EventF2S: Asynchronous and Sparse Spiking AER Framework using
  Neuromorphic-Friendly Algorithm
Lakshmi Annamalai Chetan Singh Thakur
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.