**1. Introduction**

Neuromorphic computing, technology that mimic neuro-biological architectures present in the nervous system using electronic circuits, has attracted attention as next-generation computing due to its characteristics as they can process complex data with high efficiency, high speed and low power consumption. Neuromorphic computing importance increased in the industry because it efficiently executes artificial intelligence algorithms by imitating the brain nerve structure of humans. The conventional von Neumann computing using separated processors and memory systems is not efficient for machine learning due to processor-memory bottlenecks. Because the machine learning has a special workload that iterates simple computation with alot of data, there should be huge data traffic between processors and memory. For neuromorphic computing system, it consists of multiple neurons and synapses to compute and store data, and a neural network to communicate them. Therefore, this computing system can compute simple iterations efficiently for the training of machine learning. This chapter will discuss neuromorphic computing goals and challenges, showing how materials, devices, algorithms development researches lead to high expectations in neuromorphic computing field. We will discuss neuromorphic systems that are already existing today and expectations for what neuromorphic computing can achieve in the future.
