Joint DNN partitioning and resource allocation for completion rate maximization of delay-aware DNN inference tasks in wireless powered mobile edge computing

With the development of smart Internet of Things (IoT), it has seen a surge in wireless devices deploying Deep Neural Network (DNN) models for real-time computing tasks. However, the inherent resource and energy constraints of wireless devices make local completion of real-time inference tasks impractical. DNN model partitioning can partition the DNN model and use edge servers to assist in completing DNN model inference tasks, but offloading also requires a lot of transmission energy consumption. Additionally, the complex structure of DNN models means partitioning and offloading across different network layers impacts overall energy consumption significantly, complicating the development of an optimal partitioning strategy. Furthermore, in certain application contexts, regular battery charging or replacement for smart IoT devices is impractical and environmentally harmful. The development of wireless energy transfer technology enables devices to obtain RF energy through wireless transmission to achieve sustainable power supply. Motivated by this, We proposes a problem of joint DNN model partition and resource allocation in Wireless Powered Edge Computing (WPMEC). However, time-varying channel state in the WPMEC have a significant impact on resource allocation decisions. How to jointly optimize DNN model partition and resource allocation decisions is also a significant challenge. We proposes an online algorithm based on Deep Reinforcement Learning (DRL) to solve the time allocation decision, simplifying a Mixed Integer Nonlinear Problem (MINLP) into a convex optimization problem. Our approach seeks to maximize the completion rate of DNN inference tasks within the constraints of time-varying wireless channel states and delay constraints. Simulation results show the exceptional performance of this algorithm in enhancing task completion rates.