# Mapping Machine Learning to Physics (ML2P)

Canonical: https://abierto.us/opportunities/darpasn25101

- Solicitation number: DARPA-SN-25-101
- Notice type: Special notice
- Status: Closed. Deadline was September 5, 2025
- Department: Department of Defense
- Agency: Defense Advanced Research Projects Agency
- Contracting office: Def Advanced Research Projects Agcy (HR0011)
- NAICS: 541715 Research and Development in the Physical, Engineering, and Life Sciences (except Nanotechnology and Biotechnology)
- Product or service code: AC11 National Defense R&D Services; Department Of Defense - Military; Basic Research
- First posted: August 8, 2025
- Last posted: August 8, 2025
- SAM.gov: https://sam.gov/workspace/contract/opp/c6233393d88d450ebf942f87430516fe/view

## Description

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.

## Publications

- August 8, 2025: Special notice, due September 5, 2025. Notice c6233393d88d450ebf942f87430516fe. https://sam.gov/workspace/contract/opp/c6233393d88d450ebf942f87430516fe/view

## Points of contact

- Solicitation Coordinator, ML2P@darpa.mil

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Source: SAM.gov Contract Opportunities bulk extract. Confirm deadlines on SAM.gov before responding. Cite https://abierto.us/opportunities/darpasn25101.