Mapping Machine Learning to Physics (ML2P)
| Agency: | DEPT OF DEFENSE |
|---|---|
| State: | Federal |
| Type of Government: | Federal |
| FSC Category: |
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| NAICS Category: |
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| Posted Date: | Aug 8, 2025 |
| Due Date: | Sep 5, 2025 |
| Solicitation No: | DARPA-SN-25-101 |
| Original Source: | Please Login to View Page |
| Contact information: | Please Login to View Page |
| Bid Documents: | Please Login to View Page |
Description
Follow
Active
Contract Opportunity
Notice ID
DARPA-SN-25-101
Related Notice
Department/Ind. Agency
DEPT OF DEFENSE
Sub-tier
DEFENSE ADVANCED RESEARCH PROJECTS AGENCY (DARPA)
Office
DEF ADVANCED RESEARCH PROJECTS AGCY
General Information
Classification
Description
Contact Information
History
- Contract Opportunity Type: Special Notice (Original)
- Original Published Date: Aug 08, 2025 02:31 pm EDT
- Original Response Date: Sep 05, 2025 11:59 pm EDT
- Inactive Policy: Manual
- Original Inactive Date: Sep 06, 2025
-
Initiative:
- None
- Original Set Aside:
- Product Service Code: AC11 - NATIONAL DEFENSE R&D SERVICES; DEPARTMENT OF DEFENSE - MILITARY; BASIC RESEARCH
-
NAICS Code:
- 541715 - Research and Development in the Physical, Engineering, and Life Sciences (except Nanotechnology and Biotechnology)
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Place of Performance:
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.
Attachments/Links
Contracting Office Address
- 675 NORTH RANDOLPH STREET
- ARLINGTON , VA 222032114
- USA
Primary Point of Contact
- Solicitation Coordinator
- ML2P@darpa.mil
Secondary Point of Contact
- Aug 08, 2025 02:31 pm EDTSpecial Notice (Original)
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