A fully event-based image processing pipeline containing neuromorphic vision sensors and spiking neural network has the potential to achieve high throughput, low latency and high dynamic range vision processing. In this work, we present an end-to-end SNN unsupervised learning inference framework to achieve near-real-time processing performance. The design uses fully event-driven operations that significantly improve learning and inference speed: over $100\times $ increase of inference throughput on CPU and near-real-time inference on GPU for neuromorphic vision sensors can be achieved. The event-driven processing method supports unsupervised spike-timing-dependent plasticity learning of convolutional SNN. When labels are limited, it achieves higher accuracy than supervised training approaches. In addition, the proposed method improves robustness for low-precision SNN as it reduces spiking activity distortion and achieves higher learning accuracy than regular discrete-time simulated low-precision networks.

中文翻译：

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SPEED：具有事件驱动的无监督学习和基于事件的视觉的近实时推理的尖峰神经网络

包含神经形态视觉传感器和尖峰神经网络的完全基于事件的图像处理管道具有实现高吞吐量、低延迟和高动态范围视觉处理的潜力。在这项工作中，我们提出了一个端到端的 SNN 无监督学习推理框架，以实现近乎实时的处理性能。该设计使用完全事件驱动的操作，可显着提高学习和推理速度：超过 $100\次 $ 可以实现神经形态视觉传感器在 CPU 上的推理吞吐量和 GPU 上近实时推理的增加。事件驱动的处理方法支持卷积 SNN 的无监督尖峰时间依赖可塑性学习。当标签有限时，它比监督训练方法获得更高的准确性。此外，所提出的方法提高了低精度 SNN 的鲁棒性，因为它减少了尖峰活动失真并实现了比常规离散时间模拟低精度网络更高的学习精度。