Researchers have developed a method to optimize machine learning algorithms for low energy consumption while maintaining their performance. The approach, called Constrained Bayesian Optimization (CBO), uses a constrained optimization framework that balances energy efficiency with predictive performance. CBO was tested on regression and classification tasks and found to reduce energy consumption without sacrificing model accuracy. --- Why it matters: This matters because as machine learning models grow in size, the energy required for training becomes increasingly important. Engineers can use this method to optimize their models for low-energy operation, which is crucial for applications where power consumption is a concern, such as edge devices or autonomous systems. Source: https://arxiv.org/abs/2407.05788