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As the product of the combination of edge computing and artificial intelligence, edge intelligence (EI) not only solves the problem of insufficient computing capacity of the end device, but also can provide users with various types of intelligent services. However, offline and online model partitioning methods respectively have problems of poor adaptability to the real computing environment and delayed feedback. In addition, previous work on optimizing energy consumption through model partitioning often ignores the latency of intelligent services. Similarly, the energy consumption of end devices and edge servers is usually not considered when optimizing latency. Therefore, we propose game-based adaptive floating-point operations and partition point decision mechanism (GAFPD) to efficiently find the optimal partition point that reduces latency and improves energy efficiency simultaneously in a dynamically changing computing environment. Numerous simulation experiments and robot-based EI system experiments show that GAFPD can simultaneously reduce the latency of intelligent services and improve the energy efficiency of edge devices, while exhibiting strong adaptability to bandwidth changes.

Game-based Adaptive FLOPs and Partition Point Decision Mechanism with Latency and Energy-Efficient Tradeoff for Edge Intelligence