With the rapid development of the Internet of Things (IoT) and communication technology, Deep Neural Network (DNN) applications like computer vision, can now be widely used in IoT devices. However, due to the insufficient memory, low computing capacity, and low battery capacity of IoT devices, it is difficult to support the high-efficiency DNN inference and meet users’ requirements for Quality of Service (QoS). Worse still, offloading failures may occur during the massive DNN data transmission due to the intermittent wireless connectivity between IoT devices and the cloud. In order to fill this gap, we consider the partitioning and offloading of the DNN model, and design a novel optimization method for parallel offloading of large-scale DNN models in a local-edge-cloud collaborative environment with limited resources. Combined with the coupling coordination degree and node balance degree, an improved Double Dueling Prioritized deep Q-Network (DDPQN) algorithm is proposed to obtain the DNN offloading strategy. Compared with existing algorithms, the DDPQN algorithm can obtain an efficient DNN offloading strategy with low delay, low energy consumption, and low cost under the premise of ensuring “delay-energy-cost” coordination and reasonable allocation of computing resources in a local-edge-cloud collaborative environment.

中文翻译：

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DDPQN：本地-边缘-云协作环境中的高效 DNN 卸载策略


随着物联网（IoT）和通信技术的快速发展，计算机视觉等深度神经网络（DNN）应用现在可以广泛应用于物联网设备中。然而，由于物联网设备内存不足、计算能力低、电池容量低，难以支持高效的DNN推理和满足用户对服务质量（QoS）的要求。更糟糕的是，由于物联网设备和云之间的无线连接间歇性，在海量 DNN 数据传输过程中可能会出现卸载失败。为了填补这一空白，我们考虑了 DNN 模型的划分和卸载，并设计了一种新颖的优化方法，用于在资源有限的本地-边缘-云协作环境中并行卸载大规模 DNN 模型。结合耦合协调度和节点平衡度，提出一种改进的双决优先深度Q网络（DDPQN）算法来获得DNN卸载策略。与现有算法相比，DDPQN算法在保证“延迟-能量”协调和本地边缘计算资源合理分配的前提下，可以获得低延迟、低能耗、低成本的高效DNN卸载策略。 -云协作环境。