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

arXiv:2402.10078v1 Announce Type: cross
Abstract: 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 …

abstract algorithm arxiv asynchronous become bio bio-inspired consumption cs.lg cs.ne data data processing eess.sp event framework integration low low power network neural network neuromorphic paradigm power power consumption processing representation sensors snn sparsity spiking neural network temporal type