Power- and Deadline-Aware Dynamic Inference on Intermittent Computing Systems

In energy-harvesting intermittent computing systems, balancing power constraints with the need for timely and accurate inference remains a critical challenge. Existing methods often sacrifice significant accuracy or fail to adapt effectively to fluctuating power conditions. This paper presents DualAdaptNet, a power- and deadline-aware neural network architecture that dynamically adapts both its width and depth to ensure reliable inference under variable power conditions. Additionally, a runtime scheduling method is introduced to select an appropriate sub-network configuration based on real-time energy-harvesting conditions and system deadlines. Experimental results on the MNIST dataset demonstrate that our approach completes up to 7.0% more inference tasks within a specified deadline, while also improving average accuracy by 15.4% compared to the state-of-the-art.