (LIF) neuron, requires additional memory to store a membrane voltage to capture the temporal dynamics of spikes. Although the required memory cost for LIF neurons significantly increases as the input dimension goes larger, a technique to reduce memory for LIF neurons has not been explored so far. To address this, we propose a simple and effective solution, EfficientLIF-Net, which shares the LIF neurons across different layers and channels. Our EfficientLIF-Net achieves comparable accuracy with the standard SNNs while bringing up to ~4.3× forward memory efficiency and ~21.9× backward memory efficiency for LIF neurons. We conduct experiments on various datasets including CIFAR10, CIFAR100, TinyImageNet, ImageNet-100, and N-Caltech101. Furthermore, we show that our approach also offers advantages on Human Activity Recognition (HAR) datasets, which heavily rely on temporal information. The code has been released at https://github.com/Intelligent-Computing-Lab-Yale/EfficientLIF-Net.

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“…Zhang 等 [22] 提出了一种异步脉冲卷积核长程结构投射, 增强 SNN 的编码, 加快学习, 降低能耗. Chowdhury 等 [23] 基于主成分分析法分析各层显著性, 提出了一种时空剪枝方法. 这些网络剪枝方法均在模型压缩率和性能方面证明了有效性, 均属于模型压缩方法, 即通过对已有网络结构进行压缩以达到减小网络规模的目的.…”

Section: 脉冲神经网络模型压缩方法unclassified

Towards event camera signal recognition using a lightweight spiking neural network

Liu¹,

Wu²,

Shi³

et al. 2023

Sci. Sin.-Inf.

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Event cameras are event-driven bio-inspired sensors, owing to the following advantages: high temporal resolution, high dynamic range, low power, and high imaging speed. Artificial neural networks (ANNs) cannot directly process their output spike signal. Spiking neural networks (SNNs) are neuromorphic computing methods with a high temporal resolution and are event-driven, which fits well with event cameras. However, deep SNNs require large storage space and neuronal computing resources, which limits their deployment in mobile edge computing. Based on the feature dimension mapping, this paper proposes a lightweight SNN for embedded devices to reduce storage requirements, improve efficiency, and enhance network performance. First, by analyzing the relationship between the parameter number and the fitting function of the network, the SNN parameter bottleneck is clear. Subsequently, a general lightweight feature extraction structure, SpikeFire, is proposed based on a lowdimensional feature extraction-fusion strategy, which significantly reduces network parameters on the premise that the basic properties of the receptive field and feature dimension are the same as those of a spiking convolution. Additionally, the module simulates the complex connection of the brain and adopts the skip connection, which not only increases the extraction of multi-scale information but also contributes to deep network optimization. Finally, the lightweight network is deployed in embedded hardware to build an event-driven imaging recognition system. Experiments show that our method can reduce the parameter number and improve the running speed without weakening the performance in public, self-built real scenes, and extreme imaging scene datasets.

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Spatio-Temporal Pruning and Quantization for Low-latency Spiking Neural Networks

Cited by 17 publications

References 22 publications

Direct learning-based deep spiking neural networks: a review

Direct learning-based deep spiking neural networks: a review

Sharing leaky-integrate-and-fire neurons for memory-efficient spiking neural networks

Towards event camera signal recognition using a lightweight spiking neural network

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