Resource-Efficient Adaptive-Network Inference Framework with Knowledge Distillation-based Unified Learning

Batteryless edge devices represent a promising avenue for sustainable computing, but are challenged by intermittent behavior and energy constraints. To address these issues, we propose a novel comprehensive approach integrating adaptive task module selection for intermittent computing paradigms. Our methodology incorporates the design of diverse task modules with varying sizes, precision levels, computational requirements, and energy consumption profiles, utilizing various compression techniques. These modules utilize a shared feature extractor that minimizes data movement and facilitates efficient checkpoint recovery, enhancing overall system robustness. In computing mode, the employed power-aware scheduler selects task modules based on performance requirements and available energy in the system. Subsequently, computation is performed to ensure optimal resource utilization while meeting application demands. We ensure optimal performance of these modules with proposed knowledge distillation-based unified learning. Quantitative evaluations on benchmark datasets-CIFAR-10, CIFAR-100, and Tiny-ImageNet-reveal that, with our proposed learning framework, designed models not only achieve improved performance metrics, including accuracy increases of 1.47%, 2.44%, and 3.70% for each dataset respectively but also enhance energy efficiency. These results validate our comprehensive and synergistic approach, demonstrating significant gains in both performance and resource optimization.