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。