Brainchip + NASA, Airforce + Neuromorphic

Brainchip + NASA

So there are actually two separate accepted proposals in with NASA - we know the Tensor proposal has progressed and been accepted as phase2.

Who doesn’t love reading the words “interplanetary CubeSats”

https://www.sbir.nasa.gov/SBIR/abstracts/20/sbir/phase1/SBIR-20-1-H6.22-4509.html

Estimated Technology Readiness Level (TRL) :

Begin: 1

End: 4

Technical Abstract (Limit 2000 characters, approximately 200 words)

The ultimate goal of this project is to create a radiation-hardened Neural Network suitable for Ede use. Neural Networks operating at the Edge will need to perform Continuous Learning and Few-shot/One-shot Learning with very low energy requirements, as will NN operation. Spiking Neural Networks (SNNs) provide the architectural framework to enable Edge operation and Continuous Learning. SNNs are event-driven and represent events as a spike or a train of spikes. Because of the sparsity of their data representation, the amount of processing Neural Networks need to do for the same stimulus can be significantly less than conventional Convolutional Neural Networks (CNNs), much like a human brain. To function in Space and in other extreme Edge environments, Neural Networks, including SNNs, must be made rad-hard.

Brainchip’s Akida Event Domain Neural Processor (www.brainchipinc.com) offers native support for SNNs. Brainchip has been able to drive power consumption down to about 3 pJ per synaptic operation in their 28nm Si implementation. The Akida Development Environment (ADE) uses industry-standard development tools Tensorflow and Keras to allow easy simulation of its IP.

Phase I is the first step towards creating radiation-hardened Edge AI capability. We plan to use the Akida Neural Processor architecture and, in Phase I, will:

1. Understand the operation of Brainchip’s IP

2. Understand 28nm instantiation of that IP (Akida)

3. Evaluate radiation vulnerability of different parts of the IP through the Akida Development Environment

4. Define architecture of target IC

5. Define how HARDSIL® will be used to harden each chosen IP block

6. Choose a target CMOS node (likely 28nm) and create a plan to design and fabricate the IC in that node, including defining the HARDSIL® process modules for this baseline process

7. Define the radiation testing plan to establish the radiation robustness of the IC

Successfully accomplishing these objectives:

• Establishes the feasibility of creating a useful, radiation-hardened product IC with embedded NPU and already-existing supporting software ecosystem to allow rapid adoption and productive use within NASA and the Space community.

• Creates the basis for an executable Phase II proposal and path towards fabrication of the processor.

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

NASA applications will include miniaturized instruments and subsystems that must operate in harsh environments, interplanetary CubeSats and SmallSats, instruments bound for outer planets and heliophysics missions to harsh radiation environments. Neural-network and machine learning capabilities are required for robotic vision, navigation, communication, observation and system health management in future autonomous robotic systems.

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

The greatest potential for the next computing revolution lies in scaling AI to the billions of smaller, power-constrained Edge devices, while making them Rad-Hard. Innovative signal processing and ML techniques will open up new opportunities for SoC architects to deliver new levels of efficient AI performance in microcontrollers targeted at both the space and terrestrial markets.

Duration: 6

https://sbir.nasa.gov/SBIR/abstracts/20/sbir/phase1/SBIR-20-1-H6.22-6631.html

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

Airforce + Neuromorphic

https://www.saffm.hq.af.mil/Portals/84/documents/FY21/RDTE_/FY21%20Space%20Force%20Research%20Development%20Test%20and%20Evaluation.pdf?ver=2020-02-11-083608-887

This first one looks like it has plenty of interesting crossover with the NASA proposals, with $6.9 million dollars allocated for 2021:


Title: Space Electronics Research

Description: Develop technologies for space-based payload components such as radiation-hardened electronic devices, microelectro-mechanical system devices, and advanced electronics packaging.

FY 2020 Plans: For 2020 and prior, this work is performed under the Space Electronics Research effort in Appropriation 3600, Budget Activity(BA) 02, PE 1206601F, Space Technology, Project 624846, Spacecraft Payload Technologies.

FY 2021 Plans: Continue leadership role in Deputy Assistant Secretary of Defense Systems Engineering trusted and assured microelectronics strategy efforts by development of trusted manufacturing techniques that reduce risk to National Security Space systems.Improving benchmarking capabilities on state-of-the-art electronics using latest spacecraft algorithms and transitioning results to acquisition community to enable data-informed payload architecture design decisions. Initiating complete space qualification planning for next generation space processor and begin implementing plan. Continue development of alternative memory approaches for high density memory needed for next-generation space systems. Continue research and development of ultra-lowpower and neuromorphic/cortical processing architectures to enable game-changing capabilities in future National Security Space systems. Continue advanced transistor research and development, and transitioning techniques to mainstream manufacturing.

Lots more neuromorphic related funding here:

https://comptroller.defense.gov/Portals/45/Documents/defbudget/fy2021/budget_justification/pdfs/03_RDT_and_E/RDTE_Vol3_OSD_RDTE_PB21_Justification_Book.pdf

https://www.saffm.hq.af.mil/Portals/84/documents/FY21/RDTE_/FY21%20Air%20Force%20Research%20Development%20Test%20and%20Evaluation%20Vol%20I.pdf?ver=2020-02-11-083544-793

Interesting to note:
-Artificial Intelligence (AI) Neuromorphic Chip (Army): This project evaluates a low cost neuromorphic chip to replace the current paper and pencils method for counting 120-millimeter mortar rounds to more accurately determine weapon system life cycle maintenance. This effort demonstrates a tactical application of AI, will increase readiness, and could save millions of dollars in maintenance cost. If successful, this technology will be available for transition to the Army’s Stryker Program Office for follow on acquisition. This project was initiated out-of-cycle in 4Q FY 2019. Initial test planning and contract preparation occurred in 4Q FY2019. This project continues in FY 2020 with FY 2020 funds.