Energy efficient machine learning models

Abstract

The present invention relates to energy efficient machine learning (ML) models, in particular to energy efficient ML model generation for energy efficient inferencing. According to an aspect of the invention, there is provided a computer implemented method of operating an ML training system for training a ML model. The method comprising: receiving an energy consumption (EC) target threshold for training; training an ML model based on a training dataset for an Epoch; estimating an EC of the ML training system whilst training said ML model over said Epoch; determining whether an accumulated EC estimate is below the EC target threshold; in response to determining the accumulated EC estimate is less than the EC target threshold, updating the training dataset for a new Epoch and repeating the training, estimating, and generating steps using the updated training dataset with said new Epoch until the ML model is validly trained; terminating training of the ML model in response to determining the accumulated EC meets or exceeds said EC target threshold; outputting said validly trained ML model and associated EC metrics. Further aspects of the invention includes a computer-implemented method of operating a ML training/inference system for performing EC benchmarking of the trained ML model for inferencing and a ML training and inferencing system for implementing said methods.

Dependencies are inferred from the claim text.