Contractor directory

MENTIUM TECHNOLOGIES INC.

GOLETA, CA · UEI QW46A3LDM478 · CAGE 7UZU7

Largest contract awards

• 80NSSC22CA230$5.0M

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

  National Aeronautics and Space Administration · NAICS 541715 · Sep 2022 – Apr 2027 · official record ↗

• 80NSSC26C0011$2.5M

  ENHANCING AND PRODUCTIZING THE DVMR RAD-HARD DIGITAL AI COPROCESSOR

  National Aeronautics and Space Administration · NAICS 541713 · Feb 2026 – May 2027 · official record ↗

• 80NSSC23CA207$1.2M

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

  National Aeronautics and Space Administration · NAICS 541715 · Sep 2023 – Jul 2026 · official record ↗

• 80NSSC18C0094$1.1M

  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.

  National Aeronautics and Space Administration · NAICS 541715 · Jul 2018 – Jun 2022 · official record ↗

• 80NSSC20C0682$181K

  RADIATION HARDENED IN-MEMORY COMPUTING FOR SPACE APPLICATIONS

  National Aeronautics and Space Administration · NAICS 541715 · Sep 2020 – Mar 2022 · official record ↗

Public records from FPDS (via USAspending.gov) and SAM.gov. Figures reflect harvested award data and may not be complete; verify against the linked official records. Not an endorsement. Company in the data by mistake or wish to update it? Email support@usegovscout.com.