Latency predictor cuts hardware-in-the-loop sampling overhead for neural architecture search
Hardware-aware neural architecture search remains bottlenecked by sequential latency measurement cycles on target devices. HiFi-LLP tackles this by replacing expensive hardware-in-the-loop feedback with a high-fidelity predictor that requires far fewer training samples than existing approaches while maintaining prediction accuracy. This matters because NAS efficiency directly impacts the viability of deploying optimized models to edge devices at scale. Reducing the sample overhead and prediction error compounds across the entire optimization pipeline, making it easier for practitioners to ship hardware-tailored architectures without prohibitive measurement costs.
Modelwire context
ExplainerHiFi-LLP's core claim is not just accuracy but sample efficiency: it requires far fewer training cycles on actual hardware to reach comparable prediction fidelity. The paper doesn't appear to claim a new architecture, but rather a data-efficient training regime for latency predictors, which is a narrower (and more testable) contribution than the summary might suggest.
This connects directly to the sample complexity thread from the Transformer theory paper published the same day. Both papers grapple with a shared problem: how many examples do you actually need to learn a useful model, rather than just whether learning is theoretically possible? HiFi-LLP applies that lens to hardware measurement cycles instead of training data, but the underlying question is identical. Where the Transformer work establishes theoretical bounds on learnability, HiFi-LLP tackles the empirical version for a different domain. The practical implication is similar too: understanding sample requirements prevents practitioners from over-investing in measurement or training regimes that hit diminishing returns.
If HiFi-LLP's predictor maintains sub-5% latency error on unseen hardware targets (mobile, edge accelerators) using fewer than 100 measurement samples, that validates the efficiency claim. If the paper only demonstrates this on the same device families used for training, the generalization story collapses and the contribution shrinks to a tuning exercise.
Coverage we drew on
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MentionsHiFi-LLP
Modelwire Editorial
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Modelwire summarizes, we don’t republish. arXiv cs.LG originally reported this story as “HiFi-LLP: High-Fidelity, Low-Cost Latency Predictors with Confidence for Robust HW-NAS”. The full content lives on arxiv.org. If you’re a publisher and want a different summarization policy for your work, see our takedown page.