Attention Spiking Neural Networks

• URL: http://arxiv.org/abs/2209.13929v1
• Date: Wed, 28 Sep 2022 09:00:45 GMT
• Title: Attention Spiking Neural Networks
• Authors: Man Yao, Guangshe Zhao, Hengyu Zhang, Yifan Hu, Lei Deng, Yonghong Tian, Bo Xu, and Guoqi Li
• Abstract summary: We study the effect of attention mechanisms in spiking neural networks (SNNs) New attention SNN architecture with end-to-end training called "MA-SNN" is proposed. Experiments are conducted in event-based DVS128 Gesture/Gait action recognition and ImageNet-1k image classification.
• Score: 32.591900260554326
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: Benefiting from the event-driven and sparse spiking characteristics of the brain, spiking neural networks (SNNs) are becoming an energy-efficient alternative to artificial neural networks (ANNs). However, the performance gap between SNNs and ANNs has been a great hindrance to deploying SNNs ubiquitously for a long time. To leverage the full potential of SNNs, we study the effect of attention mechanisms in SNNs. We first present our idea of attention with a plug-and-play kit, termed the Multi-dimensional Attention (MA). Then, a new attention SNN architecture with end-to-end training called "MA-SNN" is proposed, which infers attention weights along the temporal, channel, as well as spatial dimensions separately or simultaneously. Based on the existing neuroscience theories, we exploit the attention weights to optimize membrane potentials, which in turn regulate the spiking response in a data-dependent way. At the cost of negligible additional parameters, MA facilitates vanilla SNNs to achieve sparser spiking activity, better performance, and energy efficiency concurrently. Experiments are conducted in event-based DVS128 Gesture/Gait action recognition and ImageNet-1k image classification. On Gesture/Gait, the spike counts are reduced by 84.9%/81.6%, and the task accuracy and energy efficiency are improved by 5.9%/4.7% and 3.4$\times$/3.2$\times$. On ImageNet-1K, we achieve top-1 accuracy of 75.92% and 77.08% on single/4-step Res-SNN-104, which are state-of-the-art results in SNNs. To our best knowledge, this is for the first time, that the SNN community achieves comparable or even better performance compared with its ANN counterpart in the large-scale dataset. Our work lights up SNN's potential as a general backbone to support various applications for SNNs, with a great balance between effectiveness and efficiency.

Related papers

• Scaling Spike-driven Transformer with Efficient Spike Firing Approximation Training [17.193023656793464]
  The ambition of brain-inspired Spiking Neural Networks (SNNs) is to become a low-power alternative to traditional Artificial Neural Networks (ANNs) This work addresses two major challenges in realizing this vision: the performance gap between SNNs and ANNs, and the high training costs of SNNs. We identify intrinsic flaws in spiking neurons caused by binary firing mechanisms and propose a Spike Firing Approximation (SFA) method using integer training and spike-driven inference.
  arXiv  Detail & Related papers  (2024-11-25T03:05:41Z)
• NAS-BNN: Neural Architecture Search for Binary Neural Networks [55.058512316210056]
  We propose a novel neural architecture search scheme for binary neural networks, named NAS-BNN. Our discovered binary model family outperforms previous BNNs for a wide range of operations (OPs) from 20M to 200M. In addition, we validate the transferability of these searched BNNs on the object detection task, and our binary detectors with the searched BNNs achieve a novel state-of-the-art result, e.g., 31.6% mAP with 370M OPs, on MS dataset.
  arXiv  Detail & Related papers  (2024-08-28T02:17:58Z)
• Enhancing Adversarial Robustness in SNNs with Sparse Gradients [46.15229142258264]
  Spiking Neural Networks (SNNs) have attracted great attention for their energy-efficient operations and biologically inspired structures. Existing techniques, whether adapted from ANNs or specifically designed for SNNs, exhibit limitations in training SNNs or defending against strong attacks. We propose a novel approach to enhance the robustness of SNNs through gradient sparsity regularization.
  arXiv  Detail & Related papers  (2024-05-30T05:39:27Z)
• Spikeformer: A Novel Architecture for Training High-Performance Low-Latency Spiking Neural Network [6.8125324121155275]
  We propose a novel Transformer-based SNN,termed "Spikeformer",which outperforms its ANN counterpart on both static dataset and neuromorphic dataset. Remarkably,our Spikeformer outperforms other SNNs on ImageNet by a large margin (i.e.more than 5%) and even outperforms its ANN counterpart by 3.1% and 2.2% on DVS-Gesture and ImageNet.
  arXiv  Detail & Related papers  (2022-11-19T12:49:22Z)
• Adaptive-SpikeNet: Event-based Optical Flow Estimation using Spiking Neural Networks with Learnable Neuronal Dynamics [6.309365332210523]
  Spiking Neural Networks (SNNs) with their neuro-inspired event-driven processing can efficiently handle asynchronous data. We propose an adaptive fully-spiking framework with learnable neuronal dynamics to alleviate the spike vanishing problem. Our experiments on datasets show an average reduction of 13% in average endpoint error (AEE) compared to state-of-the-art ANNs.
  arXiv  Detail & Related papers  (2022-09-21T21:17:56Z)
• SNN2ANN: A Fast and Memory-Efficient Training Framework for Spiking Neural Networks [117.56823277328803]
  Spiking neural networks are efficient computation models for low-power environments. We propose a SNN-to-ANN (SNN2ANN) framework to train the SNN in a fast and memory-efficient way. Experiment results show that our SNN2ANN-based models perform well on the benchmark datasets.
  arXiv  Detail & Related papers  (2022-06-19T16:52:56Z)
• Training High-Performance Low-Latency Spiking Neural Networks by Differentiation on Spike Representation [70.75043144299168]
  Spiking Neural Network (SNN) is a promising energy-efficient AI model when implemented on neuromorphic hardware. It is a challenge to efficiently train SNNs due to their non-differentiability. We propose the Differentiation on Spike Representation (DSR) method, which could achieve high performance.
  arXiv  Detail & Related papers  (2022-05-01T12:44:49Z)
• Advancing Residual Learning towards Powerful Deep Spiking Neural Networks [16.559670769601038]
  Residual learning and shortcuts have been evidenced as an important approach for training deep neural networks. MS-ResNet is able to significantly extend the depth of directly trained SNNs. MS-ResNet 104 achieves 76.02% accuracy on ImageNet, the first time in the domain of directly trained SNNs.
  arXiv  Detail & Related papers  (2021-12-15T05:47:21Z)
• Spiking Neural Networks for Visual Place Recognition via Weighted Neuronal Assignments [24.754429120321365]
  Spiking neural networks (SNNs) offer both compelling potential advantages, including energy efficiency and low latencies. One promising area for high performance SNNs is template matching and image recognition. This research introduces the first high performance SNN for the Visual Place Recognition (VPR) task.
  arXiv  Detail & Related papers  (2021-09-14T05:40:40Z)
• Progressive Tandem Learning for Pattern Recognition with Deep Spiking Neural Networks [80.15411508088522]
  Spiking neural networks (SNNs) have shown advantages over traditional artificial neural networks (ANNs) for low latency and high computational efficiency. We propose a novel ANN-to-SNN conversion and layer-wise learning framework for rapid and efficient pattern recognition.
  arXiv  Detail & Related papers  (2020-07-02T15:38:44Z)
• You Only Spike Once: Improving Energy-Efficient Neuromorphic Inference to ANN-Level Accuracy [51.861168222799186]
  Spiking Neural Networks (SNNs) are a type of neuromorphic, or brain-inspired network. SNNs are sparse, accessing very few weights, and typically only use addition operations instead of the more power-intensive multiply-and-accumulate operations. In this work, we aim to overcome the limitations of TTFS-encoded neuromorphic systems.
  arXiv  Detail & Related papers  (2020-06-03T15:55:53Z)

This list is automatically generated from the titles and abstracts of the papers in this site.

This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.