mlsanityData to 14 Oct 2026

Rank Portability Does Not Imply Feasibility Portability: Target-Specific Evaluation of Joint Hardware Constraints

arXiv 2609.22122 ↗First posted 20 Aug 2026cs.LGcited by 0

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Abstract (arXiv)

Cross-device hardware evaluation often assumes that if architecture rankings transfer across devices, a proxy device can support target-side model selection. We stress-test this assumption for joint latency-energy feasibility across two public architecture families. On NAS-Bench-201, cross-device rank correlations are moderate, while target-comparable feasible-set overlap remains incomplete. A faithful AdaProxy diagnostic substantially improves latency ranking, showing that the observed boundary failures are not simply due to weak adaptation. Exact finite-sample split-conformal analysis also exposes an evidence bottleneck: a finite one-sided 90% threshold requires at least nine calibration observations. We then replicate the phenomenon on 10,000 GPT architectures across 13 HW-GPT-Bench devices. Relative to an RTX3080 proxy, target latency SRCC ranges from 0.951 to 0.996, yet proxy-reuse violation risk ranges from 33.3% to 100% under matched joint constraints. These results show that rank portability, feasibility portability, and target-specific decision support are distinct evaluation objects. Cross-device evaluations should therefore report which target environments actually support the operating point being claimed.

Key facts. No citing papers in the map yet (data through 14 Oct 2026). Builds on 10 of its 13 references that are in the map. First posted on arXiv 20 Aug 2026.

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10 of 13 references.

References · 10

  1. 2018MnasNet: Platform-Aware Neural Architecture Search for Mobilecited by 943
  2. 2019Once-for-All: Train One Network and Specialize it for Efficient Deploymentcited by 787
  3. 2018ProxylessNAS: Direct Neural Architecture Search on Target Task and Hardwarecited by 673
  4. 2020NAS-Bench-201: Extending the Scope of Reproducible Neural Architecture Searchcited by 416
  5. 2019SelectiveNet: A Deep Neural Network with an Integrated Reject Optioncited by 350
  6. 2021HW-NAS-Bench:Hardware-Aware Neural Architecture Search Benchmarkcited by 84
  7. 2021HELP: Hardware-Adaptive Efficient Latency Prediction for NAS via Meta-Learningcited by 16
  8. 2024HW-GPT-Bench: Hardware-Aware Architecture Benchmark for Language Modelscited by 10
  9. 2021One Proxy Device Is Enough for Hardware-Aware Neural Architecture Searchcited by 8
  10. 2023Multi-Predict: Few Shot Predictors For Efficient Neural Architecture Searchcited by 6

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