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.

翻译：神经形态计算有望在能效方面相比传统冯·诺依曼计算范式实现数量级的提升。其目标是通过学习并模拟大脑功能，在材料、器件、电路、架构和算法等不同抽象层实现创新，从而开发出具备自适应、容错、低功耗、快速响应和低能耗特性的智能系统。随着复杂视觉任务能耗因数据集规模扩大而持续呈指数级增长，且资源受限的边缘设备日益普及，基于脉冲的神经形态计算方法有望成为当前主导视觉领域的深度卷积神经网络的有效替代方案。本章将系统介绍神经形态计算，阐述设计堆栈不同层级（器件、电路和算法）的若干代表性案例，并展望近期在计算机视觉领域具有发展前景的若干激动人心的应用方向与未来研究趋势。