Enabling artificial intelligence directly on the device is difficult due to the limited computing power and small memory size of IoT devices. In this case, the quality of the Internet connection would not have a significant impact. The solution to this problem can be the execution of information processing using neural networks installed directly on IoT devices. However, stable network connections are not available everywhere, which is a limitation for meeting real-time requirements. Often, commercial smart IoT devices transfer information to the cloud for subsequent intelligent processing. Data processing of human health and physiological parameters from different sensors (heartrate monitoring, glucose monitoring, oxygen saturation, etc.) generally requires immediate processing. For example, security camera-based object-recognition tasks operate with detection intervals of 500 ms to capture and respond to target events. Traditionally, such applications operate in real time. Various artificial intelligence (AI) applications in the IoT field include smart healthcare services, smart agriculture, smart environment monitoring, smart exploration, and smart disaster rescue. In the age of neural networks and the Internet of Things (IoT), the search for new neural network architectures capable of operating on devices with limited computing power and small memory size is becoming an urgent agenda.
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