ENERGIZE: A Neuroevolution Framework for Energy-Efficient Machine Learning

The increasing deployment of Artificial Intelligence across various domains has led to a significant rise in power consumption, raising environmental concerns, and highlighting the need for energy-efficient algorithms and hardware. Machine Learning models – particularly Deep Convolutional Neural Networks and Large Language Models – demand substantial computational resources, contributing to higher carbon emissions and reduced sustainability. This work tackles the issue of energy consumption in Machine Learning, with a specific focus on inference. The proposed methodology leverages Neuroevolution to construct effective models while minimizing power usage. This work proposes a novel approach that trains two models simultaneously in a single process, explicitly encouraging one to consume less power without substantially compromising accuracy. It also proposes a mutation strategy that reinserts layer modules with a preference for power-efficient components. This approach is validated in two scenarios using the Fashion-MNIST, CIFAR-10, and CIFAR-100 datasets: (i) evolving models from scratch, and (ii) optimizing pre-trained models for energy efficiency. When evolving from scratch, this method reduces power consumption by up to 49% with only a 2% accuracy drop on Fashion-MNIST, achieves a 20% power reduction on CIFAR-10 while improving accuracy by 0.8%, and enhances accuracy by 12.8% on CIFAR-100 while reducing power usage by 4%. For pre-trained models, this work achieves a 19.8% reduction in power usage on Fashion-MNIST with minimal accuracy loss, a 47% reduction on CIFAR-10 at the cost of a 7.7% drop in accuracy, and a 21.2% power saving on CIFAR-100 despite a 27.2% performance decline.