Sustainability-Integrated Neural Architecture Search with ZEP-NAS
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 optimization leads to architectures that may be computationally heavy, environmentally costly, and ultimately misaligned with sustainability goals.
Challenges in Emission-Based Optimization for NAS
Optimizing NAS with carbon-aware metrics is challenging due to the difficulty of accurately attributing real-time emissions to individual architecture trials. In large-scale NAS experiments, multiple candidate models run in parallel on shared GPU clusters, making fine-grained emission tracking nearly impossible. Without precise attribution, integrating sustainability into objective functions becomes unreliable and prevents environmentally fair optimization.
ZEP-NAS Framework: Zero-Cost Emission Proxy Estimation
The proposed ZEP-NAS framework introduces a Transformer-based in-context learning mechanism that estimates emissions in real time without requiring direct measurement from hardware-level tools. This “zero-cost emission proxy” enables scalable, parallel search while incorporating environmental impact directly into architecture evaluation. By predicting emissions with negligible overhead, ZEP-NAS maintains the speed and flexibility required for modern NAS workflows.
Unified Objective Balancing Performance and Environmental Efficiency
ZEP-NAS optimizes a unified objective function that co-balances accuracy and sustainability, ensuring that discovered models deliver strong predictive performance while minimizing carbon footprint. This integrated formulation enables the search algorithm to avoid architectures that are unnecessarily large or computationally intensive, promoting models that remain efficient during training and deployment without compromising task quality.
Experimental Validation and Emission Reduction Outcomes
Experiments across multiple Computer Vision and NLP datasets demonstrate that ZEP-NAS achieves significant emission reductions—up to 43%—with minimal degradation in test accuracy. The approach maintains near-linear scalability as search job counts increase, confirming its suitability for large-scale NAS pipelines. These findings underscore the importance of embedding sustainability into core NAS objectives, enabling the discovery of models that are both accurate and environmentally responsible.
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