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Maximum Phase I Award Amount: $200,000

Maximum Phase II Award Amount: $1,100,000

Accepting SBIR Phase I Applications: YES

Accepting STTR Phase I Applications: NO


The Office of Proliferation Detection (PD) within Defense Nuclear Nonproliferation Research and Development (DNN R&D) has an objective to develop and demonstrate innovative, digital methods to accelerate the transition of the next generation of Artificial Intelligence (AI) technologies. COTS AI is generally inadequate for the highly-specialized, high-consequence mission of the national security domain. Meeting this objective requires innovation in data science and AI capabilities to broadly enhance DNN R&Dā€™s diverse capabilities in feature engineering and detection, remote sensing, and signal processing capabilities. Grant applications are sought in the following subtopic:



a.      Cloud/High-Performance Computing (HPC) Architectures by Design

Innovation in cost-effective cloud/high-performance computing (HPC) architectures by design is needed to enhance traditional capabilities in remote sensing and signal processing capabilities and enable non-traditional methods (e.g., machine learning) for feature engineering and detection. There is a growing opportunity for expanded mission capability through the utilization of increasingly-available public and commercial imagery and sensor data, as well as open-source text streams, including social media, newsfeeds, scientific literature, and public records. There is also a growing number of HPC-based system models (deterministic and optimization-based) that need to be cost-effectively integrated with data analytics through cost- and time-optimized cloud/HPC architectures. Successful proposals will develop a system concept and software architecture for a proposed cloud and HPC combined architecture that optimizes cost-to-solution and time-to-solution, and should include at least two diverse data streams and one HPC-based systems model for demonstration of a limited-functionality prototype. Phase I deliverable is a final report that describes system concept, software architecture, algorithms, and results from limited functionality prototype demonstration.


Questions ā€“ Contact: Angie Sheffield,


b.      Other

In addition to the specific subtopics listed above, grant applications in other areas relevant to this topic are invited.


Questions ā€“ Contact: Angie Sheffield,

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