NASA SBIR 2022-I Solicitation

Proposal Summary

Proposal Information

Proposal Number:
22-1- H6.22-2330
Subtopic Title:
Deep Neural Net and Neuromorphic Processors for In-Space Autonomy and Cognition
Proposal Title:
Adaptive Neuromorphic Processors for Cognitive Communications

Small Business Concern

   
Firm:
          
Brisk Computing, LLC
          
   
Address:
          
1191 Red Ash Court, Centerville, OH 45458
          
   
Phone:
          
(937) 765-7742                                                                                                                                                                                
          

Principal Investigator:

   
Name:
          
Tarek Taha
          
   
E-mail:
          
ttaha@ieee.org
          
   
Address:
          
1191 Red Ash Ct, OH 45458 - 4763
          
   
Phone:
          
(937) 765-7742                                                                                                                                                                                
          

Business Official:

   
Name:
          
Tarek Taha
          
   
E-mail:
          
ttaha@ieee.org
          
   
Address:
          
1191 Red Ash Ct, OH 45458 - 4763
          
   
Phone:
          
(937) 765-7742                                                                                                                                                                                
          

Summary Details:

   
Estimated Technology Readiness Level (TRL) :                                                                                                                                                          
Begin: 2
End: 4
          
          
     
Technical Abstract (Limit 2000 characters, approximately 200 words):

The objective of this work is to develop highly Size, Weight, and Power (SWaP) efficient neuromorphic processors that can train deep learning algorithms. The training phase for deep learning is very compute and data intensive. Being able to train a network on the satellite eliminates the need to send large volumes of data to earth for training a new network. However, this requires an extremely energy efficient deep learning training processor. We will develop resistive crossbar neuromorphic processors, with the primary target being to train deep learning algorithms. Although our system would work for any type of data, we plan to focus on networks for cognitive communication applications. We will also look at processing networks for other data sets. The key outcomes of the work will be the processor design, processor performance metrics on various applications, prototype system, and software for the processor.

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

Potential NASA applications include various deep learning training and inference tasks on satellites. These include cognitive communications, processing sensor outputs, and scientific experiments. Additionally, the developed system could be used for UAVs.

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

The non-NASA market would be primarily for edge processing, where power is highly limited. The market includes both the DoD and the commercial market. DoD applications include cognitive communications, sensor processing, cognitive decision making, and federated learning. Commercial applications include communications systems, automobiles, consumer electronics, and robots.

          
          
     
Duration:     6
          
          

Form Generated on 05/25/2022 15:38:04