- Country: USA
- Summary: Mapping Machine Learning to Physics (ML2P)
- UST Ref No: 124211694
- Deadline: 05 Sep 2025
- Financier: Self Financed
- Purchaser Ownership: Government
- Tender Value: Refer Document
- Notice Type: Tender
- Document Ref. No.: DARPA-SN-25-101
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- Description:
- 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. - Documents:
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CLICK HERE TO LOGINMapping Machine Learning to Physics (ML2P) - USA Tender
The DEPT OF DEFENSE | DEFENSE ADVANCED RESEARCH PROJECTS AGENCY (DARPA), a Government sector organization in USA, has announced a new tender for Mapping Machine Learning to Physics (ML2P). This tender is published on USARFP under UST Ref No: 124211694 and is categorized as a Tender. Interested and eligible suppliers are invited to participate by reviewing the tender documents and submitting their bids before the deadline on 2025-09-05.
The estimated tender value is Refer Document, and full details, including technical specifications and submission requirements, are provided in the official tender documents. Ensure all submissions meet the criteria outlined to be considered for evaluation.
