Abstract
Abstract. The introduction of Artificial Intelligence (AI) will provide great opportunities and pose significant challenges in Internet of Things (IoT) systems. IoT devices can create huge amount of data in real-time. Meanwhile, it is crucial to handle immediate processing and decision-making in IoT applications. However, IoT devices are usually constrained by limited computation power, memory, and energy compared to traditional devices, making the deployment of efficient AI algorithms a challenging task. In this paper, we present different algorithmic strategies to overcome the above problems, including model compression, quantization, and hardware accelerators. On the other hand, we also focus on the role of decentralization and edge computing architectures to increase the scalability and performance of AI-IoT systems. Finally, this paper also reviews energy-efficient AI algorithms and latency reduction methods to make the decision process real-time for various IoT applications.
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