National Aeronautics and Space Administration (NASA)

Neuroevolution of Electronic Liquid State Machines

Our staff, formerly employed at Warrant Technologies, successfully completed a Phase I SBIR (Proposal #: H6.22-4121, CN: 80NSSC19C0260, 2019-1) in the fall of 2019 in support of NASA’s ongoing mission to develop technologies associated with the subtopic Deep Neural Net and Neuromorphic Processors for In-Space Autonomy and Cognition.


• Generated an AI solution that is robust both at the hardware layer as well as the model layer
• Circumvented the use of expensive and power-hungry general-purpose Graphics Processing Units (GPUs)
• Utilized a familiar and pre-existing hardware resource onboard many spacefaring systems


Technical Description:

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.

Potential Applications:

• Lightweight centralized sensory-affectory system for evolutionary robotics applications
• Energy-efficient neuromorphic implementation for cognitive radio networks
• Fault tolerant and adaptive onboard navigation system for terrain exploration
• Adaptive electromyographic interpreter for prosthetic limbs
• Energy efficient sensor preprocessor for internet-of-things (IoT) networks
• High performance analog radio frequency filter for cognitive radio.

Contract Details:

Contract/Task Order No.: 80NSSC19C0260
Customer Agency: NASA
Contract Type: T&M
Program Name: Deep Neural Net and Neuromorphic Processors for In-Space Autonomy and Cognition
Total Award Value: $150K
Period of Performance (PoP): 08/19/2019 – 02/18/2020