Wanted: small lightweight artificial intelligence (AI) and machine learning ability for embedded computing

Sept. 24, 2024
Potential applications are C4ISR, cyber, trusted machine learning, pattern recognition, signature analysis, and neural control of complex-systems.

ROME, N.Y. – U.S. Air Force computer researchers are asking for industry's help in developing artificial intelligence (AI) and machine learning in embedded computing, computing at the edge, nanocomputing, space computing, and robust algorithms.

Officials of the Air Force Research Laboratory Information Directorate in Rome, N.Y., issued a broad agency announcement (FA8750-23-S-7004) last week for the Extreme Computing project.

Extreme Computing seeks industry help for research into enabling technologies for computational diversity and efficient computing architectures, machine learning and artificial intelligence in embedded system and architectures, computing at the edge, nanocomputing, space computing, and robust algorithms and applications.

Related: Artificial intelligence (AI) embedded computing design and development tools suite introduced by Infineon

This project has four technical areas:

-- small and lightweight autonomous and sophisticated computing for command, control, communications, intelligence, surveillance, reconnaissance, and cyber applications;

-- current and emerging nanoelectronics for information processing towards bio-inspired computing architectures that consume ultra-low amounts of power for applications like computer vision, knowledge extraction, autonomous flight, and decision-making.

-- neuromorphic computing and machine learning for computationally intelligent systems for perception, adaptability, resiliency, and autonomy, involving computational neuroscience, nanoelectronics, nano photonics, high-performance computing, embedded deep learning, trusted machine learning, pattern recognition, signature analysis, autonomous adaptive operations, human-machine collaboration, neural control of complex-systems, in-situ training of neuromorphic hardware, and online learning in neural networks; and

-- computers, algorithms, and applications for embedded deep learning to reduce size, weight, and power consumption, for embedded artificial intelligence (AI) and machine learning.

Related: Rugged embedded computing system for machine learning and artificial intelligence (AI) introduced by Elma

The technical contact for small and lightweight autonomous and sophisticated computing is Nathan Inkawhich, whose email address is [email protected]. The contact for current and emerging nanoelectronics for information processing is Joseph Van Nostrand, whose email address is [email protected].

The technical contact for neuromorphic computing and machine learning is Clare Thiem, whose email address is [email protected]. The contact for computers, algorithms, and applications for embedded deep learning is Mark Barbell, whose email address is [email protected].

Related: Air Force researchers ask industry for artificial intelligence (AI) and machine learning for military C4ISR

Total funding for the Extreme Computing program will be about $497.9 million through 2028. Several contractors will be involved.

Companies interested should email white papers to each technical area's contact, shown above. Email white papers by 30 Sept. 2024 for 2025 awards; by 30 Sept. 2025 for 2026 awards; by 30 Sept. 2026 for 2027 awards; and by 30 Sept. 2027 for 2028 awards.

Email program questions or concerns to Joel Moore at [email protected], and business questions or concerns to Amber Buckley at [email protected]. More information is online at https://sam.gov/opp/dcc0322700c140f390dab1f4d6ca3fc3/view.

About the Author

John Keller | Editor-in-Chief

John Keller is the Editor-in-Chief, Military & Aerospace Electronics Magazine--provides extensive coverage and analysis of enabling electronics and optoelectronic technologies in military, space and commercial aviation applications. John has been a member of the Military & Aerospace Electronics staff since 1989 and chief editor since 1995.

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