You are here

Neuroevolution of Electronic Liquid State Machines

Award Information
Agency: National Aeronautics and Space Administration
Branch: N/A
Contract: 80NSSC19C0260
Agency Tracking Number: 194121
Amount: $86,089.00
Phase: Phase I
Program: SBIR
Solicitation Topic Code: H6
Solicitation Number: SBIR_19_P1
Timeline
Solicitation Year: 2019
Award Year: 2019
Award Start Date (Proposal Award Date): 2019-08-19
Award End Date (Contract End Date): 2020-02-18
Small Business Information
115 North College Avenue, Suite 111, Bloomington, IN, 47404-3933
DUNS: 078813998
HUBZone Owned: N
Woman Owned: N
Socially and Economically Disadvantaged: N
Principal Investigator
 Derek Whitley
 (812) 573-8051
 derek.whitley@warranttek.com
Business Contact
 Mike Norris
Phone: (812) 361-6721
Email: mike_norris@warranttek.com
Research Institution
N/A
Abstract
The subtopic being addressed identifies current spacefaring computer hardware as insufficient for executing conventional artificial intelligence (AI) algorithms due to space, weight, and power constraints. Conversely, neuromorphic computing architectures have exhibited the ability to performatively execute AI programs while meeting these criteria. Presented here is one such general purpose neuromorphic computing architecture.Based on the continuous time recurrent neural network model and instantiated upon the reconfigurable fabric of a field-programmable gate array, clusters of hardware-accelerated neurons can be evolved in real time while responding directly to environmental conditions. Preliminary work with this neuromorphic solution exceeded expectations when solving complex time-series problems while simultaneously minimizing spatial and power consumption.Unlike many existing machine learning methods, this architecture can undergo hardware evolution for novel solutions or hardware adaptivity for existing solutions that are performing below necessary thresholds. Circuits undergoing intrinsic hardware evolution or adaptation exhibit naturally occurring fault tolerances as a result of real world environmental noise. These inherent phenomena make the continuous time recurrent neural network in evolvable hardware a powerful candidate for extraterrestrial and spacefaring operation.

* Information listed above is at the time of submission. *

US Flag An Official Website of the United States Government