An Energy-Efficient Partition and Offloading Method for Multi-DNN Applications in Edge-End Collaboration Environments

Zhiqing Yang, Xiang He, Teng Wang, Zhongjie Wang

Abstract: Deep Neural Networks (DNNs) have emerged as the preferred solution for Internet of Things (IoT) applications, owing to their remarkable performance capabilities. However, the inherent complexity of DNNs presents significant challenges for IoT devices that are constrained by limited computational power and battery life. To adeptly navigate the demands of intricate inference tasks, edge computing is leveraged, enabling collaborative inference of DNNs between IoT devices and edge servers. However, existing research rarely focus simultaneously on the power consumption of IoT devices, the latency of collaborative inference and the cost of edge servers. Moreover, current research seldom takes into account the deployment of multiple DNN applications on IoT devices, a critical factor for adapting to increasingly complex edge-end collaborative environments. This research focuses on optimizing the inference power consumption of multiple DNN applications deployed on IoT devices in larger-scale edge-end collaboration environments, under the constraints of maximum End-to-End latency and the cost of edge servers. To address this issue, we propose the Greedy Genetic Algorithm, which leverages a combination of greedy strategy and Genetic Algorithm. The performance of our proposed method is extensively evaluated through experiments, demonstrating its superiority in achieving lower inference power consumption with fewer iterations compared to existing solutions.