Mapping Machine Learning to Physics (ML2) Proposers Day DARPA-SN-25-102
Summary
AI-generated · Aug 25, 2025Edge ML deployments in resource-limited battlefield environments demand power-aware solutions. The ML2P program aims to prioritize power efficiency from the outset by mapping ML efficiency to physics using precise Joule measurements, enabling accurate power and performance predictions across diverse hardware architectures.
It also seeks development of multi-objective optimization functions that balance power consumption with performance metrics, and to explore how local optimizations interact through Energy Semantics of ML (ES-ML) to address the energy-aware ML optimization problem. This includes an information session (Proposers Day) for interested participants to learn more about the program.
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.
From Special Notice posted on Aug 13, 2025Notice history
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