Architecture Engineers Award

The rising environmental impact of deep learning has made sustainability an essential consideration in model design. While existing sustainable Neural Architecture Search (NAS) methods primarily focus on reducing the computational cost of the search process, they often overlook the substantial emissions generated by the final architectures during training and deployment. This oversight allows carbon-intensive models to persist even after an efficient search. To address this gap, this research emphasizes the need to embed long-term emission awareness directly into NAS objectives. Limitations of Conventional Sustainable NAS Approaches Traditional sustainability efforts in NAS mainly target the efficiency of the search algorithm itself, such as reducing GPU hours or computational overhead during architecture exploration. However, these methods neglect the broader lifecycle emissions of selected models, including training, fine-tuning, and large-scale deployment. This partial optimizati...