NASA SBIR 2017 Solicitation


PROPOSAL NUMBER: 17-2 A2.02-9133
SUBTOPIC TITLE: Unmanned Aircraft Systems Technology
PROPOSAL TITLE: Low-Power, ultra-Fast Deep Learning Neuromorphic Chip for Unmanned Aircraft Systems

SMALL BUSINESS CONCERN (Firm Name, Mail Address, City/State/Zip, Phone)
Mentium Technologies Inc.
2208 Pacific Coast Drive
Goleta, CA 93117 - 5494
(805) 617-6245

PRINCIPAL INVESTIGATOR/PROJECT MANAGER (Name, E-mail, Mail Address, City/State/Zip, Phone)
Farnood Merrikh Bayat
2208 Pacific Coast Drive
Goleta, CA 93117 - 5494
(805) 708-4652

CORPORATE/BUSINESS OFFICIAL (Name, E-mail, Mail Address, City/State/Zip, Phone)
Farnood Merrikh Bayat
2208 Pacific Coast Drive
Goleta, CA 93117 - 5494
(805) 708-4652

Estimated Technology Readiness Level (TRL) at beginning and end of contract:
Begin: 3
End: 6

Technology Available (TAV) Subtopics
Unmanned Aircraft Systems Technology is a Technology Available (TAV) subtopic that includes NASA Intellectual Property (IP). Do you plan to use the NASA IP under the award?

TECHNICAL ABSTRACT (Limit 2000 characters, approximately 200 words)

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 state-of-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 analog-computational 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.



POTENTIAL NASA COMMERCIAL APPLICATIONS (Limit 1500 characters, approximately 150 words)
We can easily envision our Deep Learning Accelerator deployed in the following NASA's Candidate Mission Products from Thrust 6 Roadmap:
- Autonomy-Enabled UAS for Earth Science
- Autonomy-Enabled Air Traffic Management
- Autonomy-Enhanced Vehicle Safety
- Inflight Vehicle Performance optimization
- Complex Decision-Making UAS

We want also emphasize that the final applications scope is wider than the Aeronautics Directorate, since the same architecture can be optimized for radioactive environments and deployed in space.

POTENTIAL NON-NASA COMMERCIAL APPLICATIONS (Limit 1500 characters, approximately 150 words)
Our final product will be attractive both for the private sector and the federal market as well.
As for the private sector, the Outcome if this SBIR project can be directly injected into the commercial UAV market, both for consumer and for executive applications.
Nevertheless, it is important to consider that with very few modifications our hardware accelerator could be used in:
- Industry intelligence (through visual, audio and sensors inputs)
- Cybersecurity
- Enterprises Big Data analytics
- Security cameras
- Automotive

Moreover, number of other federal agencies could be interested in our product, here is a list of the majors:
- EPA, Environmental Protection Agencies
- USDA, Department of Agriculture
- DHS, Department of Homeland Security
- DoD, Department of Defense
- NOAA, National Oceanic and Atmospheric Administration
- DoE, Department of Energy
- DoT, Department of Transportation

TECHNOLOGY TAXONOMY MAPPING (NASA's technology taxonomy has been developed by the SBIR-STTR program to disseminate awareness of proposed and awarded R/R&D in the agency. It is a listing of over 100 technologies, sorted into broad categories, of interest to NASA.)
Algorithms/Control Software & Systems (see also Autonomous Systems)
Autonomous Control (see also Control & Monitoring)
Avionics (see also Control and Monitoring)
Circuits (including ICs; for specific applications, see e.g., Communications, Networking & Signal Transport; Control & Monitoring, Sensors)
Data Processing
Image Analysis
Man-Machine Interaction
Recovery (see also Autonomous Systems)

Form Generated on 03-05-18 17:24