Spike-based Neuromorphic Computing for Next-Generation Computer Vision

arXiv:2310.09692v2 Announce Type: replace-cross
Abstract: Neuromorphic Computing promises orders of magnitude improvement in energy efficiency compared to traditional von Neumann computing paradigm. The goal is to develop an adaptive, fault-tolerant, low-footprint, fast, low-energy intelligent system by learning and emulating brain functionality which can be realized through innovation in different abstraction layers including material, device, circuit, architecture and algorithm. As the energy consumption in complex vision tasks keep increasing exponentially due to larger data set and resource-constrained edge devices become increasingly …

abstract abstraction arxiv brain computer computer vision computing cs.ai cs.et cs.lg cs.ne eess.iv efficiency energy energy efficiency improvement innovation intelligent low low-energy material neuromorphic neuromorphic computing next orders paradigm through type vision