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Artificial Intelligence of Things (AIoT) which integrates artificial intelligence (AI) and the Internet of Things (IoT), has attracted increasing attention recently. With the remarkable development of AI, deep neural networks (DNNs) have achieved great success from research to deployment in many applications. However, deploying complex and state-of-the-art (SOTA) AI models on edge applications is increasingly a big challenge. This paper focuses on the convergence of AIoT-based edge devices, lightweight DNNs, and neural network compression. We provide a comprehensive analysis of them and many practical suggestions for researchers: how to obtain/design lightweight DNNs, how to select suitable AI edge devices, and how to compress and deploy them in practice. Finally, future trends and opportunities are presented, including trustworthy AI, federated learning, and robust deployment.
Sun et al. (2022) studied this question.