Procurement Summary
Country: USA
Summary: Mapping Machine Learning to Physics (ML2P)
Deadline: 05 Sep 2025
Other Information
Notice Type: Tender
TOT Ref.No.: 124211694
Document Ref. No.: DARPA-SN-25-101
Financier: Self Financed
Purchaser Ownership: Public
Tender Value: Refer Document
Purchaser's Detail
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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.
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
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
Classification
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)
Place of Performance:
Documents
Tender Notice
DARPA-SN-25-101.pdf