Abstract: Recently, with the rapid growth of big data and hardware capabilities, artificial intelligence (AI) has achieved significant development. Artificial neural networks (ANNs) have been successfully applied to solve numerous problems in academia and industry. However, deploying AI networks on edge devices remains challenging. These scenarios generally have strict limitations on power and size, while also have high requirements for system latency and real-time performance. Thus, building low-power AI computing systems involves making trade-offs among performance, power, and size. This article reviews the current state of low-power AI computing systems, introduces low-power AI computing hardware and software tools, elaborates on existing technical challenges, discusses system evaluation methods and metrics, and looks to future development trends.