Minimizing Energy Consumption of Deep Learning Models by Energy-Aware Training

• URL: http://arxiv.org/abs/2307.00368v1
• Date: Sat, 1 Jul 2023 15:44:01 GMT
• Title: Minimizing Energy Consumption of Deep Learning Models by Energy-Aware Training
• Authors: Dario Lazzaro, Antonio Emanuele Cin\`a, Maura Pintor, Ambra Demontis, Battista Biggio, Fabio Roli, Marcello Pelillo
• Abstract summary: We propose EAT, a gradient-based algorithm that aims to reduce energy consumption during model training. We demonstrate that our energy-aware training algorithm EAT is able to train networks with a better trade-off between classification performance and energy efficiency.
• Score: 26.438415753870917
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Deep learning models undergo a significant increase in the number of parameters they possess, leading to the execution of a larger number of operations during inference. This expansion significantly contributes to higher energy consumption and prediction latency. In this work, we propose EAT, a gradient-based algorithm that aims to reduce energy consumption during model training. To this end, we leverage a differentiable approximation of the $\ell_0$ norm, and use it as a sparse penalty over the training loss. Through our experimental analysis conducted on three datasets and two deep neural networks, we demonstrate that our energy-aware training algorithm EAT is able to train networks with a better trade-off between classification performance and energy efficiency.

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