CAMEO: Compressive Energy-Aware Multi-Task Deep Inference in Resource-Constrained Energy Harvesting Systems

Multi-task Convolutional Neural Network (CNN) inference on Energy Harvesting (EH) devices has received limited attention, particularly in scenarios where energy availability fluctuates significantly. This poses substantial implementation challenges, especially in deploying and running inference under varying energy conditions while performing multiple correlated tasks. In this work, we present an end-to-end framework that enables multi-task inference to inherently conserve computation under dynamic energy conditions. To facilitate this adaptation in multi-task settings, we propose inter-task and intra-task shared-weight design strategies. The inter-task design removes redundancy across related tasks, while the intra-task design targets redundancy within weight matrices. Finally, to enable efficient inference on resource-constrained devices, we compress the model and propose novel algorithms that support energy-aware adaptation and compression-tolerant computation without expensive weight reconstruction. Experimental results show that our proposed framework can enable up to 1.8x inference latency reduction, 48% energy efficiency improvement, and 3.5x memory conservation.