ONHS Report: FA875023S7004

EXTREME COMPUTING

Solicitation ID: FA875023S7004

Agency: AFRL/Information Directorate

Type: Presolicitation

Deadline: 2028-09-20T16:00:00 ET

Eligibility: Maybe (Dual Approval Required)

Funding: Total funding of approximately $497.9M. Individual awards will normally range from $1M to $3M, with the potential for awards up to $99.9M.

Doc Type: BAA

Clearance: Top Secret

Program Manager: Joel Moore

Last Updated: 2025-11-24 13:02

Analyzed File: 23-04 Amend 8 second repub.docx

Eligibility Reasoning

Federally Funded Research and Development Centers (FFRDCs) and Government entities... cannot propose to this BAA in any capacity unless they meet the following conditions: FFRDCs must clearly demonstrate that the proposed work is not otherwise available from the private sector; and FFRDCs must provide a letter on official letterhead from their sponsoring organization citing the specific authority establishing their eligibility to propose to Government solicitations and compete with industry... This information is required for FFRDCs proposing to be prime contractors or sub-awardees.

Note: This is an AI-generated summary...

Summary

The Department of the Air Force, through the Air Force Research Laboratory (AFRL), has released Broad Agency Announcement (BAA) FA8750-23-S-7004, titled 'Extreme Computing.' With a total estimated funding of $497.9 million, this open, two-step BAA is effective until September 20, 2028. It solicits white papers for research, development, and evaluation of advanced computing technologies. Key technical areas include computational diversity and efficient architectures, embedded AI/machine learning, computing at the edge, nanocomputing, space computing, and robust algorithms. The objective is to develop revolutionary computational capabilities with greater autonomy and intelligence for Command, Control, Communications, Computers, Intelligence, Surveillance, and Reconnaissance (C4ISR) and other Air Force/Space Force applications, particularly in SWaP-constrained environments. Initial submissions are limited to white papers; formal proposals are by invitation only. Multiple awards are anticipated, typically ranging from $1M to $3M over 36 months, with the potential for larger awards up to $99.9M, issued as contracts, grants, or other transactions (OTs).

Overall Technical Areas

Efficient Computing ArchitecturesEmbedded Machine LearningComputing at the EdgeNanocomputingSpace Domain ComputingRobust AI AlgorithmsNeuromorphic ComputingNanoelectronic DevicesHigh-Performance Embedded ComputingAutonomous Intelligent SystemsBio-Inspired ArchitecturesSecure & Trusted AILow SWaP System DesignComputational NeurosciencePattern Recognition & Analysis

Probable LBNL Areas

BSACSAESAETAPSA

Focus Areas

Example Priorities:

Seeking innovative research to explore and develop computational capabilities with greater sophistication, autonomy, intelligence, and assurance for addressing dynamic mission requirements imposed by Command, Control, Communications, Computers, Intelligence, Surveillance and Reconnaissance (C4ISR) and Cyber applications...
high performance embedded computing that supports on-board processing using advanced machine learning applications
robust and secure machine learning technology to strengthen and defend military applications of artificial intelligence and machine learning
non-conventional neuromorphic systems and applications
methods and architectures that can provide dramatic improvements in the performance/cost of systems

Specific Technical Skills:

High Performance Embedded ComputingSecure Machine LearningNeuromorphic SystemsC4ISR SystemsCyber ApplicationsSWaP OptimizationAutonomous SystemsSystem Architecture DesignArtificial Intelligence

Potential PIs for this Area:

Name	Score	Organization
Samuel Williams	0.71	CSA \| AMCR \| Computer Science
Vinay Sawal	0.70	CSA \| NERSC \| Data Center Department
Angelos Ioannou	0.70	CSA \| AMCR \| Computer Science
Georgios Michelogiannakis	0.69	CSA \| AMCR \| Computer Science
Tiffany Connors	0.69	CSA \| NERSC \| Data Center Department
Neel Rajeshbhai Vora	0.69	PSA \| ATAP \| BACI
Jean Luca Bez	0.69	CSA \| SciData \| Data Science Research
Alexander Sim	0.69	CSA \| SciData \| Data Science Research
Gazi Mahmud	0.69	BSA \| Environmental Genomics & Systems Biology \| Molecular Ecosystems Bio
Hasita Veluri	0.68	CSA \| AMCR \| Computer Science

Example Priorities:

explore current and emerging nanoelectronics for information processing towards novel bio-inspired computing architectures that utilize ultra-low consumed power
emerging nanomaterials and processes that enable innovative interfaces/circuits/architectures and assess 2D and 3D technology research
multidisciplinary development of memristive, spintronic, ferroelectric, or hybrid complementary metal oxide semiconductor (CMOS) technologies in novel architectures
Nano electronic architectures that support novel information processing such as spike processing and synthetic neurons in bioinspired architectures

Specific Technical Skills:

NanoelectronicsBio-inspired ComputingMemristive DevicesSpintronicsFerroelectric MaterialsHybrid CMOS2D/3D NanomaterialsLow-Power DesignSpike-based ProcessingNeuron-electronic Interfaces

Potential PIs for this Area:

Name	Score	Organization
Sayeef Salahuddin	0.73	ESA \| MSD \| Material Physics
Ran Cheng	0.70
Kaushalya Jhuria	0.70	PSA \| ATAP \| FS&IBT
Junqiao Wu	0.69	ESA \| MSD \| Material Physics
Shiva Shunmugam Nagarajan	0.69	ESA \| ALS \| Users
Carolin Sutter-Fella	0.68	ESA \| MF \| MF Inorganic Facility
Ali Javey	0.68	ESA \| MSD \| Material Physics
Alp Sipahigil	0.68	ESA \| MSD \| Material Physics
Fabrice Roncoroni	0.68	ESA \| MF \| MF Theory Facility
Hasita Veluri	0.68	CSA \| AMCR \| Computer Science

Example Priorities:

advance the understanding and development of computationally intelligent systems towards increased perception, adaptability, resiliency, and autonomy for energy efficient, agile Air Force and Space Force (AF&SF) platforms
embedded deep learning and the trade space of accuracy vs. computing resources as typical deep neural networks are pushed to low size, weight, and power embedded devices
research and development in trusted and robust machine learning, which are resilient to perturbations in input data and secured against adversarial actions
in situ training of neuromorphic hardware, and online learning in neural networks

Specific Technical Skills:

Neuromorphic ComputingEmbedded Deep LearningComputational NeuroscienceTrusted AIRobust Machine LearningOnline LearningIn-situ TrainingNanophotonicsEnergy-Efficient HardwareHuman-Machine Collaboration

Potential PIs for this Area:

Name	Score	Organization
Karthik Shekhar	0.70	BSA \| Biological Systems & Engineering \| Biodesign Dept
Neel Rajeshbhai Vora	0.69	PSA \| ATAP \| BACI
Hasita Veluri	0.68	CSA \| AMCR \| Computer Science
Stephanie Prince	0.68	CSA \| SciData \| Data & Computational Science
Georgios Michelogiannakis	0.68	CSA \| AMCR \| Computer Science
Angelos Ioannou	0.67	CSA \| AMCR \| Computer Science
Marco Pritoni	0.67	ETA \| Building Technology & Urban Systems \| Bldg Technologies Dept
Shashank Subramanian	0.67	CSA \| NERSC \| Sci Eng & Workflows Department
Corneel Casert	0.67	CSA \| NERSC \| Sci Eng & Workflows Department
Kaushalya Jhuria	0.67	PSA \| ATAP \| FS&IBT

Example Priorities:

develop advanced efficient computing architectures and systems, as well as robust algorithms and applications
achieve orders of magnitude improvement in size, weight and power, for deploying robust artificial intelligence and machine learning capabilities
in an embedded computing environment for Ground, Air and Space domains

Specific Technical Skills:

Efficient Computing ArchitecturesEmbedded SystemsDeep Learning AlgorithmsSWaP ReductionRobust Artificial IntelligenceEdge AIHardware AccelerationSpace-based ComputingMulti-domain OperationsAlgorithm Optimization

Potential PIs for this Area:

Name	Score	Organization
Angelos Ioannou	0.71	CSA \| AMCR \| Computer Science
Samuel Williams	0.70	CSA \| AMCR \| Computer Science
Jean Luca Bez	0.70	CSA \| SciData \| Data Science Research
Hasita Veluri	0.69	CSA \| AMCR \| Computer Science
Neel Rajeshbhai Vora	0.69	PSA \| ATAP \| BACI
Aydin Buluc	0.69	CSA \| AMCR \| Computer Science
Georgios Michelogiannakis	0.69	CSA \| AMCR \| Computer Science
Alexander Sim	0.69	CSA \| SciData \| Data Science Research
Gazi Mahmud	0.68	BSA \| Environmental Genomics & Systems Biology \| Molecular Ecosystems Bio
Edward Bethel	0.68	CSA \| SciData \| Data Science Research

Potential LBNL PIs (Overall)

Note on PI Matching: These suggestions are generated through an AI-driven semantic analysis of LBNL staff profiles.

Name	Score	Organization
Angelos Ioannou	0.77	CSA \| AMCR \| Computer Science · Efficient Computing Architectures
Luisa Gonzalez	0.77	CSA \| AMCR \| Computer Science · Nanocomputing
Venkatesh Akella	0.76	CSA \| SciData \| Data Science Applications · High-Performance Embedded Computing
Christoph Kirst	0.76	CSA \| SciData \| Data & Computational Science · Computational Neuroscience
Dilip Vasudevan	0.75	CSA \| AMCR \| Computer Science · Neuromorphic Computing
Luisa Caldas	0.75	ETA \| Building Tech. & Urban Sys. \| Bldg Technologies Dept · Bio-Inspired Architectures
Sayeef Salahuddin	0.75	ESA \| MSD \| Material Physics · Nanoelectronic Devices
Farzad Fatollahi-Fard	0.74	CSA \| AMCR \| Computer Science · Computing at the Edge
Suren Byna	0.74	CSA \| SciData \| Data Science Research · Space Domain Computing
Alexandre Bayen	0.73	ETA \| Energy Analysis \| Systems & Energy Tech Analysis · Secure & Trusted AI

Fundamental Research Exemption (FRE)

Applicable to any effort not considered Fundamental Research. Offerors are responsible for ensuring that all employees and/or subcontractors who will work on a resulting contract are eligible to do so. Any employee who is not a U.S. citizen or a permanent resident will be restricted from working on any resultant contract unless prior approval of the Department of State or the Department of Commerce is obtained via a technical assistance agreement or an export license. Violations of these regulations can result in criminal or civil penalties.