Accurate reporting of energy and carbon usage is essential for understanding the potential
climate impacts of machine learning research. We introduce a framework that makes this …

From an environmental standpoint, there are a few crucial aspects of training a neural
network that have a major impact on the quantity of carbon that it emits. These factors …

Machine learning (ML) workloads have rapidly grown, raising concerns about their carbon
footprint. We show four best practices to reduce ML training energy and carbon dioxide …

The carbon footprint of algorithms must be measured and transparently reported so
computer scientists can take an honest and active role in environmental sustainability. In this …

With the exponential growth of computational power and the availability of large-scale
datasets in recent years, remarkable advancements have been made in the field of artificial …

Climate change is one of the greatest challenges facing humanity, and we, as machine
learning (ML) experts, may wonder how we can help. Here we describe how ML can be a …

Abstract Machine learning and deep learning models have become essential in the recent
fast development of artificial intelligence in many sectors of the society. It is now widely …

Deep learning (DL) can achieve impressive results across a wide variety of tasks, but this
often comes at the cost of training models for extensive periods on specialized hardware …

Reporting electricity consumption is essential for sustainable AI | Nature Machine Intelligence
Skip to main content Thank you for visiting nature.com. You are using a browser version with …

Machine learning accounts for considerable global electricity demand and resulting
environmental impact, as training a large deep-learning model produces 284000kgs of the …