类脑视觉注意力用于SpiNNaker上的手语识别
Neuromorphic visual attention for Sign-language recognition on SpiNNaker
AI总结：
本文提出一种端到端的类脑架构，用于手语识别，结合了类脑视觉注意力机制和紧凑的类脑神经网络，实现高效能和低功耗的手语识别。
摘要：
近年来，手语识别在分类准确率上取得了显著进步；然而，现有方法的延迟和功耗限制了其在实时部署中的适用性。类脑传感和处理提供了一种基于稀疏、事件驱动计算的替代范式，支持低延迟和节能感知。本文介绍了一种端到端的类脑架构，用于美国手语（ASL）手指拼写识别，该架构集成了用于在线感兴趣区域提取的脉冲视觉注意力机制和部署在SpiNNaker类脑平台上的紧凑脉冲神经网络。我们对两个数据集进行了基准测试：一个合成的基于事件的手语MNIST数据集和一个原生记录的ASL-DVS数据集，同时提供了手语识别及相关工作的全面概述。这项工作在模拟中实现了92.27%的性能，并在类脑硬件部署中实现了83.1%的性能，同时在所有基准方法中实现了最节能的架构（0.565 mW）和低延迟（3 ms）。尽管设计紧凑，该系统展示了任务相关的视觉注意力应用在边缘部署中的适用性。
英文摘要：
Sign-language recognition has achieved substantial gains in classification accuracy in recent years; however, the latency and power requirements of most existing methods limit their suitability for real-time deployment. Neuromorphic sensing and processing offer an alternative paradigm based on sparse, event-driven computation that supports low-latency and energy-efficient perception. In this work, we introduce an end-to-end neuromorphic architecture for American Sign Language (ASL) fingerspelling recognition that integrates a spiking visual attention mechanism for online region-of-interest extraction with a compact spiking neural network deployed on the SpiNNaker neuromorphic platform. We benchmark the proposed system against two datasets: a synthetically generated event-based version of the Sign Language MNIST dataset and a natively recorded ASL-DVS dataset, whilst providing a comprehensive overview of Sign-language recognition and related work. This work yields competitive performance in simulation (92.27%) and comparable performance on neuromorphic hardware deployment (83.1%), while achieving the most energy-efficient architecture (0.565 mW) and low latency (3 ms) across all benchmarked approaches. Despite its compact design, the system demonstrates the suitability of task-dependent visual attention applications for edge deployment.
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