ENHANCING AND PRODUCTIZING THE DVMR RAD-HARD DIGITAL AI COPROCESSOR

DEFINITIVE CONTRACT won by MENTIUM TECHNOLOGIES INC. · work in GOLETA, CA

$2,499,575

Recipient	MENTIUM TECHNOLOGIES INC. UEI QW46A3LDM478
Award ID	80NSSC26C0011
Award amount	$2,499,575
Award type	DEFINITIVE CONTRACT
Awarding agency	National Aeronautics and Space Administration — National Aeronautics and Space Administration
NAICS	541713 — RESEARCH AND DEVELOPMENT IN NANOTECHNOLOGY
PSC	AJ14
Set-aside	Small business
Start date	Feb 10, 2026
End date	May 9, 2027
Place of performance	GOLETA, CA
Record updated	Jun 22, 2026

View the official award record

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EO14042 SBIR PHASE II LUNAR SEQUENTIAL - NEUROMORPHIC CHIP FOR SENSING, SITUATIONAL AWARNESS, AND DECISION MAKING IN RADIATIVE ENVIRONMENTS

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FY23 SBIR PHASE III - TESTING NEUROMORPHIC ARCHITECTURES FOR HIGH CAPACITY/LOW-POWER AI IN SUB-ORBITAL FLIGHT

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ARTIFICIAL INTELLIGENCE REALIZED THROUGH MACHINE LEARNING ALGORITHMS SEEMS TO BE THE ONLY VIABLE SOLUTION TO IMPLEMENT PERCEPTION, ENABLE PILOT ASSISTANTS AND EVENTUALLY FULL AUTONOMY TO UAS. CURRENTLY, MANY UAS HAVE SOME KIND OF CONVENTIONAL COMPUTER VISION (CV) HELPING THEM IN OBSTACLE AVOIDANCE OR TARGET ACQUISITION. INTERESTINGLY THOUGH, SINCE 2012 DEEP NEURAL NETWORKS (DNN) HAVE DRAMATICALLY OUTPERFORMED CONVENTIONAL CV ALGORITHMS IN THOSE TASKS AND PUSHED ARTIFICIAL INTELLIGENCE (AI) LIMITS IN A VARIETY OF OTHER APPLICATIONS INCLUDING, BUT NOT LIMITED, TO OBJECT RECOGNITION, VIDEO ANALYTICS, DECISION MAKING AND CONTROL, SPEECH RECOGNITION, ETC. UNFORTUNATELY, THE COMPUTATIONAL POWER REQUIRED FOR REAL-TIME DNN OPERATION CAN STILL ONLY BE DELIVERED BY BULKY, EXPENSIVE, SLOW, HEAVY AND ENERGY-HUNGRY DIGITAL SYSTEMS LIKE GPUS. THIS IS WHY MENTIUM IS DEVOTED TO DELIVERING DISRUPTIVE TECHNOLOGY IN THE FIELD OF MACHINE LEARNING HARDWARE ACCELERATORS, AND IN PARTICULAR FOR THIS PROJECT, INTO THE DEEP LEARNING HARDWARE ACCELERATORS FIELD. EXPERIMENTAL DATA AND PHASE I RESULTS CONFIRM THAT OUR HARDWARE CAN DELIVER 100X TO 1000X GAIN IN SPEED AND IN POWER EFFICIENCY COMPARED TO OTHER STATEOF- THE-ART ACCELERATORS. OUR FINAL PRODUCT WILL BE ABLE TO ANALYZE, IN REAL-TIME, BIG DATA STREAMS COMING FROM CAMERAS, SENSORS AND/OR AVIONICS AND TO CATEGORIZE (CLASSIFY) THEM FOR THE PURPOSE OF DECISION MAKING OR OBJECT LOCALIZATION TO ACHIEVE BETTER NAVIGATION AND COLLISION AVOIDANCE IN UAS. THE SAME HARDWARE PROCESSOR WILL BE DEPLOYABLE IN THE AIR TRAFFIC SYSTEMS, FOR REAL-TIME DATA ANALYSIS AND DECISION-MAKING. ALL WITH MORE THAN 10X REDUCTION IN COST AND POWER CONSUMPTION. THIS DISTRUPTIVE TECHNOLOGY IS BASED ON AN ANALOGCOMPUTATIONAL CORE, EXPLOITING THE MEMORY DEVICES TO CARRY OUT THE COMPUTATION AT A PHYSICAL LEVEL. ANALOG COMPUTATION IS INHERENTLY FASTER AND MORE EFFICIENT THAN THE DIGITAL ONE, WHILE THE IN-MEMORY COMPUTATION REMOVES THE DATA TRANSFER BOTTLENECK.

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