NASA SBIR 2020-I Solicitation

Proposal Summary


PROPOSAL NUMBER:
 20-1- H6.22-6631
SUBTOPIC TITLE:
 Deep Neural Net and Neuromorphic Processors for In-Space Autonomy and Cognition
PROPOSAL TITLE:
 Scalable Neural Net and Neuromorphic Module for In-Space Autonomous Orientation and Maneuvering
SMALL BUSINESS CONCERN (Firm Name, Mail Address, City/State/Zip, Phone)
Tensor Innovation Partners, LLC
337 Forest Park Circle
Longwood, FL 32779
(407) 617-4874

Principal Investigator (Name, E-mail, Mail Address, City/State/Zip, Phone)

Name:
Lee Wooldridge
E-mail:
lee.wooldridge@tensorinnovation.com
Address:
337 Forest Park Circle Longwood, FL 32779 - 5875
Phone:
(407) 617-4741

Business Official (Name, E-mail, Mail Address, City/State/Zip, Phone)

Name:
Lee Wooldridge
E-mail:
lee.wooldridge@tensorinnovation.com
Address:
337 Forest Park Circle Longwood, FL 32779 - 5875
Phone:
(407) 617-4741
Estimated Technology Readiness Level (TRL) :
Begin: 5
End: 7
Technical Abstract (Limit 2000 characters, approximately 200 words)

Tensor, along with several commercial partners, is developing new technology suitable for small satellites (SmallSat/CubeSat) and small launch vehicles.  As part of this development, we are designing autonomy and artificial cognition capabilities for small scale satellites and vehicles that will be scalable to any space vehicle.  Taking advantage of our previous experience in the areas of neural modelling and advanced automation algorithms we are proposing a deep neural net and in-space autonomy and cognition systems neuromorphic processing module for this solicitation.  Using the COTS The BrainChip, Inc. Akida with fully configurable neural processing cores and scalable neural nets, we can design autonomy and artificial cognition capabilities for our prototype CubeSat that will be scalable to any space vehicle.  The overarching goal is to make spacecraft autonomy affordable and ubiquitous.

For Phase I of this SBIR, we intend to develop a neuromorphic-based modular architecture suitable for SmallSat autonomous operation and create metrics to validate the SWaP performance of our hardware design in Phase II.  As with any hardware, the driving cost factor is often the software that makes it useful.  In AI systems, the cost of the deep learning needed to provide robust, adaptable performance is often prohibitive.  In addition to a modular hardware design, Tensor will also design a cost effective, user-friendly suite of tools to support simplified training and implementation of spacecraft autonomy during Phase I for development and application during Phase II.  It is our goal in Phase II of this SBIR to demonstrate an affordable package of prototype autonomous control hardware and software that is scalable and readily adaptable to a variety of spacecraft morphologies and mission classes.

Potential NASA Applications (Limit 1500 characters, approximately 150 words)

The possibilities and applications are practically limitless across a spectrum of mission types.  Short list of the possibilities: Predictive and adaptive communications, radio, and system architecture, Opportunistic data collection, Continuous power allocation, Predictive failure/error detection, maintenance, mediation, and mitigation, Mission decision prioritization,Spacecraft constellation active collaboration optimizing, Continuous allocation optimization of system resources, Optimized integration of navigation, situation awareness, etc.

Potential Non-NASA Applications (Limit 1500 characters, approximately 150 words)

Our commercialization plan includes continuing development for the neuromorphic autonomous module for insertion into several commercial small launcher programs now and  in the future.  We will also apply the technology developed to other military applications with groups such as MDA, DARPA and USAF.  The system will be available as a “plug and play module” for all future spacecraft.

Duration: 5

Form Generated on 06/29/2020 21:07:35