Greening Edge AI: Optimizing Inference Accuracy and Reducing Carbon Emissions With Renewable Energy

Machine-learning (ML) inference services are vital for artificial intelligence (AI) applications on edge devices. However, there are challenges to balance between maintaining high inference accuracy and minimizing carbon emissions. To foster green AI services, we introduce a novel framework that taps into green energy sources, empowering Internet of Things (IoT) devices to execute inference tasks at the network’s edge. In the context of multitask concurrent scheduling, our methodology seeks to simultaneously optimize both the inference accuracy and carbon footprint of all inference tasks. To support this endeavor, we propose a unique incentive constraint that ensures equitable allocation of computing resources for IoT devices equipped with energy harvesting modules, thereby promoting efficient collaboration. Furthermore, recognizing the system’s dynamic nature and fluctuating availability of green energy, we introduce the carbon-aware online inference task offloading algorithm (OTOA). This algorithm, anchored in the Lyapunov optimization method, leverages a graph-based offloading strategy, amalgamating elements from the two-sided matching method, to derive an approximate optimal solution using real-time data. Our meticulous theoretical evaluations and in-depth experiments with real-world datasets affirm OTOA’s superiority over existing approaches.