SEval-NAS: A Search-Agnostic Evaluation for Neural Architecture Search
Abstract
Neural architecture search (NAS) automates the discovery of neural networks that meet specified criteria, yet its evaluation procedures are often hardcoded, limiting the ability to introduce new metrics. This issue is especially pronounced in hardware-aware NAS, where objectives depend on target devices such as edge hardware. To address this limitation, we propose SEval-NAS, a metric-evaluation mechanism that converts architectures to strings, embeds them as vectors, and predicts performance metrics. Using NATS-Bench and HW-NAS-Bench, we evaluated accuracy, latency, and memory. Kendall's correlations showed stronger latency and memory predictions than accuracy, indicating the suitability of SEval-NAS as a hardware cost predictor. We further integrated SEval-NAS into FreeREA to evaluate metrics not originally included. The method successfully ranked FreeREA-generated architectures, maintained search time, and required minimal algorithmic changes. Our implementation is available at: https://github.com/Analytics-Everywhere-Lab/neural-architecture-search
Keywords
Cite
@article{arxiv.2603.00099,
title = {SEval-NAS: A Search-Agnostic Evaluation for Neural Architecture Search},
author = {Atah Nuh Mih and Jianzhou Wang and Truong Thanh Hung Nguyen and Hung Cao},
journal= {arXiv preprint arXiv:2603.00099},
year = {2026}
}
Comments
To be published in the Proceedings of The 41st ACM/SIGAPP Symposium on Applied Computing (SAC26)