SpikeVideoFormer: An Efficient Spike-Driven Video Transformer with Hamming Attention and $\mathcal{O}(T)$ Complexity (2505.10352v1)

Abstract: Spiking Neural Networks (SNNs) have shown competitive performance to Artificial Neural Networks (ANNs) in various vision tasks, while offering superior energy efficiency. However, existing SNN-based Transformers primarily focus on single-image tasks, emphasizing spatial features while not effectively leveraging SNNs' efficiency in video-based vision tasks. In this paper, we introduce SpikeVideoFormer, an efficient spike-driven video Transformer, featuring linear temporal complexity $\mathcal{O}(T)$. Specifically, we design a spike-driven Hamming attention (SDHA) which provides a theoretically guided adaptation from traditional real-valued attention to spike-driven attention. Building on SDHA, we further analyze various spike-driven space-time attention designs and identify an optimal scheme that delivers appealing performance for video tasks, while maintaining only linear temporal complexity. The generalization ability and efficiency of our model are demonstrated across diverse downstream video tasks, including classification, human pose tracking, and semantic segmentation. Empirical results show our method achieves state-of-the-art (SOTA) performance compared to existing SNN approaches, with over 15\% improvement on the latter two tasks. Additionally, it matches the performance of recent ANN-based methods while offering significant efficiency gains, achieving $\times 16$, $\times 10$ and $\times 5$ improvements on the three tasks. https://github.com/JimmyZou/SpikeVideoFormer

• The paper introduces SpikeVideoFormer, an SNN-based video transformer featuring spike-driven Hamming attention and simultaneous spatial-temporal attention for efficient video processing.
• Empirical results show SpikeVideoFormer achieves state-of-the-art performance over existing SNN methods and significant efficiency gains, like x16 improvement in video semantic segmentation, over ANN methods.
• The findings suggest potential for efficient, low-energy video processing applications on neuromorphic hardware, such as autonomous systems and real-time analytics.

SpikeVideoFormer: An Efficient Spike-Driven Video Transformer

In the domain of machine learning, Spiking Neural Networks (SNNs) have emerged as a compelling alternative to traditional Artificial Neural Networks (ANNs), particularly due to their energy efficiency and ability to emulate biological neural processes. However, SNNs have primarily been explored in single-image tasks, lacking effective utilization in video-based vision environments. The paper "SpikeVideoFormer: An Efficient Spike-Driven Video Transformer with Hamming Attention and $\mathcal{O}(T)$ Complexity" addresses this limitation by introducing SpikeVideoFormer, a video Transformer grounded in SNN principles that leverages temporal and spatial efficiency through innovative architectural designs.

Overview

SpikeVideoFormer is posited as a transformative architecture that integrates SNNs within video processing contexts. The core novelties include the introduction of spike-driven Hamming attention (SDHA) and the simultaneous attention mechanisms across spatial and temporal domains. These innovations offer computational complexity advantageous for video tasks, specifically maintaining linear temporal complexity $\mathcal{O}(T)$. The paper elucidates the underpinnings of SDHA as a potent adaptation from real-valued attention to spike-driven attention, providing significant theoretical and empirical improvements over existing methods that employ dot-product attention.

Key Results

Empirical evaluation across several tasks such as video classification, human pose tracking, and video semantic segmentation demonstrate that SpikeVideoFormer achieves state-of-the-art performance vis-à-vis existing SNN-based approaches. Notable findings include:

• A 15% performance enhancement in video classification on tasks previously dominated by SNN architectures.
• Significant efficiency gains in video semantic segmentation, achieving up to $\times 16$ improvement compared to ANN methods.
• Demonstrated successful application in pose tracking scenarios, with markedly reduced computational overhead and improved fidelity in human posture estimation.

Assertions and Contributions

The authors propose that the spike-driven attention mechanisms proposed offer significant computational savings without a loss in functional accuracy. The specific alteration from dot-product to Hamming-based similarity in attention calculations addresses inherent limitations in spike-driven feature similarity estimations, confirmed through theoretical underpinning and benchmarks. Furthermore, the introduction of attention mechanisms distinct to SNNs presents a model accommodating both temporal and spatial information encoding—vital for video processing tasks.

Implications and Future Work

The implications of this research stretch into the practical deployments of neuromorphic hardware, highlighting its potential in applications that demand high efficiency and low energy consumption, such as autonomous systems and real-time video analytics. The demonstration of efficient processing within SpikeVideoFormer suggests potential advancements in real-world applications whereby energy-efficient models are paramount.

Future research is indicated in expanding the scope of SpikeVideoFormer to handle more complex tasks, exploring scaling mechanisms, and further optimizing the spike-driven computations within varied neuromorphic hardware settings. As spiking neuron technology evolves, the efficiencies demonstrated here will likely catalyze a broader shift toward implementing SNNs in diverse video-based ecosystems.

The paper provides a comprehensive exploration into leveraging SNNs for video tasks, presenting not only performance benefits but also critical insights into their theoretical application—thus marking a pivotal contribution to both machine learning performance and efficiency paradigms.