Mapping Machine Learning to Physics (ML2P)
SOL #: DARPA-PS-25-32Solicitation
Overview
Buyer
DEPT OF DEFENSE
Defense Advanced Research Projects Agency (Darpa)
DEF ADVANCED RESEARCH PROJECTS AGCY
ARLINGTON, VA, 222032114, United States
Place of Performance
Place of performance not available
NAICS
Research and Development in the Physical (541715)
PSC
National Defense R&D Services; Department Of Defense Military; Applied Research (AC12)
Set Aside
No set aside specified
Original Source
Timeline
1
Posted
Sep 23, 2025
2
Last Updated
Oct 6, 2025
3
Submission Deadline
Dec 17, 2025, 5:00 PM
Qualification Details
Fit reasons
- NAICS alignment with historical contract wins in similar service areas.
- Scope strongly matches core technical capabilities and delivery model.
Risks
- Past performance thresholds may require one additional teaming partner.
- Potential clarification needed on staffing minimums before bid/no-bid.
Next steps
Validate eligibility requirements, assign capture owner, and schedule partner outreach to confirm teaming strategy before submission planning.
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.
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.
People
Points of Contact
Solicitation CoordinatorPRIMARY
Files
Versions
Version 2Viewing
Solicitation
Posted: Oct 6, 2025
Version 1
Solicitation
Posted: Sep 23, 2025