Details
Original language | English |
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Title of host publication | 5th Workshop on practical ML for limited/low resource settings |
Publication status | E-pub ahead of print - 2 Apr 2024 |
Abstract
Keywords
- cs.LG, cs.AI
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5th Workshop on practical ML for limited/low resource settings. 2024.
Research output: Chapter in book/report/conference proceeding › Conference contribution › Research › peer review
}
TY - GEN
T1 - Towards Leveraging AutoML for Sustainable Deep Learning
T2 - A Multi-Objective HPO Approach on Deep Shift Neural Networks
AU - Hennig, Leona
AU - Tornede, Tanja
AU - Lindauer, Marius
PY - 2024/4/2
Y1 - 2024/4/2
N2 - 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.
AB - 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.
KW - cs.LG
KW - cs.AI
U2 - https://doi.org/10.48550/arXiv.2404.01965
DO - https://doi.org/10.48550/arXiv.2404.01965
M3 - Conference contribution
BT - 5th Workshop on practical ML for limited/low resource settings
ER -