Towards Leveraging AutoML for Sustainable Deep Learning

A Multi-Objective HPO Approach on Deep Shift Neural Networks

authored by: Leona Hennig, Tanja Tornede, Marius Lindauer
Abstract: Deep Learning (DL) has advanced various fields by extracting complex patterns from large datasets. However, the computational demands of DL models pose environmental and resource challenges. Deep shift neural networks (DSNNs) offer a solution by leveraging shift operations to reduce computational complexity at inference. Following the insights from standard DNNs, we are interested in leveraging the full potential of DSNNs by means of AutoML techniques. We study the impact of hyperparameter optimization (HPO) to maximize DSNN performance while minimizing resource consumption. Since this combines multi-objective (MO) optimization with accuracy and energy consumption as potentially complementary objectives, we propose to combine state-of-the-art multi-fidelity (MF) HPO with multi-objective optimization. Experimental results demonstrate the effectiveness of our approach, resulting in models with over 80\% in accuracy and low computational cost. Overall, our method accelerates efficient model development while enabling sustainable AI applications.
Organisation(s): Institute of Artificial Intelligence
Machine Learning Section
Type: Conference contribution
Publication date: 02.04.2024
Publication status: E-pub ahead of print
Peer reviewed: Yes
Electronic version(s): https://doi.org/10.48550/arXiv.2404.01965 (Access: Open)

BibTeX

@inproceedings{a7d2e4c6b76f474e9e7bdd8159a70e99,
title = "Towards Leveraging AutoML for Sustainable Deep Learning: A Multi-Objective HPO Approach on Deep Shift Neural Networks",
abstract = "Deep Learning (DL) has advanced various fields by extracting complex patterns from large datasets. However, the computational demands of DL models pose environmental and resource challenges. Deep shift neural networks (DSNNs) offer a solution by leveraging shift operations to reduce computational complexity at inference. Following the insights from standard DNNs, we are interested in leveraging the full potential of DSNNs by means of AutoML techniques. We study the impact of hyperparameter optimization (HPO) to maximize DSNN performance while minimizing resource consumption. Since this combines multi-objective (MO) optimization with accuracy and energy consumption as potentially complementary objectives, we propose to combine state-of-the-art multi-fidelity (MF) HPO with multi-objective optimization. Experimental results demonstrate the effectiveness of our approach, resulting in models with over 80\% in accuracy and low computational cost. Overall, our method accelerates efficient model development while enabling sustainable AI applications.",
keywords = "cs.LG, cs.AI",
author = "Leona Hennig and Tanja Tornede and Marius Lindauer",
year = "2024",
month = apr,
day = "2",
doi = "10.48550/arXiv.2404.01965",
language = "English",
booktitle = "5th Workshop on practical ML for limited/low resource settings",
}