Deep Neural Networks (DNNs) have become an essential and important supporting technology for smart Internet-of-Things (IoT) systems. Due to the high computational costs of large-scale DNNs, it might be infeasible to directly deploy them in energy-constrained IoT devices. Through offloading computation-intensive tasks to the cloud or edges, the computation offloading technology offers a feasible solution to execute DNNs. However, energy-efficient offloading for DNN based smart IoT systems with deadline constraints in the cloud-edge environments is still an open challenge. To address this challenge, we first design a new system energy consumption model, which takes into account the runtime, switching, and computing energy consumption of all participating servers (from both the cloud and edge) and IoT devices. Next, a novel energy-efficient offloading strategy based on a Self-adaptive Particle Swarm Optimization algorithm using the Genetic Algorithm operators (SPSO-GA) is proposed. This new strategy can efficiently make offloading decisions for DNN layers with layer partition operations, which can lessen the encoding dimension and improve the execution time of SPSO-GA. Simulation results demonstrate that the proposed strategy can significantly reduce energy consumption compared to other classic methods.

中文翻译：

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云边缘环境中基于 DNN 的智能物联网系统的节能卸载

深度神经网络 (DNN) 已成为智能物联网 (IoT) 系统必不可少的重要支撑技术。由于大规模 DNN 的计算成本很高，将它们直接部署在能源受限的物联网设备中可能是不可行的。通过将计算密集型任务卸载到云端或边缘，计算卸载技术为执行 DNN 提供了可行的解决方案。然而，在云边缘环境中具有截止日期限制的基于 DNN 的智能物联网系统的节能卸载仍然是一个开放的挑战。为了应对这一挑战，我们首先设计了一个新的系统能耗模型，该模型考虑了所有参与服务器（来自云和边缘）和物联网设备的运行时、交换和计算能耗。下一个，提出了一种基于使用遗传算法算子的自适应粒子群优化算法 (SPSO-GA) 的新型节能卸载策略。这种新策略可以通过层分区操作有效地为 DNN 层做出卸载决策，从而减少编码维度并提高 SPSO-GA 的执行时间。仿真结果表明，与其他经典方法相比，所提出的策略可以显着降低能耗。