Papers › KNAS: Green Neural Architecture Search
KNAS: Green Neural Architecture Search
Jingjing Xu, Liang Zhao, Junyang Lin, Rundong Gao, Xu sun, Hongxia Yang
Many existing neural architecture search (NAS) solutions rely on downstream training for architecture evaluation, which takes enormous computations. Considering that these computations bring a large carbon footprint, this paper aims to explore a green (namely environmental-friendly) NAS solution that evaluates architectures without training. Intuitively, gradients, induced by the architecture itself, directly decide the convergence and generalization results. It motivates us to propose the gradient kernel hypothesis: Gradients can be used as a coarse-grained proxy of downstream training to evaluate random-initialized networks. To support the hypothesis, we conduct a theoretical analysis and find a practical gradient kernel that has good correlations with training loss and validation performance. According to this hypothesis, we propose a new kernel based architecture search approach KNAS. Experiments show that KNAS achieves competitive results with orders of magnitude faster than "train-then-test" paradigms on image classification tasks. Furthermore, the extremely low search cost enables its wide applications. The searched network also outperforms strong baseline RoBERTA-large on two text classification tasks. Codes are available at \url{https://github.com/Jingjing-NLP/KNAS} .
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Code
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Tasks
Results from the paper archive 2025-07-28
| Task | Dataset | Model | Metric | Value | Rank at snapshot | Leaderboard | Report |
|---|---|---|---|---|---|---|---|
| Neural Architecture Search | NAS-Bench-201, CIFAR-10 | KNAS (k=40) | Accuracy (Test) | 93.43 | #24 of 37 | Archive leaderboard | report |
| Neural Architecture Search | NAS-Bench-201, CIFAR-100 | KNAS (k=40) | Accuracy (Test) | 71.05 | #23 of 40 | Archive leaderboard | report |
| Neural Architecture Search | NAS-Bench-201, ImageNet-16-120 | KNAS (k=40) | Accuracy (Test) | 45.05 | #30 of 49 | Archive leaderboard | report |
| Neural Architecture Search | NATS-Bench Topology, CIFAR-10 | KNAS (Xu et al., 2021) | Test Accuracy | 93.05 | #8 of 11 | Archive leaderboard | report |
| Neural Architecture Search | NATS-Bench Topology, CIFAR-100 | KNAS (Xu et al., 2021) | Test Accuracy | 68.91 | #8 of 11 | Archive leaderboard | report |
| Neural Architecture Search | NATS-Bench Topology, ImageNet16-120 | KNAS (Xu et al., 2021) | Test Accuracy | 34.11 | #8 of 11 | Archive leaderboard | report |
Ranks are positions in the archive's leaderboards as they stood at the 2025-07-28 snapshot. Results published since then are not among these rows, so a rank here is not a current standing.
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