Inactive

Machine learning (ML) moves fast, but it needs power. More power than we have, and that’s the problem. The Department of Defense faces additional constraints with ML deployments at the edge in resourc... Machine learning (ML) moves fast, but it needs power. More power than we have, and that’s the problem. The Department of Defense faces additional constraints with ML deployments at the edge in resource-limited battlefield environments. The ML2P program is about prioritizing power efficiency consumption right from the start. ML2P will map ML efficiency directly to physics using precise Joule measurements, enabling accurate power and performance predictions across diverse hardware architectures. ML2P will develop multi-objective optimization functions that balance power consumption with performance metrics and discover how local optimizations interact through Energy Semantics of ML (ES-ML) to solve the energy-aware ML optimization problem.