  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 ubiquitous, spike-based
neuromorphic computing approaches can be viable alternative to deep
convolutional neural network that is dominating the vision field today. In this
book chapter, we introduce neuromorphic computing, outline a few representative
examples from different layers of the design stack (devices, circuits and
algorithms) and conclude with a few exciting applications and future research
directions that seem promising for computer vision in the near future.
