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：神经形态计算有望相比传统冯·诺依曼计算范式实现数量级的能效提升。其目标是通过学习与模拟大脑功能，开发出具备自适应、容错、低功耗、快速且节能的智能系统，这可通过材料、器件、电路、架构与算法等不同抽象层的创新实现。随着复杂视觉任务因数据集扩大而能耗呈指数级增长，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可行的替代方案。在本章中，我们将介绍神经形态计算，概述设计堆栈不同层（器件、电路与算法）的若干代表性实例，并总结几项有望在近期内应用于计算机视觉的激动人心的应用与未来研究方向。