# Energy-Aware Machine Learning  ## Metadata - Author: [email protected] - Full Title: Energy-Aware Machine Learning - Category: #articles - Document Tags: #ai - Summary: DARPA's ML2P program aims to create AI models that balance high performance with low energy use. It measures energy in joules to design smarter and more efficient machine learning systems. This approach could improve AI for defense and everyday technology while guiding future hardware design. - URL: https://www.darpa.mil/news/2025/energy-aware-machine-learning ## Highlights - [Our Mapping Machine Learning to Physics (ML2P) program](https://www.darpa.mil/research/programs/mapping-machine-learning-physics) is designed to transform how artificial intelligence (AI) systems balance performance with energy use. ([View Highlight](https://read.readwise.io/read/01k642qmckd805z41h1ff18t63)) - ML2P addresses this gap by mapping machine learning model performance to physical electric characteristics, with a focus on measuring energy use in joules. By embedding energy-awareness into the design of AI systems, ML2P aims to create models that can achieve the right balance between accuracy and power consumption. ([View Highlight](https://read.readwise.io/read/01k642qqhvec5yakn36hrakypt)) - “Making models more efficient and performant is crucial as AI applications often require substantial computational resources, leading to high energy consumption,” he said. “By enabling principled simulation of machine learning model performance on general-purpose compute systems, it could provide insights into how hardware should be optimized for AI workloads.” ([View Highlight](https://read.readwise.io/read/01k642sd6t5yxfmxqfk80nrcq2)) - If successful, ML2P will establish a new paradigm for AI design: one where efficiency and performance go hand in hand, ensuring that AI systems can economically operate at speed and scale with high levels of accuracy. ([View Highlight](https://read.readwise.io/read/01k642sn7jh2rnz846rk4eq9re))