Rethinking Machine Learning For Power

The power consumed by machine learning is exploding, and while advances are being made in reducing the power consumed by them, model sizes and training sets are increasing even faster. Even with the introduction of fabrication technology advances, specialized architectures, and the application of optimization techniques, the trend is disturbing. Couple that with the explosion in edge devices that are adding increasing amounts of intelligence and it becomes clear that something dramatic has to happen. The right answer is not to increase the world's energy production. It is to use what we have more wisely. The industry has to start taking total energy consumed by a machine learning application seriously, and that must include asking the question, 'Is the result worth the power expenditure?'