STAA-SNN: Spatial-Temporal Attention Aggregator for Spiking Neural Networks

• URL: http://arxiv.org/abs/2503.02689v3
• Date: Tue, 29 Apr 2025 05:16:11 GMT
• Title: STAA-SNN: Spatial-Temporal Attention Aggregator for Spiking Neural Networks
• Authors: Tianqing Zhang, Kairong Yu, Xian Zhong, Hongwei Wang, Qi Xu, Qiang Zhang,
• Abstract summary: Spiking Neural Networks (SNNs) have gained significant attention due to their biological plausibility and energy efficiency.<n>However, the performance gap between SNNs and Artificial Neural Networks (ANNs) remains a substantial challenge hindering the widespread adoption of SNNs.<n>We propose a Spatial-Temporal Attention Aggregator SNN framework, which dynamically focuses on and captures both spatial and temporal dependencies.
• Score: 17.328954271272742
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Spiking Neural Networks (SNNs) have gained significant attention due to their biological plausibility and energy efficiency, making them promising alternatives to Artificial Neural Networks (ANNs). However, the performance gap between SNNs and ANNs remains a substantial challenge hindering the widespread adoption of SNNs. In this paper, we propose a Spatial-Temporal Attention Aggregator SNN (STAA-SNN) framework, which dynamically focuses on and captures both spatial and temporal dependencies. First, we introduce a spike-driven self-attention mechanism specifically designed for SNNs. Additionally, we pioneeringly incorporate position encoding to integrate latent temporal relationships into the incoming features. For spatial-temporal information aggregation, we employ step attention to selectively amplify relevant features at different steps. Finally, we implement a time-step random dropout strategy to avoid local optima. As a result, STAA-SNN effectively captures both spatial and temporal dependencies, enabling the model to analyze complex patterns and make accurate predictions. The framework demonstrates exceptional performance across diverse datasets and exhibits strong generalization capabilities. Notably, STAA-SNN achieves state-of-the-art results on neuromorphic datasets CIFAR10-DVS, with remarkable performances of 97.14%, 82.05% and 70.40% on the static datasets CIFAR-10, CIFAR-100 and ImageNet, respectively. Furthermore, our model exhibits improved performance ranging from 0.33\% to 2.80\% with fewer time steps. The code for the model is available on GitHub.

Related papers

• Spatial-Temporal Search for Spiking Neural Networks [32.937536365872745]
  Spiking Neural Networks (SNNs) are considered as a potential candidate for the next generation of artificial intelligence. We propose a differentiable approach to optimize SNN on both spatial and temporal dimensions. Our methods achieve comparable classification performance of CIFAR10/100 and ImageNet with accuracies of 96.43%, 78.96%, and 70.21%, respectively.
  arXiv  Detail & Related papers  (2024-10-24T09:32:51Z)
• Enhancing SNN-based Spatio-Temporal Learning: A Benchmark Dataset and Cross-Modality Attention Model [30.66645039322337]
  High-quality benchmark datasets are great importance to the advances of Artificial Neural Networks (SNNs) Yet, the SNN-based cross-modal fusion remains underexplored. In this work, we present a neuromorphic dataset that can better exploit the inherent-temporal betemporal of SNNs.
  arXiv  Detail & Related papers  (2024-10-21T06:59:04Z)
• Towards Low-latency Event-based Visual Recognition with Hybrid Step-wise Distillation Spiking Neural Networks [50.32980443749865]
  Spiking neural networks (SNNs) have garnered significant attention for their low power consumption and high biologicalability. Current SNNs struggle to balance accuracy and latency in neuromorphic datasets. We propose Step-wise Distillation (HSD) method, tailored for neuromorphic datasets.
  arXiv  Detail & Related papers  (2024-09-19T06:52:34Z)
• Advancing Spiking Neural Networks towards Multiscale Spatiotemporal Interaction Learning [10.702093960098106]
  Spiking Neural Networks (SNNs) serve as an energy-efficient alternative to Artificial Neural Networks (ANNs) We have designed a Spiking Multiscale Attention (SMA) module that captures multiscaletemporal interaction information. Our approach has achieved state-of-the-art results on mainstream neural datasets.
  arXiv  Detail & Related papers  (2024-05-22T14:16:05Z)
• Efficient and Effective Time-Series Forecasting with Spiking Neural Networks [47.371024581669516]
  Spiking neural networks (SNNs) provide a unique pathway for capturing the intricacies of temporal data. Applying SNNs to time-series forecasting is challenging due to difficulties in effective temporal alignment, complexities in encoding processes, and the absence of standardized guidelines for model selection. We propose a framework for SNNs in time-series forecasting tasks, leveraging the efficiency of spiking neurons in processing temporal information.
  arXiv  Detail & Related papers  (2024-02-02T16:23:50Z)
• Fully Spiking Denoising Diffusion Implicit Models [61.32076130121347]
  Spiking neural networks (SNNs) have garnered considerable attention owing to their ability to run on neuromorphic devices with super-high speeds. We propose a novel approach fully spiking denoising diffusion implicit model (FSDDIM) to construct a diffusion model within SNNs. We demonstrate that the proposed method outperforms the state-of-the-art fully spiking generative model.
  arXiv  Detail & Related papers  (2023-12-04T09:07:09Z)
• A Hybrid ANN-SNN Architecture for Low-Power and Low-Latency Visual Perception [27.144985031646932]
  Spiking Neural Networks (SNN) are a class of bio-inspired neural networks that promise to bring low-power and low-latency inference to edge devices. We show for the task of event-based 2D and 3D human pose estimation that our method consumes 88% less power with only a 4% decrease in performance compared to its fully ANN counterparts.
  arXiv  Detail & Related papers  (2023-03-24T17:38:45Z)
• 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)
• 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)
• ANNETTE: Accurate Neural Network Execution Time Estimation with Stacked Models [56.21470608621633]
  We propose a time estimation framework to decouple the architectural search from the target hardware. The proposed methodology extracts a set of models from micro- kernel and multi-layer benchmarks and generates a stacked model for mapping and network execution time estimation. We compare estimation accuracy and fidelity of the generated mixed models, statistical models with the roofline model, and a refined roofline model for evaluation.
  arXiv  Detail & Related papers  (2021-05-07T11:39:05Z)
• 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)

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.