BrainChip + NASA

uiux

Regular


BrainChip and VORAGO Technologies Agree to Collaborate through the Akida™ Early Access Program

BrainChip Holdings Ltd (ASX: BRN), a leading provider of ultra-low power high performance AI technology, today announced that VORAGO Technologies has signed the Akida™ Early Access Program Agreement. The collaboration is intended to support a Phase I NASA program for a neuromorphic processor that meets spaceflight requirements. The BrainChip Early Access Program is available to a select group of customers that require early access to the Akida device, evaluation boards and dedicated support. The EAP agreement includes payments that are intended to offset the Company’s expenses to support partner needs.

The Akida neuromorphic processor is uniquely suited for spaceflight and aerospace applications. The device is a complete neural processor and does not require an external CPU, memory or Deep Learning Accelerator (DLA). Reducing component count, size and power consumption are paramount concerns in spaceflight and aerospace applications. The level of integration and ultra-low power performance of Akida supports these critical criteria. Additionally, Akida provides incremental learning. With incremental learning, new classifiers can be added to the network without retraining the entire network. The benefit in spaceflight and aerospace applications is significant as real-time local incremental learning allows continuous operation when new discoveries or circumstances occur.



BrainChip’s Akida set for spaceflight via NASA as Renesas Electronics America signs first IP agreement

Tech company BrainChip (ASX: BRN) has announced a double combo of new developments relating to its Akida neuromorphic processor including a new order from US space agency NASA and signing an intellectual property (IP) license agreement with Japanese semiconductor giant Renesas Electronics.

BrainChip confirmed that NASA had ordered its Akida Early Access Evaluation Kit to be used at the NASA/Ames Research Center (ARC) at Moffett Field in California.


---


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

---


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


---



3 The Evolution of Analytics Algorithms
As we have seen above, the growth of data Volume and Variety bring both tremendous
opportunities as well as new challenges. However, the growth of data analytics capabilities
available to the user community is growing as well. In particular, the explosion of machine
learning has been remarkable. There are now multiple well-written off-the-shelf frameworks to
support machine learning in a variety of languages, though the largest explosion has been in the
Python and R areas. Most of the interest appears to be in the area of Deep Neural Networks
(DNNs), which have become well-used with the arrival of accessible computing power,
including processing units particularly suited for DNNs such as Graphics Processing Units, or
even purpose built neural processing units (Van der Made and Mankar, 2017)
. In addition,
several variations on the theme of DNNs have begun to be deployed to tackle EO problems. For
example, one of the problems with scaling land cover and land use studies to global scale is the
difficulty with applying a model trained in one area to work in other areas. Transfer Learning
shows promise for solving this conundrum


van der Made, P.A. and Mankar, A.S., Brainchip Inc, 2017. Neural processor based accelerator
system and method. U.S. Patent Application 15/218,075
 
Last edited:
  • Like
  • Fire
Reactions: 22 users

stuart888

Regular
Sure gives me confidence that they have been working with Nasa, plus whatever else in our government. Since they moved from phase 1 approval, then got moved to phase 2, etc is special. Nasa and the government stuff has to be rock solid, same with the automotive industry.

Brainchip is dotting all the best dots between all the major players. The ramp up period for this stuff takes time. You don't just put a new chip in a Mercedes and roll it down the assembly line. Those Renesas videos showing off the DRP-AI are wow factor. The perfect Use Case for Brainchip Akida's Spiking Neural Network, with low power and learning smarts.

Cannot wait for each quarter's financial and relationship announcements. I am in no hurry for rockets, dollar cost averaging in while the world is going through a rough patch.
 
  • Like
  • Thinking
Reactions: 11 users

uiux

Regular

Attachments

  • N210021885.pdf
    1.6 MB · Views: 220
  • Like
  • Fire
Reactions: 11 users
  • Like
Reactions: 7 users

Diogenese

Top 20
  • Like
  • Haha
  • Love
Reactions: 15 users

uiux

Regular

1661614330676.png


Mission Radiation Environment Modeling And Analysis: Avionics Trade Study For Gcd Rad-Neuro Project

The two main objectives of this trade study are characterizing the mission radiation environment for multiple Design Reference Missions (DRM) and analysis of radiation effects on avionics with the goal of producing radiation tolerant Neuromorphic Computing processor chips with innovative radiation-induced fault mitigation. The NASA process for defining radiation requirements for flight avionics is applied to this domain. The effects of trapped protons and electrons in the Van Allen radiation belts predominates in Low-Earth-Orbit (LEO) and the solar wind, solar flares and Galactic Cosmic Rays (GCRs) are the dominant radiation challenge in the open space between the planets of our solar system. The nature and energy of the particles that cause circuit upset and failure is very different in the two regimes. Two results are produced from the radiation models: determining the Total Integrated Dose (TID) experienced by avionics for a given DRM and predicting the Single Event Effects (SEE) rates for avionics during high rate exposure. These tools are applied to existing semiconductors and can be used for predicting the radiation performance of future semiconductors based on early radiation testing of new devices.

---

Very interesting read.
 
  • Like
  • Fire
Reactions: 19 users

uiux

Regular
  • Like
  • Fire
Reactions: 13 users

DJM263

LTH - 2015

View attachment 15178

Mission Radiation Environment Modeling And Analysis: Avionics Trade Study For Gcd Rad-Neuro Project

The two main objectives of this trade study are characterizing the mission radiation environment for multiple Design Reference Missions (DRM) and analysis of radiation effects on avionics with the goal of producing radiation tolerant Neuromorphic Computing processor chips with innovative radiation-induced fault mitigation. The NASA process for defining radiation requirements for flight avionics is applied to this domain. The effects of trapped protons and electrons in the Van Allen radiation belts predominates in Low-Earth-Orbit (LEO) and the solar wind, solar flares and Galactic Cosmic Rays (GCRs) are the dominant radiation challenge in the open space between the planets of our solar system. The nature and energy of the particles that cause circuit upset and failure is very different in the two regimes. Two results are produced from the radiation models: determining the Total Integrated Dose (TID) experienced by avionics for a given DRM and predicting the Single Event Effects (SEE) rates for avionics during high rate exposure. These tools are applied to existing semiconductors and can be used for predicting the radiation performance of future semiconductors based on early radiation testing of new devices.

---

Very interesting read.
Hey @uiux

It’s just dawned on me, that If we are no longer a chip producer but a supplier of design (IP), what are your thoughts on who’s “chip” is being Rad Harden that are tied to NASA & BRN?

AKIDA BALLISTA
 
  • Like
  • Fire
Reactions: 6 users

uiux

Regular
Hey @uiux

It’s just dawned on me, that If we are no longer a chip producer but a supplier of design (IP), what are your thoughts on who’s “chip” is being Rad Harden that are tied to NASA & BRN?

AKIDA BALLISTA


I think it will be made available to the entire avionics community, including NASA.

The radneuro presentation mentions AFRL - who we already know is using Akida via information systems laboratories:


1661679460569.png


1661679542467.png


1661679649349.png


---

We've also seen a previous position advertised by KBR, in light of the presentations, they clearly mention all processor types undergoing research and radhardening:




RadNeuro Research Engineer

KBR is looking for a Research Engineer to work with a team to develop energy efficient, radiation tolerant and fault tolerant Neuromorphic processor for aerospace applications, with an emphasis on Lunar, deep-space, and Martian deployments. This involves first analyzing leading aerospace Neural Network (NN) applications and prototyping NN applications to determine requirements (e.g., size of NN, arithmetic precision required, frame rate required) and then mapping to representative state of the art Neural net accelerators and Neuromorphic Processors.

The RadNeuro Research Engineer will, within the RadNeuro team, develop prototypes for the demonstration of embedded Neuromorphic applications in NASA-relevant areas, such as small UAS, rovers, or nanosats for the RadNeuro project. Modern neuromorphic processors provide high performance while using minimal power for tasks like vision-based rover navigation, entry-descent-and-landing, sensor fusion, or on-board decision making. Within this task, we aim to demonstrate selected capabilities using neuromorphic co-processors (e.g., Google TPU, Intel Loihi, or Akida brainchip) with a prototypical implementation on a small existing platform (TBD).

The task involves the integration of sensors and cameras with the neuromorphic system in an architecture typically involving small, embedded boards (e.g., UP-boards, ARM-based like Raspberry PI, Nvidia Jetson, or similar) with the NN-processor connected, potentially together with some custom FPGA configuration. A small and simple controller will be implemented to drive the demonstration system. Small amounts of embedded software (C, C++, python) will need to be implemented.

In compliance with the U.S. federal government's vaccine mandate, only candidates who will be fully vaccinated for COVID-19 by December 8, 2021 or who have a reasonable accommodation or approved medical exception will be considered for this position.

***Must be a U.S. Citizen or Permanent Resident***

Required Education, Experience, & Skills:

  • Master's degree in electrical engineering, computer science or related field
  • 2 + years of relevant experience
  • A mix of hardware and software integration and testing, especially associated with radiation tolerance for advanced processors
  • Development of embedded software and low-level sensor interfaces on small platforms
  • Programming skills in C (and/or C++) and python and experience with frameworks like ArduCopter/ArduRover or ROS
  • Working knowledge in Field Programmable Gate Arrays (FPGA)
  • Some experience in customizing existing platforms (UAS, Rovers) like mounting and wiring sensors, cameras, and control computers is necessary to facilitate a quick construction and assembly of the prototype demonstrators

Preferred Experience & Skills:
  • Working knowledge in Deep Neural Networks (e.g., Keras or similar frameworks)
  • Test Engineering
  • Hardware and software testing
  • Field Programmable Gate Arrays (FPGA)
  • Technology maturation
  • Systems integration, robotics
  • UAV, rover, and/or Nanosat lab work

---

And nearly every SBIR/STTR has mentioned an "end user", these are:
  • DOE
  • MDA
  • DARPA
  • USAF
  • AFWERX
  • NASA
  • The Artemis Program
  • EVA Exploration Office
  • EVA Strategic Planning and Architecture group
  • Exploration Mission Planning Office
  • IARPA
  • AFRL
  • CCDC
  • NRO
  • NAVY
  • ARMY

It's not really much of a secret, it's all laid out in the documentation:


---

Further, the moon rover detailed in the NASA preso, said to be enabled by neuromorphic tech:

1661679683589.png



So that could mean the technology will be shared with these projects:





 
Last edited:
  • Like
  • Fire
  • Love
Reactions: 33 users

uiux

Regular

View attachment 15178

Mission Radiation Environment Modeling And Analysis: Avionics Trade Study For Gcd Rad-Neuro Project

The two main objectives of this trade study are characterizing the mission radiation environment for multiple Design Reference Missions (DRM) and analysis of radiation effects on avionics with the goal of producing radiation tolerant Neuromorphic Computing processor chips with innovative radiation-induced fault mitigation. The NASA process for defining radiation requirements for flight avionics is applied to this domain. The effects of trapped protons and electrons in the Van Allen radiation belts predominates in Low-Earth-Orbit (LEO) and the solar wind, solar flares and Galactic Cosmic Rays (GCRs) are the dominant radiation challenge in the open space between the planets of our solar system. The nature and energy of the particles that cause circuit upset and failure is very different in the two regimes. Two results are produced from the radiation models: determining the Total Integrated Dose (TID) experienced by avionics for a given DRM and predicting the Single Event Effects (SEE) rates for avionics during high rate exposure. These tools are applied to existing semiconductors and can be used for predicting the radiation performance of future semiconductors based on early radiation testing of new devices.

---

Very interesting read.


Interesting extract on analog vs digital:

Analog circuits are also affected by radiation. Unfortunately, work in this area lags far
behind work on digital circuits. For example, some of the softest components on a COTS CPU board are the voltage regulators and current sensors used for managing power. They change voltage value or fail at low TID. Replacement of these soft components with harder components is the trick for improving radiation tolerance. Radiation changes the threshold value of FETs, resulting in bias and voltage shifts. Only if these threshold changes are balanced out using circuit techniques will the analog circuit work reliably in space. Proof of radiation tolerance is by test, as these circuits tend to be novel.
 
  • Like
Reactions: 5 users

uiux

Regular

Neuromorphic Techedsat-13: The First Flight Of A Neuromorphic Processor

Neuromorphic processors, inspired by the wiring of the brain, permit certain classes of Artificial Intelligence/Machine Learning (AI/ML) algorithms to run far more efficiently. Ultimately, such systems will make the small- and nano-satellite platforms even more useful in terms of greatly improved power, communication and internal data management. In addition, on-board processing of images and data will help to not only rapidly interpret the information, but also reduce the amount of data that needs to be transmitted to ground stations. This initial flight experiment uses the Intel/Loihi processor combined with a custom interface board and three communication channels to run the AI/ML scripts. These will vary with increasing length and complexity during the course of the mission. The algorithms will use the (at first) limited sensor data to ‘learn’ - with comparisons to similar architecture in comparable ground experiments. Some of the applications for successor flights in the TES-n flight series include Cognitive Communications, whereby the overall communication system is optimized per overflight – by optimizing timing and data transmission functions. Lastly, the performance of the Loihi 14nm process technology will be monitored for performance in the LEO radiation environment, thus looking for induced hardware and software errors. This information will help guide future radiation protection techniques to extend the lifetime and overall utility. The TES-13 is a 3U nanosat successfully launched by the Virgin Orbit Launcher-1 on January 13, 2022, and will presage more flights and AI/ML applications to come.


---

This is what NASA have been doing with the orbital Loihi chip
 
  • Like
Reactions: 7 users

DJM263

LTH - 2015
I think it will be made available to the entire avionics community, including NASA.

The radneuro presentation mentions AFRL - who we already know is using Akida via information systems laboratories:


View attachment 15249

View attachment 15250

View attachment 15251

---

We've also seen a previous position advertised by KBR, in light of the presentations, they clearly mention all processor types undergoing research and radhardening:




RadNeuro Research Engineer

KBR is looking for a Research Engineer to work with a team to develop energy efficient, radiation tolerant and fault tolerant Neuromorphic processor for aerospace applications, with an emphasis on Lunar, deep-space, and Martian deployments. This involves first analyzing leading aerospace Neural Network (NN) applications and prototyping NN applications to determine requirements (e.g., size of NN, arithmetic precision required, frame rate required) and then mapping to representative state of the art Neural net accelerators and Neuromorphic Processors.

The RadNeuro Research Engineer will, within the RadNeuro team, develop prototypes for the demonstration of embedded Neuromorphic applications in NASA-relevant areas, such as small UAS, rovers, or nanosats for the RadNeuro project. Modern neuromorphic processors provide high performance while using minimal power for tasks like vision-based rover navigation, entry-descent-and-landing, sensor fusion, or on-board decision making. Within this task, we aim to demonstrate selected capabilities using neuromorphic co-processors (e.g., Google TPU, Intel Loihi, or Akida brainchip) with a prototypical implementation on a small existing platform (TBD).

The task involves the integration of sensors and cameras with the neuromorphic system in an architecture typically involving small, embedded boards (e.g., UP-boards, ARM-based like Raspberry PI, Nvidia Jetson, or similar) with the NN-processor connected, potentially together with some custom FPGA configuration. A small and simple controller will be implemented to drive the demonstration system. Small amounts of embedded software (C, C++, python) will need to be implemented.

In compliance with the U.S. federal government's vaccine mandate, only candidates who will be fully vaccinated for COVID-19 by December 8, 2021 or who have a reasonable accommodation or approved medical exception will be considered for this position.

***Must be a U.S. Citizen or Permanent Resident***

Required Education, Experience, & Skills:

  • Master's degree in electrical engineering, computer science or related field
  • 2 + years of relevant experience
  • A mix of hardware and software integration and testing, especially associated with radiation tolerance for advanced processors
  • Development of embedded software and low-level sensor interfaces on small platforms
  • Programming skills in C (and/or C++) and python and experience with frameworks like ArduCopter/ArduRover or ROS
  • Working knowledge in Field Programmable Gate Arrays (FPGA)
  • Some experience in customizing existing platforms (UAS, Rovers) like mounting and wiring sensors, cameras, and control computers is necessary to facilitate a quick construction and assembly of the prototype demonstrators

Preferred Experience & Skills:
  • Working knowledge in Deep Neural Networks (e.g., Keras or similar frameworks)
  • Test Engineering
  • Hardware and software testing
  • Field Programmable Gate Arrays (FPGA)
  • Technology maturation
  • Systems integration, robotics
  • UAV, rover, and/or Nanosat lab work

---

And nearly every SBIR/STTR has mentioned an "end user", these are:
  • DOE
  • MDA
  • DARPA
  • USAF
  • AFWERX
  • NASA
  • The Artemis Program
  • EVA Exploration Office
  • EVA Strategic Planning and Architecture group
  • Exploration Mission Planning Office
  • IARPA
  • AFRL
  • CCDC
  • NRO
  • NAVY
  • ARMY

It's not really much of a secret, it's all laid out in the documentation:


---

Further, the moon rover detailed in the NASA preso, said to be enabled by neuromorphic tech:

View attachment 15252


So that could mean the technology will be shared with these projects:






Appreciate your thoughts and attention to detail @uiux.

Your a valuable participant in the 1000 eyes.

Thank you

AKIDA BALLISTA
 
  • Like
  • Love
Reactions: 11 users
  • Like
  • Fire
Reactions: 16 users

uiux

Regular
6rf7ss.jpg
 
  • Like
Reactions: 5 users
  • Like
  • Haha
Reactions: 5 users

uiux

Regular
Program SolicitationOpen DateClose DateSelection Announcement Date
NASA STTR 2021 Phase II Proposal Period
only STTR 2021 Phase I awardees eligible to apply
May 06, 2022Due last day of Phase I contractSep 09, 2022*

I think this is actually the list to watch:

** https://sbir.nasa.gov/SBIR/abstracts/sttr22.html **


CompanyDescription
Alphacore, IncRad-Hard Adaptive Dual-Mode Event-Based Vision and Perception for Autonomous Robot Operations
Alphacore, IncMachine-Learning (ML) Enabled Reliable Multi-Modal Sensor Operation for Rocket Propulsion Systems

- In response to NASA STTR topic T4.01, Information Technologies for Intelligent and Adaptive Space Robotics, Alphacore Inc. in partnership with the Arizona State University (ASU) School of Earth and Space Exploration

- Alphacore and its Research Partner, Arizona State University



Interesting crossover here:


The Army looks forward to working with the following companies:
· Alphacore, Inc. (Tempe, Ariz.) for ANDROMEDA: Asynchronous Neuromorphic Detector Read-Out with ML-Enabled Digital Architecture
· Intellisense Systems, Inc. (Torrance, Calif.) for Radiofrequency One-Shot Learning for Emission Recognition


These items below are apparently SBIR and STTR mixed together in a list - which is confusing:

Program SolicitationView AbstractsDownload Abstracts
2022 SBIR/STTR Phase IViewDownload


These are the items I am closely watching from Phase1, to see how they morph in Phase2 (or get excluded):


CompanyDescription
NUMEMNeuromorphic Processor with radiation tolerant MRAM
Exploration InstituteNeuromorphic Electronics that Rethinks Verifiable Efficiency on Spacecraft (NERVES)
Niobium MicrosystemsScalable Neuromorphic Energy-Efficient Accelerator for Heterogeneous Processor Architectures
Brisk ComputingAdaptive Neuromorphic Processors for Cognitive Communications
Intellisense SystemsAdaptive Deep Onboard Reinforcement Bidirectional Learning System


A few of these companies have directly mentioned Akida in previous SBIR/STTR proposals. From NASA's presentation, they refer to Akida as "Freya - Norse clairvoyant shapeshifter goddess". This naming convention is interesting when compared with Loihi's "Hawiaan Northstar". Freya was meant to be a bit of a hussy who slept with a few dwarves. I am hoping this is an indicator of ubiquity.


1662444445364.png


1662444413070.png
 
Last edited:
  • Like
  • Love
  • Fire
Reactions: 30 users

uiux

Regular
Interesting one:

Program SolicitationOpen DateClose DateSelection Announcement Date
NASA SBIR Ignite 2022 Phase I SolicitationJul 12, 2022Sep 01, 2022Nov 01, 2022*
NASA STTR 2021 Phase II Proposal Period
only STTR 2021 Phase I awardees eligible to apply
May 06, 2022Due last day of Phase I contractSep 09, 2022*
NASA SBIR 2022 Phase I SolicitationJan 06, 2022Mar 09, 2022May 26, 2022
NASA STTR 2022 Phase I SolicitationJan 06, 2022Mar 09, 2022May 26, 2022
NASA SBIR 2021 Phase II Proposal Period
only SBIR 2021 Phase I awardees eligible to apply
Oct 07, 2021Due last day of Phase I contractFeb 18, 2022
NASA STTR 2020 Phase II Proposal Period
only STTR 2020 Phase I awardees eligible to apply
Aug 19, 2021Due last day of Phase I contractDec 21, 2021
NASA SBIR 2020 Phase II Proposal Period
only SBIR 2020 Phase I awardees eligible to apply
Jan 18, 2021Due last day of Phase I contractMay 13, 2021
NASA SBIR 2021 Phase I SolicitationNov 09, 2020Jan 08, 2021Mar 25, 2021
NASA STTR 2021 Phase I SolicitationNov 09, 2020Jan 08, 2021Mar 25, 2021
NASA SBIR 2020 Phase I SolicitationJan 21, 2020Apr 20, 2020Jun 30, 2020
NASA STTR 2020 Phase I SolicitationJan 21, 2020Apr 20, 2020Jun 30, 2020
* Dates are scheduled but are subject to revision
Note: Proposals must be submitted electronically by no later than 5pm ET on the last day of submissions.



---

Pattern 1: Open date and selection date being ~5-6 month window, eg. Jan -> Jun

Pattern 2: Open date / Close date / Selection date of SBIR/STTR seems to fall on the same day - unless they opened at different time

Question: When is the SBIR 2022 Phase II selection date?
 
  • Like
  • Thinking
Reactions: 5 users
Top Bottom