Multihardware Adaptive Latency Prediction for Neural Architecture Search

In hardware-aware neural architecture search (NAS), accurately assessing a model's inference efficiency is crucial for search optimization. Traditional approaches, which measure numerous samples to train proxy models, are impractical across varied platforms due to the extensive resources needed...

Full description

Saved in:
Bibliographic Details
Published in:IEEE internet of things journal 2025-02, Vol.12 (3), p.3385-3398
Main Authors: Lin, Chengmin, Yang, Pengfei, Wang, Quan, Guo, Yitong, Wang, Zhenyi
Format: Article
Language:English
Subjects:
Accuracy
Adaptation models
Adaptive sampling
Computer architecture
Data models
Dynamic sample allocation
few-shot learning
Hardware
hardware-aware
latency predictor
Network architecture
Network latency
neural architecture search (NAS)
Optimization
Parameter identification
Performance evaluation
Platforms
Predictive models
representative sample sampling
Sample size
Search process
Training
Citations: Items that this one cites
Online Access:Get full text
Tags: Add Tag
No Tags, Be the first to tag this record!
Description
Summary:In hardware-aware neural architecture search (NAS), accurately assessing a model's inference efficiency is crucial for search optimization. Traditional approaches, which measure numerous samples to train proxy models, are impractical across varied platforms due to the extensive resources needed to remeasure and rebuild models for each platform. To address this challenge, we propose a multihardware-aware NAS method that enhances the generalizability of proxy models across different platforms while reducing the required sample size. Our method introduces a multihardware adaptive latency prediction (MHLP) model that leverages one-hot encoding for hardware parameters and multihead attention mechanisms to effectively capture the intricate interplay between hardware attributes and network architecture features. Additionally, we implement a two-stage sampling mechanism based on probability density weighting to ensure the representativeness and diversity of the sample set. By adopting a dynamic sample allocation mechanism, our method can adjust the adaptive sample size according to the initial model state, providing stronger data support for devices with significant deviations. Evaluations on NAS benchmarks demonstrate the MHLP predictor's excellent generalization accuracy using only 10 samples, guiding the NAS search process to identify optimal network architectures.
ISSN:2327-4662
2327-4662
DOI:10.1109/JIOT.2024.3480990