Low-power, ultra-fast deep learning neuromorphic chip for unmanned aircraft systems
Award amount
$116,707Program and phase
SBIR · Phase IAward year
2017Award details
- Awarding agency
- National Aeronautics and Space Administration
- Branch
- Not reported
- Recipient company
- MENTIUM TECHNOLOGIES INC.
- Proposal award date
- 2017-06-09
- Contract end date
- 2017-12-08
- UEI
- QW46A3LDM478
- Solicitation number
- Not reported
- Topic code
- A2.02
Research abstract
Technology keywords
Not reported
Other awards by this recipient
Verified by the same UEI. These are other public SBIR/STTR award pages, not a merged company record.
- Neuromorphic chip for sensing, situational awarness, and decision making in radiative environments
- Low-Power, ultra-Fast Deep Learning Neuromorphic Chip for Unmanned Aircraft Systems
- SBIR Phase I: Addressing the memory bottleneck in deep neural networks in cloud platforms
Awards with the same topic code
Verified by an exact topic code match. This is not a text-similarity match.
- Safety Analysis For Evaluating (SAFE) sUAS
- Autonomous Contingency Detection and Reaction for Unmanned Aircraft
- Compact High Power 3D LiDAR System for (UAS) Unmanned Aircraft Systems
- Windhover Unmanned Aircraft Systems (UAS) Software Ecosystem
- NExT ADS-B - An Affordable Architecture for ADS-B Coverage to the Surface for UAS in the NAS
Research funding history
4 SBIR/STTR records share this UEI (2017–2022). Reported award amounts total $5,663,239. Reported amounts are not federal obligations.
Phases: Phase II, Phase I
Agencies: National Aeronautics and Space Administration, National Science Foundation