## 1 Introduction  
Artificial Intelligence (AI) has been used commonly to achieve tasks by mimicking human problem-solving skills, learning from given data, and utilizing the learned skills to process future missions. Artificial neural network (ANN) is a sub-category of AI, which is used to solve applications that are difficult to be reached by the traditional problem-solving algorithm by leveraging approximation theorem. Thanks to the development of VLSI and Moore's law, the hardware resources make ANN possible to develop and become more complicated and powerful. This intricate, multiple-layered ANN is called deep learning neural network (DNN). DNN utilizes big data as the training source to generate a complex model for individual applications. Convolution neural network (CNN), a kind of DNN that mainly targets image-related processing, has shown its capability as it outperformed human ability regarding image object recognition [1]. CNN is commonly used by AI frameworks such as TensorFlow and Caffe in data center platforms. The platform typically runs on the central processing unit (CPU) and graphic processing unit (GPU) to train the big data for future inference. The applications utilizing CNN, such as autonomous vehicles, image recognition door locks, wearable devices, robotics, and remote sensing satellites, are seeking to be addressed in the edge scenario. The requirements for edge scenarios are real-time processing, power consumption awareness, and device size limitation. These applications are not suitable to be covered by the general-purpose processor (GPP) and GPU. GPP and GPU are general-purpose designs for sequential works and graphic processing due to their non-size, non-energy, and non-computation efficiency. Hence, edge-cloud coordination is proposed, which utilizes connections (e.g., the internet) to connect the cloud server and edge sensors to distribute the processing load and balance the system size. In this technique, the sensors collect and transfer the data to the cloud server for inference, which cleverly eliminates the computing load from the edge device. However, edge-cloud coordination technology has its downside for real-time applications. The edge-cloud coordination system, which utilizes the protocol that transfers the collected data back to the cloud server and gets the inference result back for a response, demands an internet connection and cannot run in areas without network coverage. Furthermore, while data privacy and security are an issue to be reckoned with (e.g., applying to face door locker), the edge-cloud coordination system shows the disadvantages. The edge-cloud coordination system might pay the penalty for the real-time computation due to the communication latency, which is not ideal for real-time devices (e.g., surveillance drones). As a result, edge AI accelerators are proposed to target edge AI devices to meet the unique edge scenario's requirements. Edge AI systems can be generally divided into two categories, power-size sensitive (PSS) and non-power-size sensitive (NPSS). Non-power-size sensitive edge AI systems include unmanned shops, surveillance systems, etc. These systems acquire power through fixed power supply facilities with a relatively extensive area to locate. Hence, NPSS systems do not urgently care about power consumption and system size but tend to be aware of data privacy. As a result, some NPSS systems also avoid using edge-cloud coordination platforms. This paper mainly targets PSS edge AI systems, but the proposed design can also be used on NPSS edge AI systems. Edge AI technology has the following advantages to deal with PSS and NPSS systems' demands. The benefits of the edge AI technology are:  
* Edge AI can improve the user experience regarding real-time data processing when AI technology is applied near customers. * Edge AI utilizes customized dataflow to pursue the compact size and manages power consumption to meet the mobility and limited power source. * Edge AI can run without internet coverage to offer privacy using local processing.