Resource Allocation for Task-Oriented Generative Artificial Intelligence in Internet of Things

The implementation of the Internet of Things (IoT) technology has the potential to unleash the capabilities of generative artificial intelligence (GAI). However, integrating GAI with IoT introduces a significant challenge in managing the limited resources of edge networks. In this article, we propose a resource optimization framework for GAI in IoT systems to address this issue, leveraging a heterogeneous computing framework. We focus on the system utility maximization problem, which jointly optimizes transmit power, heterogeneous computing allocation, CPU-cycle frequency, GPU-cycle frequency, and task scheduling under the latency constraint. The optimal CPU-cycle frequency, GPU-cycle frequency, and computing allocation are obtained by employing data parallelism analysis. In particular, we develop a hierarchical soft actor-critic with an intrinsic curiosity (HSAC-IC) algorithm to determine the task scheduling strategy. The HSAC-IC algorithm utilizes a hierarchical strategy structure and an intrinsic curiosity module (ICM) to improve learning efficiency and performance, particularly in environments characterized by sparse rewards, high-dimensional action spaces, and complex tasks. Our simulations benchmark the HSAC-IC algorithm against two existing deep reinforcement learning (DRL) algorithms and three reference schemes. The results illustrate that our scheme significantly outperforms these alternatives, ensuring AIGC user service requirements, while minimizing service generation costs, and optimizing resource allocation by configuring the image quality strategy on edge servers.