Mapping Machine Learning to Physics ML2P Tender, USA - 127664700

Procurement Summary

Country: USA

Summary: Mapping Machine Learning to Physics ML2P

Deadline: 17 Dec 2025

Other Information

Notice Type: Tender

TOT Ref.No.: 127664700

Document Ref. No.: DARPA-PS-25-32

Financier: Self Financed

Purchaser Ownership: Public

Tender Value: Refer Document

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Tender Details

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.
Notice ID: DARPA-PS-25-32
Department/Ind. Agency: DEPT OF DEFENSE
Sub-tier: DEFENSE ADVANCED RESEARCH PROJECTS AGENCY (DARPA)
Office: DEF ADVANCED RESEARCH PROJECTS AGCY
Product Service Code: AC12 - NATIONAL DEFENSE R&D SERVICES; DEPARTMENT OF DEFENSE - MILITARY; APPLIED RESEARCH
NAICS Code: 541715 - RESEARCH AND DEVELOPMENT IN THE PHYSICAL, ENGINEERING, AND LIFE SCIENCES (ExCEPT NANOTECHNOLOGY AND BIOTECHNOLOGY)
Inactive Dates: JAN 07, 2026
Inactive Policy: MANUAL

Documents

 Tender Notice