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and emerging unsupervised Deep Learning techniques in Big Data Artificial Intelligence
BRN - NASA
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Rob Telson has mentioned it a couple of times of late and the CEO Sean Hehir also so who or what is considering the use of wearables for health applications apart from Tata that has known links to Brainchip?
Well the following paper sets out what NASA is exploring to try and maintain and treat their astronauts as they deal with the health effects of deep space flight.
No actual opinion as to whether it is or will be AKIDA making most of this possible but what other technology can monitor all five senses on chip without connection on virtually no power millions of miles from Earth IN REAL TIME.
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Well the following paper sets out what NASA is exploring to try and maintain and treat their astronauts as they deal with the health effects of deep space flight.
No actual opinion as to whether it is or will be AKIDA making most of this possible but what other technology can monitor all five senses on chip without connection on virtually no power millions of miles from Earth IN REAL TIME.
My opinion only DYOR
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AKIDA BALLISTA
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NASA TechPort
NASA's Technology Portfolio Management System (TechPort) is a single, comprehensive resource for locating detailed information about NASA-funded technologies. Those technologies cover a broad range of areas, such as propulsion, nanotechnology, robotics, and human health. You can find useful...
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Breaking news story which could be something that AKIDA technology is well placed to compliment:
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Looking at the direction NASA are heading in recently with machine learning, sensors, artificial intelligence etc I think Akida will be playing a very big part in this.
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A little historic perspective on NASA and where Brainchip is likely fitting in noting that this what NASA were looking for in 2020:
Home
National Aeronautics and Space Administration
Branch:
N/A
Program | Phase | Year:
SBIR | Phase I | 2021
Solicitation:
SBIR_21_P1
Topic Number:
H6.22
NOTE: The Solicitations and topics listed on this site are copies from the various SBIR agency solicitations and are not necessarily the latest and most up-to-date. For this reason, you should use the agency link listed below which will take you directly to the appropriate agency server where you can read the official version of this solicitation and download the appropriate forms and rules.
The official link for this solicitation is:https://sbir.gsfc.nasa.gov/solicitations
Release Date:
November 09, 2020
Open Date:
November 09, 2020
Application Due Date:
January 08, 2021
Close Date:
January 08, 2021
Description:
Lead Center: GRC
Participating Centers: ARC
Scope Title:
This subtopic specifically focuses on advances in signal and data processing. Neuromorphic processing will enable NASA to meet growing demands for applying artificial intelligence and machine learning algorithms onboard a spacecraft to optimize and automate operations. This includes enabling cognitive systems to improve mission communication and data-processing capabilities, enhance computing performance, and reduce memory requirements. Neuromorphic processors can enable a spacecraft to sense, adapt, act, and learn from its experiences and from the unknown environment without necessitating involvement from a mission operations team. Additionally, this processing architecture shows promise for addressing the power requirements that traditional computing architectures now struggle to meet in space applications.
The goal of this program is to develop neuromorphic processing software, hardware, algorithms, architectures, simulators, and techniques as enabling capability for autonomous space operations. Emerging memristor and other radiation-tolerant devices, which show potential for addressing the need for energy-efficient neuromorphic processors and improved signal processing capability, are of particular interest due to their resistance to the effects of radiation.
Additional areas of interest for research and/or technology development include: (a) spiking algorithms that learn from the environment and improve operations, (b) neuromorphic processing approaches to enhance data processing, computing performance, and memory conservation, and (c) new brain-inspired chips and breakthroughs in machine understanding/intelligence. Novel memristor approaches that show promise for space applications are also sought.
This subtopic seeks innovations focusing on low-size, -weight, and -power (-SWaP) applications suitable to lunar orbital or surface operations, thus enabling efficient onboard processing at lunar distances. Focusing on SWaP-constrained platforms opens up the potential for applying neuromorphic processors in spacecraft or robotic control situations traditionally reserved for power-hungry general-purpose processors. This technology will allow for increased speed, energy efficiency, and higher performance for computing in unknown and uncharacterized space environments including the Moon and Mars. Proposed innovations should justify their SWaP advantages and target metrics over the comparable relevant state of the art.
Expected TRL or TRL Range at completion of the Project: 4 to 6
Primary Technology Taxonomy:
Level 1: TX 10 Autonomous Systems
Level 2: TX 10.1 Situational and Self Awareness
Desired Deliverables of Phase I and Phase II:
Phase I will emphasize research aspects for technical feasibility and show a path toward a Phase II proposal. Phase I deliverables include concept of operations of the research topic, simulations, and preliminary results. Early development and delivery of prototype hardware/software is encouraged.
Phase II will emphasize hardware and/or software development with delivery of specific hardware and/or software products for NASA, targeting demonstration operations on a low-SWaP platform. Phase II deliverables include a working prototype of the proposed product and/or software, along with documentation and tools necessary for NASA to use the product and/or modify and use the software. In order to enable mission deployment, proposed prototypes should include a path, preferably demonstrated, for fault and mission tolerances. Phase II deliverables should include hardware/software necessary to show how the advances made in the development can be applied to a CubeSat, SmallSat, and rover flight demonstration.
State of the Art and Critical Gaps:
The current state of the art (SOA) for in-space processing is the High Performance Spaceflight Computing (HPSC) processor being developed by Boeing for NASA Goddard Space Flight Center (GSFC). The HPSC, called the Chiplet, contains 8 general purpose processing cores in a dual quad-core configuration. Delivery is expected by December 2022. In a submission to the Space Technology Mission Directorate (STMD) Game Changing Development (GCD) program, the highest computational capability required by a typical space mission is 35 to 70 GFLOPS (billion fast logical operations per second).
The current SOA does not address the capabilities required for artificial intelligence and machine learning applications in the space environment. These applications require significant amounts of multiply and accumulate operations, in addition to a substantial amount of memory to store data and retain intermediate states in a neural network computation. Terrestrially, these operations require general-purpose graphics processing units (GP-GPUs), which are capable of teraflops (TFLOPS) each—approximately 3 orders of magnitude above the anticipated capabilities of the HPSC.
Neuromorphic processing offers the potential to bridge this gap through a novel hardware approach. Existing research in the area shows neuromorphic processors to be up to 1,000 times more energy efficient than GP-GPUs in artificial intelligence applications. Obviously, the true performance depends on the application, but nevertheless the architecture has demonstrated characteristics that make it well-adapted to the space environment.
Relevance / Science Traceability:
The Cognitive Communications Project, through the Human Exploration and Operations Mission Directorate (HEOMD) Space Communications and Navigation (SCaN) Program, is one potential customer of work from this subtopic area. Neuromorphic processors are a key enabler to the cognitive radio and system architecture envisioned by this project. As communications become more complex, cognition and automation will play a larger role to mitigate complexity and reduce operations costs. Machine learning will choose radio configurations and adjust for impairments and failures. Neuromorphic processors will address the power requirements that traditional computing architectures now struggle to meet and are of relevance to Lunar return and Mars for autonomous operations, as well as of interest to HEOMD and Science Mission Directorate (SMD) for in situ avionics capabilities.
Home
Deep Neural Net and Neuromorphic Processors for In-Space Autonomy and Cognition
Agency:National Aeronautics and Space Administration
Branch:
N/A
Program | Phase | Year:
SBIR | Phase I | 2021
Solicitation:
SBIR_21_P1
Topic Number:
H6.22
NOTE: The Solicitations and topics listed on this site are copies from the various SBIR agency solicitations and are not necessarily the latest and most up-to-date. For this reason, you should use the agency link listed below which will take you directly to the appropriate agency server where you can read the official version of this solicitation and download the appropriate forms and rules.
The official link for this solicitation is:https://sbir.gsfc.nasa.gov/solicitations
Release Date:
November 09, 2020
Open Date:
November 09, 2020
Application Due Date:
January 08, 2021
Close Date:
January 08, 2021
Description:
Lead Center: GRC
Participating Centers: ARC
Scope Title:
Neuromorphic Capabilities
Scope Description:This subtopic specifically focuses on advances in signal and data processing. Neuromorphic processing will enable NASA to meet growing demands for applying artificial intelligence and machine learning algorithms onboard a spacecraft to optimize and automate operations. This includes enabling cognitive systems to improve mission communication and data-processing capabilities, enhance computing performance, and reduce memory requirements. Neuromorphic processors can enable a spacecraft to sense, adapt, act, and learn from its experiences and from the unknown environment without necessitating involvement from a mission operations team. Additionally, this processing architecture shows promise for addressing the power requirements that traditional computing architectures now struggle to meet in space applications.
The goal of this program is to develop neuromorphic processing software, hardware, algorithms, architectures, simulators, and techniques as enabling capability for autonomous space operations. Emerging memristor and other radiation-tolerant devices, which show potential for addressing the need for energy-efficient neuromorphic processors and improved signal processing capability, are of particular interest due to their resistance to the effects of radiation.
Additional areas of interest for research and/or technology development include: (a) spiking algorithms that learn from the environment and improve operations, (b) neuromorphic processing approaches to enhance data processing, computing performance, and memory conservation, and (c) new brain-inspired chips and breakthroughs in machine understanding/intelligence. Novel memristor approaches that show promise for space applications are also sought.
This subtopic seeks innovations focusing on low-size, -weight, and -power (-SWaP) applications suitable to lunar orbital or surface operations, thus enabling efficient onboard processing at lunar distances. Focusing on SWaP-constrained platforms opens up the potential for applying neuromorphic processors in spacecraft or robotic control situations traditionally reserved for power-hungry general-purpose processors. This technology will allow for increased speed, energy efficiency, and higher performance for computing in unknown and uncharacterized space environments including the Moon and Mars. Proposed innovations should justify their SWaP advantages and target metrics over the comparable relevant state of the art.
Expected TRL or TRL Range at completion of the Project: 4 to 6
Primary Technology Taxonomy:
Level 1: TX 10 Autonomous Systems
Level 2: TX 10.1 Situational and Self Awareness
Desired Deliverables of Phase I and Phase II:
- Prototype
- Hardware
- Software
Phase I will emphasize research aspects for technical feasibility and show a path toward a Phase II proposal. Phase I deliverables include concept of operations of the research topic, simulations, and preliminary results. Early development and delivery of prototype hardware/software is encouraged.
Phase II will emphasize hardware and/or software development with delivery of specific hardware and/or software products for NASA, targeting demonstration operations on a low-SWaP platform. Phase II deliverables include a working prototype of the proposed product and/or software, along with documentation and tools necessary for NASA to use the product and/or modify and use the software. In order to enable mission deployment, proposed prototypes should include a path, preferably demonstrated, for fault and mission tolerances. Phase II deliverables should include hardware/software necessary to show how the advances made in the development can be applied to a CubeSat, SmallSat, and rover flight demonstration.
State of the Art and Critical Gaps:
The current state of the art (SOA) for in-space processing is the High Performance Spaceflight Computing (HPSC) processor being developed by Boeing for NASA Goddard Space Flight Center (GSFC). The HPSC, called the Chiplet, contains 8 general purpose processing cores in a dual quad-core configuration. Delivery is expected by December 2022. In a submission to the Space Technology Mission Directorate (STMD) Game Changing Development (GCD) program, the highest computational capability required by a typical space mission is 35 to 70 GFLOPS (billion fast logical operations per second).
The current SOA does not address the capabilities required for artificial intelligence and machine learning applications in the space environment. These applications require significant amounts of multiply and accumulate operations, in addition to a substantial amount of memory to store data and retain intermediate states in a neural network computation. Terrestrially, these operations require general-purpose graphics processing units (GP-GPUs), which are capable of teraflops (TFLOPS) each—approximately 3 orders of magnitude above the anticipated capabilities of the HPSC.
Neuromorphic processing offers the potential to bridge this gap through a novel hardware approach. Existing research in the area shows neuromorphic processors to be up to 1,000 times more energy efficient than GP-GPUs in artificial intelligence applications. Obviously, the true performance depends on the application, but nevertheless the architecture has demonstrated characteristics that make it well-adapted to the space environment.
Relevance / Science Traceability:
The Cognitive Communications Project, through the Human Exploration and Operations Mission Directorate (HEOMD) Space Communications and Navigation (SCaN) Program, is one potential customer of work from this subtopic area. Neuromorphic processors are a key enabler to the cognitive radio and system architecture envisioned by this project. As communications become more complex, cognition and automation will play a larger role to mitigate complexity and reduce operations costs. Machine learning will choose radio configurations and adjust for impairments and failures. Neuromorphic processors will address the power requirements that traditional computing architectures now struggle to meet and are of relevance to Lunar return and Mars for autonomous operations, as well as of interest to HEOMD and Science Mission Directorate (SMD) for in situ avionics capabilities.
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Back in 2008 before NASA had ever heard of Brainchip’s Digital AKIDA technology for SCNN the following paper was published. Just more speculation around the secret other things that Brainchip is working on beyond vision with NASA as mentioned by Rob Telson.
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AKIDA BALLISTA
Artificial neural networks comprising spiking neurons of a novel type have been conceived as improved pattern- analysis and pattern- recognition computational systems. These neurons are represented by a mathematical model denoted the state- variable model (SVM), which among other things, exploits a computational parallelism inherent in spiking-neuron geometry. Networks of SVM neurons offer advantages of speed and computational efficiency, relative to traditional artificial neural networks. The SVM also overcomes some of the limitations of prior spiking-neuron models. There are numerous potential pattern-recognition, tracking, and data-reduction (data preprocessing) applications for these SVM neural networks on Earth and in exploration of remote planets.
Spiking neurons imitate biological neurons more closely than do the neurons of traditional artificial neural networks. A spiking neuron includes a central cell body (soma) surrounded by a treelike interconnection network (dendrites). Spiking neurons are so named because they generate trains of output pulses (spikes) in response to inputs received from sensors or from other neurons. They gain their speed advantage over traditional neural networks by using the timing of individual spikes for computation, whereas traditional artificial neurons use averages of activity levels over time. Moreover, spiking neurons use the delays inherent in dendritic processing in order to efficiently encode the information content of incoming signals. Because traditional artificial neurons fail to capture this encoding, they have less processing capability, and so it is necessary to use more gates when implementing traditional artificial neurons in electronic circuitry. Such higher-order functions as dynamic tasking are effected by use of pools (collections) of spiking neurons interconnected by spike-transmitting fibers.
The SVM includes adaptive thresholds and submodels of transport of ions (in imitation of such transport in biological neurons). These features enable the neurons to adapt their responses to high-rate inputs from sensors, and to adapt their firing thresholds to mitigate noise or effects of potential sensor failure. The mathematical derivation of the SVM starts from a prior model, known in the art as the point soma model, which captures all of the salient properties of neuronal response while keeping the computational cost low. The point-soma latency time is modified to be an exponentially decaying function of the strength of the applied potential.
Choosing computational efficiency over biological fidelity, the dendrites surrounding a neuron are represented by simplified compartmental submodels and there are no dendritic spines. Updates to the dendritic potential, calcium- ion concentrations and conductances, and potassium-ion conductances are done by use of equations similar to those of the point soma. Diffusion processes in dendrites are modeled by averaging among nearest-neighbor compartments. Inputs to each of the dendritic compartments come from sensors.Alternatively or in addition, when an affected neuron is part of a pool, inputs can come from other spiking neurons.
At present, SVM neural networks are implemented by computational simulation, using algorithms that encode the SVM and its submodels. However, it should be possible to implement these neural networks in hardware: The differential equations for the dendritic and cellular processes in the SVM model of spiking neurons map to equivalent circuits that can be implemented directly in analog very-large-scale integrated (VLSI) circuits.
This work was done by Terrance Huntsberger of Caltech for NASA's Jet Propulsion Laboratory.
NPO- 40945
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MAY 1, 2008 | INFORMATION TECHNOLOGY
Spiking Neurons for Analysis of Patterns
High-performance pattern-analysis systems could be implemented as analog VLSI circuits.
Artificial neural networks comprising spiking neurons of a novel type have been conceived as improved pattern- analysis and pattern- recognition computational systems. These neurons are represented by a mathematical model denoted the state- variable model (SVM), which among other things, exploits a computational parallelism inherent in spiking-neuron geometry. Networks of SVM neurons offer advantages of speed and computational efficiency, relative to traditional artificial neural networks. The SVM also overcomes some of the limitations of prior spiking-neuron models. There are numerous potential pattern-recognition, tracking, and data-reduction (data preprocessing) applications for these SVM neural networks on Earth and in exploration of remote planets.
Spiking neurons imitate biological neurons more closely than do the neurons of traditional artificial neural networks. A spiking neuron includes a central cell body (soma) surrounded by a treelike interconnection network (dendrites). Spiking neurons are so named because they generate trains of output pulses (spikes) in response to inputs received from sensors or from other neurons. They gain their speed advantage over traditional neural networks by using the timing of individual spikes for computation, whereas traditional artificial neurons use averages of activity levels over time. Moreover, spiking neurons use the delays inherent in dendritic processing in order to efficiently encode the information content of incoming signals. Because traditional artificial neurons fail to capture this encoding, they have less processing capability, and so it is necessary to use more gates when implementing traditional artificial neurons in electronic circuitry. Such higher-order functions as dynamic tasking are effected by use of pools (collections) of spiking neurons interconnected by spike-transmitting fibers.
The SVM includes adaptive thresholds and submodels of transport of ions (in imitation of such transport in biological neurons). These features enable the neurons to adapt their responses to high-rate inputs from sensors, and to adapt their firing thresholds to mitigate noise or effects of potential sensor failure. The mathematical derivation of the SVM starts from a prior model, known in the art as the point soma model, which captures all of the salient properties of neuronal response while keeping the computational cost low. The point-soma latency time is modified to be an exponentially decaying function of the strength of the applied potential.
Choosing computational efficiency over biological fidelity, the dendrites surrounding a neuron are represented by simplified compartmental submodels and there are no dendritic spines. Updates to the dendritic potential, calcium- ion concentrations and conductances, and potassium-ion conductances are done by use of equations similar to those of the point soma. Diffusion processes in dendrites are modeled by averaging among nearest-neighbor compartments. Inputs to each of the dendritic compartments come from sensors.Alternatively or in addition, when an affected neuron is part of a pool, inputs can come from other spiking neurons.
At present, SVM neural networks are implemented by computational simulation, using algorithms that encode the SVM and its submodels. However, it should be possible to implement these neural networks in hardware: The differential equations for the dendritic and cellular processes in the SVM model of spiking neurons map to equivalent circuits that can be implemented directly in analog very-large-scale integrated (VLSI) circuits.
This work was done by Terrance Huntsberger of Caltech for NASA's Jet Propulsion Laboratory.
NPO- 40945
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Should have mentioned the former CEO Mr. Dinardo was fond of saying “what AKIDA is doing is recognising patterns, give us any pattern, any pattern at all”.
Probably just a coincidence that not long after he said this for the umpteenth time Vorago then NASA became EAPs.
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Space Use-Case: Onboard Satellite Image Classification
Satellite imagery is the most important sector of space industry, as around 38% of satellites are fully dedicated to earth observation. Then, for each sample, the error gradient is propagated backward in the network, and the weights are adjusted to minimise this...
An interesting paper proposing another use case that a COTS AKIDA 90nm is well suited to perform.
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Fullmoonfever
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Just having a surf for any keywords and this popped up from a few days ago.
The paper itself is not driven by Akida (& not that I even understand a 10th of the tech side of it haha) but what I found interesting was some background details of one of the main authors - Konstantinos.
Link to paper below and snip of his background...yes we know of NASA and I probs missed it (?) re cybersecurity integration....."and is used by"
Been noted before?
Tactical Imagery Intelligence Operations by Cloud Robotics and Artificial Intelligence
February 2022
Project: Cyberdefense of Urban Critical Infrastructure
Authors:
Konstantinos Demertzis
Democritus University of Thrace
Lykourgos Magafas
www.researchgate.net
The paper itself is not driven by Akida (& not that I even understand a 10th of the tech side of it haha) but what I found interesting was some background details of one of the main authors - Konstantinos.
Link to paper below and snip of his background...yes we know of NASA and I probs missed it (?) re cybersecurity integration....."and is used by"
Been noted before?
Tactical Imagery Intelligence Operations by Cloud Robotics and Artificial Intelligence
February 2022
Project: Cyberdefense of Urban Critical Infrastructure
Authors:
Konstantinos Demertzis
Democritus University of Thrace
Lykourgos Magafas
(PDF) Tactical Imagery Intelligence Operations by Cloud Robotics and Artificial Intelligence
PDF | Intelligence service, in government and military operations, evaluates information on the strength, activities, and possible course of action of... | Find, read and cite all the research you need on ResearchGate
www.researchgate.net
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Thanks Rocket577 the favour is returned. This seems a very promising lead to what NASA and Brainchip have been working on in the vision sphere:
Something for the 1,000 Eyes to consider unless uiux has already been here and knows the answer. LOL
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The following paragraph is where I see the opportunity for AKIDA technology to have a role:
"Unlike conventional edge detection systems, which rely on both a high-speed camera and a bulky computer or digital signal processor, this innovation uses an analog technique to process images. Its simple, sleek design consists of three basic parts: a linear image sensor, an analog signal processing circuit, and a digital circuit. The result is a smaller, more reliable technology with increased processing frame rates. The design can easily be tailored to the end use, and can be reconfigured to respond to positive and/or negative going edges. Furthermore, the threshold sensitivity can be varied and algorithmically set, making it well suited for a number of other terrestrial applications from transportation to manufacturing."
Could this "digital circuit" be a Renesas MCU with AKIDA on board???
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Hi Rocket577
A quick keyword spot on my part had my attention grabbed by the following extract:
"AI for Quantum Computing • Framework for Mining and Analysis of Petabyte-size Time-series on the NASA Earth Exchange (NEX) (Michaelis & Nemani/AIST-16) • An Assessment of Hybrid Quantum Annealing Approaches for Inferring and Assimilating Satellite Surface Flux Data into Global Land Surface Models (Halem/AIST-16)"
This has resonance because Quantum annealing and spiking neural networks was a NASA research paper which as I stated previously armed with some questions from Dio I emailed Brainchip and received and acknowledgment stating they would come back to me and now some months have passed and despite a gentle reminder no response has been forthcoming. And while this extract references Assimilating Satellite Surface Fluz Data for Global Land Surface Models in relation to Mining and Analysis it would also be something required by the program where NASA is funding the army of tiny Ai intelligent mining robots which according to the two researchers was looking at spiking neural network technology. This has been posted about a few times but more recently over here by myself.
If the spider web of connections my brain is putting together is even half correct these are "exciting times" and why Peter van der Made and Mr. Dinardo both felt a younger leader was needed.
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2024 - Quantum Technology Partnership from NASA and Microsoft
It has been announced that NASA and Microsoft have partnered to use quantum technologies in space missions. According to the news, NASA will be able to
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At times like these it is important to consider the known facts so that this type of speculation does not seem at all fanciful in my opinion:
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ARTIFICIAL INTELLIGENCE, NEWS
Nichols MartinJanuary 10, 2022, 11:56 am
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BrainChip Holdings has added Information Systems Laboratories in its Early Access Program and will provide the latter with assistance in the development of an artificial intelligence technology meant to support the Air Force Research Laboratory’s radar projects.
ISL is developing the technology based on BrainChip’s Akida neural networking processor, which uses a neuromorphic architecture to support edge-based AI growth, the Australian software company said Sunday.
Akida is designed to mimic the human brain’s spiking nature and support edge AI applications including learning, inference and on-chip training.
ISL can assess the ultra-low-power processor and utilize related engineering resources as a member of BrainChip’s EAP.
“As part of BrainChip’s EAP, we’ve had the opportunity to evaluate firsthand the capabilities that Akida provides to the AI ecosystem,” said Jamie Bergin, senior vice president and manager for ISL’s research, development and engineering solutions division.
ISL provides expertise, research and engineering in multiple areas including signal processing, undersea technologies, cybersecurity, surveillance and advanced radar systems."
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ARTIFICIAL INTELLIGENCE, NEWS
BrainChip-Made AI Processor Used in New Tech for AFRL Radar Projects
by
Nichols MartinJanuary 10, 2022, 11:56 am
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BrainChip Holdings has added Information Systems Laboratories in its Early Access Program and will provide the latter with assistance in the development of an artificial intelligence technology meant to support the Air Force Research Laboratory’s radar projects.
ISL is developing the technology based on BrainChip’s Akida neural networking processor, which uses a neuromorphic architecture to support edge-based AI growth, the Australian software company said Sunday.
Akida is designed to mimic the human brain’s spiking nature and support edge AI applications including learning, inference and on-chip training.
ISL can assess the ultra-low-power processor and utilize related engineering resources as a member of BrainChip’s EAP.
“As part of BrainChip’s EAP, we’ve had the opportunity to evaluate firsthand the capabilities that Akida provides to the AI ecosystem,” said Jamie Bergin, senior vice president and manager for ISL’s research, development and engineering solutions division.
ISL provides expertise, research and engineering in multiple areas including signal processing, undersea technologies, cybersecurity, surveillance and advanced radar systems."
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