Bio-inspired Address Event Representation (AER) sensors have attracted
significant popularity owing to their low power consumption, high sparsity, and
high temporal resolution. Spiking Neural Network (SNN) has become the inherent
choice for AER data processing. However, the integration of the AER-SNN
paradigm has not adequately explored asynchronous processing, neuromorphic
compatibility, and sparse spiking, which are the key requirements of
resource-constrained applications. To address this gap, we introduce a
brain-inspired AER-SNN object recognition solution, which includes a data
encoder integrated with a First-To-Spike recognition network. Being fascinated
by the functionality of neurons in the visual cortex, we designed the solution
to be asynchronous and compatible with neuromorphic hardware. Furthermore, we
have adapted the principle of denoising and First-To-Spike coding to achieve
optimal spike signaling, significantly reducing computation costs. Experimental
evaluation has demonstrated that the proposed method incurs significantly less
computation cost to achieve state-of-the-art competitive accuracy. Overall, the
proposed solution offers an asynchronous and cost-effective AER recognition
system that harnesses the full potential of AER sensors.

利用神经元在视皮层的功能特性，我们介绍了一种异步且与神经形态硬件兼容的 AER-SNN 对象识别解决方案，该解决方案采用数据编码器与首次发放脉冲识别网络集成，通过降噪和首次发放编码的原则实现了最佳脉冲信号传递，显著降低计算成本，并通过实验证明，所提出的方法在获得最先进的准确性的同时大大降低了计算成本。该解决方案为异步和具有成本效益的 AER 识别系统，充分发挥了 AER 传感器的潜力。

EventF2S：基于神经形态友好算法的异步稀疏脉冲 AER 框架

EventF2S: Asynchronous and Sparse Spiking AER Framework using  Neuromorphic-Friendly Algorithm

Event-based camera is a bio-inspired vision sensor that records intensity
changes (called event) asynchronously in each pixel. As an instance of
event-based camera, Dynamic and Active-pixel Vision Sensor (DAVIS) combines a
standard camera and an event-based camera. However, traditional models could
not deal with the event stream asynchronously. To analyze the event stream
asynchronously, most existing approaches accumulate events within a certain
time interval and treat the accumulated events as a synchronous frame, which
wastes the intensity change information and weakens the advantages of DAVIS.
Therefore, in this paper, we present the first neural asynchronous approach to
process event stream for event-based camera. Our method asynchronously extracts
dynamic information from events by leveraging previous motion and critical
features of gray-scale frames. To our best knowledge, this is the first neural
asynchronous method to analyze event stream through a novel deep neural
network. Extensive experiments demonstrate that our proposed model achieves
remarkable improvements against the state-of-the-art baselines.

该论文介绍了一种基于神经网络的异步处理 Event-based Camera 事件流的方法，利用前一时刻的动态信息和灰度帧的关键特征，提取事件中的动态信息，相较现有同类研究结果表现优异。