Estimating Environmental Cost Throughout Model's Adaptive Life Cycle

TL;DR

This paper introduces PreIndex, a predictive tool that estimates the environmental and computational costs of retraining neural networks during distributional shifts, promoting sustainable AI practices.

Contribution

PreIndex is a novel, efficient index that estimates resource costs of model retraining with only one data pass, applicable across various models and data shifts.

Findings

PreIndex accurately predicts energy and carbon costs across datasets.

It correlates with training metrics like epochs and gradient norms.

PreIndex enables cost-effective model retraining decisions.

Abstract

With the rapid increase in the research, development, and application of neural networks in the current era, there is a proportional increase in the energy needed to train and use models. Crucially, this is accompanied by the increase in carbon emissions into the environment. A sustainable and socially beneficial approach to reducing the carbon footprint and rising energy demands associated with the modern age of AI/deep learning is the adaptive and continuous reuse of models with regard to changes in the environment of model deployment or variations/changes in the input data. In this paper, we propose PreIndex, a predictive index to estimate the environmental and compute resources associated with model retraining to distributional shifts in data. PreIndex can be used to estimate environmental costs such as carbon emissions and energy usage when retraining from current data distribution to new data distribution. It also correlates with and can be used to estimate other resource indicators associated with deep learning, such as epochs, gradient norm, and magnitude of model parameter change. PreIndex requires only one forward pass of the data, following which it provides a single concise value to estimate resources associated with retraining to the new distribution shifted data. We show that PreIndex can be reliably used across various datasets, model architectures, different types, and intensities of distribution shifts. Thus, PreIndex enables users to make informed decisions for retraining to different distribution shifts and determine the most cost-effective and sustainable option, allowing for the reuse of a model with a much smaller footprint in the environment. The code for this work is available here: https://github.com/JEKimLab/AIES2024PreIndex