Scalable Event-by-event Processing of Neuromorphic Sensory Signals With Deep State-Space Models

arXiv:2404.18508v1 Announce Type: new
Abstract: Event-based sensors are well suited for real-time processing due to their fast response times and encoding of the sensory data as successive temporal differences. These and other valuable properties, such as a high dynamic range, are suppressed when the data is converted to a frame-based format. However, most current methods either collapse events into frames or cannot scale up when processing the event data directly event-by-event. In this work, we address the key challenges of …

abstract arxiv cs.ai cs.lg cs.ne data differences dynamic encoding event neuromorphic processing real-time real-time processing scalable sensors sensory space state temporal type