EENAS: Zero-Shot Energy-Efficiency-Aware Neural Architecture Search

Despite the critical need for sustainable deep learning, model energy efficiency remains largely neglected in Hardware-Aware Neural Architecture Search (HW-NAS). Discovering energy-efficient models is severely bottlenecked by costly on-device measurements and the brittleness of training-free accuracy proxies, which often yield unbalanced "glass cannon" architectures. We propose Energy-Efficient Neural Architecture Search (EENAS), a strictly zero-shot HW-NAS framework. EENAS introduces a PCA-based anomaly router that dispatches tasks to decision trees or tabular transformers, achieving <10\% MAPE zero-shot energy prediction on entirely unseen GPUs. Simultaneously, we robustly estimate accuracy by ensembling training-free proxies via a novel Log Z-score aggregation. Combining these predictors unlocks Absolute Hardware Bounding, discovering Pareto-optimal architectures for strict energy budgets entirely offline in under 30 minutes. Evaluated on ImageNet-1K, our models achieve 40\% less energy consumption at comparable accuracy, or a 2.5\% accuracy improvement under the same energy budget compared to efficient baselines.