BRN Discussion Ongoing
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Hi Bravo,Hi FMF,
Lockheed Martin Australia have been working in partnership with QinetiQ Australia on the AIR6500 solution. This is a project that coud reportedly lead to a global franchise with an export market estimated to be worth $83 billion AUD.
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Lockheed wins crucial Aussie Air6500 All Domain deal for $765M
“This critical capability will allow the ADF to leverage information from across all domains at greater speeds, with better accuracy and at a greater scale than it is capable of today,” Stephanie Hill, executive vice president of Lockheed Martin Rotary and Mission Systems, said in a statement.
By Colin Clarkon August 28, 2023 at 11:41 AM
Lockheed Martin Air 6500 image. (Lockheed Martin Australia)
SYDNEY — In a major contract award that could lead to a global franchise, Australia has awarded Lockheed Martin $765 million AUD ($487 million US) for the first phase of building what it calls Air6500.
Lockheed beat Northrop Grumman for the right to produce an Australian project that comes with an export market the defense giant estimates to be worth $83 billion AUD ($55 billion US) A statement from the Australian Defense Ministry says the system is expected to grow to become “a multibillion dollar program.”
Lockheed started working on a version of Air6500 seven years ago, before it was known by that name, and has invested roughly $100 million of its own money developing it. That includes doubling the size of its Australian workforce to 200 to develop the project. The project started with 10 Americans and 10 Australians.
One of the most intriguing capabilities that Lockheed is building into its system is what the company is claiming as a world-first passive radar system by an Australian company called Silentium.
The passive radar works by tracking reflections of objects from FM radio waves. Multiples of the relatively low-cost system can be deployed around the world to track a wide array of objects from Low Earth Orbit to the surface of the sea. It won’t replace active scanning radar, which is crucial for targeting, but would provide an important cueing capability to active radar. Of course, passive radar will not attract enemy fire since it does not emit.
The Australian company Consunet provides Lockheed’s Air6500 with an electromagnetic battle management subsystem which allows pilots and weapons to find ways through radar and other detection nets to avoid detection. It includes a visual system to help plan missions.
Lockheed and Australian defense officials have been keenly aware that if they can develop an ITAR-free system — one that is not burdened by any US export restrictions — they could sell the system to a myriad of countries eager to use a system used by a Five Eyes country, one that thus meets US security and other requirements.
While Australia doesn’t use the term All Domain Operations, more than a dozen industry and government officials have acknowledged in background conversations that Air6500 would provide many of the capabilities expected by the US of an All Domain system, one able to connect sensors and communications from space to air to ground to the sea and below and across to the cyber domain. Lockheed’s system is based on an open architecture.
“Australia’s AIR6500-1 program is truly transformational. It will set the blueprint for future military Joint All-Domain Operations across the globe.” Stephanie Hill, executive vice president of Lockheed Martin Rotary and Mission Systems, said in a company statement. “This critical capability will allow the ADF to leverage information from across all domains at greater speeds, with better accuracy and at a greater scale than it is capable of today.”
Lockheed was the first US defense prime to make a company-wide commitment to All Domain Operations and has been pursuing capabilities relevant to it across the enterprise. To get some idea of how important Air6500 has been seen by Lockheed Martin, note that the company flew its chief operations officer, Frank St. John, here for the final oral presentations to the Australian Department of Defense.
“The project is likely to generate up to 230 jobs, including for subcontractors, in high-tech areas including software development, systems engineering, project management and logistics. Around 150 jobs will be in South Australia, 60 in the NSW Hunter region, with others in Brisbane and Canberra,” a Defense Department statement released just after midnight local time said.
“This first-of-its-kind system will provide greater situational awareness and defence against increasingly advanced air and missile threats, as well as give the ADF increased levels of interoperability with the United States and allied partners,” the statement says.
Lockheed Martin Australia has already awarded contracts to more than 10 leading-edge companies such as Leidos Australia, Consunet, Consilium, C4I, Silentium, Penten, Lucid Consulting Engineering, and engaged with US prime contractors Raytheon and Boeing during the risk reduction phase to develop their AIR6500 offering.
Lockheed wins crucial Aussie Air6500 All Domain deal for $765M - Breaking Defense
“This critical capability will allow the ADF to leverage information from across all domains at greater speeds, with better accuracy and at a greater scale than it is capable of today,” Stephanie Hill, executive vice president of Lockheed Martin Rotary and Mission Systems, said in a statement.breakingdefense.com
That Silentium silent radar detecting objects passively from "ambient" radio signals would certainly benefit from TeNNs in sorting out the wheat from the chaff. There would be a lot of messy signals and a lot of object classification to do. Something like noise cancellation?
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Is that the cup with the tea leaves?
Nudging 8 million by 11:30 at 21 cents - that's about 9% more than 19.3 cents, so we can assume it's not eligible shareholders.
db1969oz
Regular
The cynic in me assumes this pump gives the boyos club a quick 10% profit on their 0.193c shares, and gives their shorting buddies some room to make 10% on the way back down! Yes, the negativity has worn off on me of late.
FiveBucks
Regular
I hate to say I probably agree but do I have the conviction to sell today and hope to buy in cheaper? Probably not.
FiveBucks
Regular
Unfortunately, I think we know the answer to that.
db1969oz
Regular
It's a thought, but knowing my luck i'd be out as the next IP deal dropped.
HopalongPetrovski
I'm Spartacus!
They would need to have been based in Australia to participate and the two that I can think of already have plenty of exposure as part of their remuneration package. RSU's etc.
HopalongPetrovski
I'm Spartacus!
It's just that intoxicatingly heady mix of greed and fear that keeps us all here Buddy.
buena suerte :-)
BOB Bank of Brainchip
Hello !!!!...Nice 
Gogo FOMO!
Looks like those expecting post-
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I was gunna say that!
Bravo
If ARM was an arm, BRN would be its biceps💪!
I don't think I've seen this web page posted before, but apologies if it has.
Anyway we're in good company (Army Research Office, Intel, Quantum Ventura, DOE, Facebook Reality Labs, etc).
Anyway we're in good company (Army Research Office, Intel, Quantum Ventura, DOE, Facebook Reality Labs, etc).
Neuromorphic Computing Lab – Bridging the gap between Nanoelectronics, Neuroscience and Machine Learning
Bridging the gap between Nanoelectronics, Neuroscience and Machine Learning
sites.psu.edu
Bravo
If ARM was an arm, BRN would be its biceps💪!
Abhronil Sengupta is an Associate Professor at Penn State. He has also been involved in the cyber-neuro RT project, along with other authors from Quantum Ventura.
Abhronil Sengupta is the Joseph R. and Janice M. Monkowski Career Development Assistant Professor of Electrical Engineering at Penn State. Credit: Poornima Tomy/Penn State. All Rights Reserved.
Expand
April 17, 2024
By Lauren Colvin
UNIVERSITY PARK, Pa. — Abhronil Sengupta, the Joseph R. and Janice M. Monkowski Career Development Assistant Professor of Electrical Engineering at Penn State, was granted a three-year, $360,000 Early Career Program Award from the Army Research Office (ARO). The award supports “early career scientists and engineers who show exceptional ability and promise for conducting basic research,” according to the Army Research Laboratory (ARL).
The ARO Early Career Program Award targets scientists and engineers who have held a tenure track position at a U.S. institution of higher education for fewer than five years at the time of application. According to an announcement published by the ARL, the award’s objective is “to foster creative basic research in science and engineering; enhance development of outstanding early career investigators; and increase opportunities for early career investigators to pursue research in areas relevant to the Army.”
Sengupta leads the Neuromorphic Computing Lab at Penn State, where his group explores next-generation, brain-inspired artificial intelligence systems that forge stronger connections with neuroscience in order to circumvent algorithmic and hardware scaling challenges of current deep learning solutions. His work on neuromorphic computing has also been recognized with a NSF CAREER Award, IEEE Electron Devices Society Early Career Award and the IEEE Circuits and Systems Society Outstanding Young Author Award, among others.
Penn State News spoke with Sengupta about the research to be conducted with the grant.
Q: What is the goal of this research?
Sengupta: Current brain-inspired engineered systems have primarily focused on the emulation of bio-plausible computational models of neurons and synapses, but incorporation of other cellular units from the brain is lacking. The Early Career project explores a holistic system-science enabled perspective to design brain-inspired learning systems by understanding the role of astrocytes. Astrocytes are an under-explored yet critical component of the brain responsible for enabling rich temporal dynamics such as neural synchronization that form the dynamical basis for learning and memory. Specifically, our project focuses on spinal central pattern generators (CPGs). CPGs are neural circuits that generate spontaneous rhythmic patterns and serve as a “brain-like” model system to design engineered learning platforms. Building upon theoretical neuroscience insights, the project will address the unmet need of understanding the key aspects of bio-fidelity required for the design of astromorphic algorithms and hardware in the context of autonomous robotic locomotion tasks.
Q: How do you plan to achieve these goals?
Sengupta: The research involves a transformative research agenda at the intersection of theoretical neuroscience, algorithms and hardware to decode and embed astrocyte functionality in brain-inspired algorithm and hardware design. The project spans complementary and intertwined explorations across multiple focus areas, ranging from neuromorphic CPG modelling, algorithmic formulations to design CPGs for robotic locomotion tasks capable of online local learning and novel energy-efficient, spin-based devices and circuits that inherently exploit the intrinsic device physics for mimicking astrocyte functionalities.
Such an end-to-end framework combining principles across the life, physical and computer sciences has the potential to enable a paradigm shift in the design of dynamical intelligent systems and have a long-term impact on the National Academy of Engineering’s Grand Challenge related to “Reverse-Engineer the Brain." Implementation of energy-efficient astromorphic hardware and algorithms will be critical for a large spectrum of Department of Defense applications for enabling real-time intelligence in autonomous systems like unmanned military robotic systems, among others.
Q: How does your research differ from what’s already been done regarding robotic locomotion?
Sengupta: In recent years, global policy optimization for robotic locomotion through reinforcement learning has been widely investigated. However, training such systems is computationally challenging, limiting its applicability for on-chip learning in edge robotic systems with resource constraints.
Existing work on brain-inspired CPG uses over-simplified neuron models and network architectures. The lack of inclusion of biological details in the CPG architecture has resulted in limited flexibility of the control system, thereby constraining their applicability to mostly simple robotic platforms like hexapod robots in contrast to the more complex design space of real-world robotic locomotion control of more intensively researched models, such as quadruped robots. In this project, we develop a detailed bio-inspired CPG model and propose to show that astrocyte control is instrumental to ensure optimal and stable gait emergence in robotic quadruped locomotion systems. Local learning mediated gait emergence ensures compatibility of our proposed control system with neuromorphic hardware, thereby leading to the potential of enabling real-time, low-power on-chip learning.
Q: In what ways has Penn State helped to enable this research?
Sengupta: The focus on multidisciplinary research at Penn State and our lab’s affiliation and collaboration with researchers in the Center for Artificial Intelligence Foundations and Engineered Systems (CAFE) and Materials Research Institute (MRI) at Penn State has been instrumental in enabling this research. I am also highly indebted to the U.S. National Science Foundation which funded our initial research in the field of astrocyte based neuromorphic computing through the Early Concept Grant for Exploratory Research (EAGER) program which is specifically targeted for interdisciplinary high-risk, high-payoff projects with a transformative scope.
Q: What are you most excited about?
Sengupta: The cross-cutting nature of the project with multiple disciplines excites me the most. The key distinguishing point of our project lies in the fact that we are striving to utilize computational neuroscience insights of astrocyte signaling enabled CPG controller design for legged robotic locomotion while parallelly investigating novel devices and circuits which can mimic astrocyte functionalities through their intrinsic physics. We believe such an interplay across the stack of theoretical neuroscience, algorithms and hardware can result in the development of a new generation of computationally efficient autonomous robotic neuromorphic platforms that are able to adapt to changing environment in an online fashion.
Last Updated April 17, 2024
www.psu.edu
Q&A: Exploring brain-inspired engineered systems with Abhronil Sengupta
Electrical engineering professor receives Army Research Office's Early Career Program Award
Abhronil Sengupta is the Joseph R. and Janice M. Monkowski Career Development Assistant Professor of Electrical Engineering at Penn State. Credit: Poornima Tomy/Penn State. All Rights Reserved.
Expand
April 17, 2024
By Lauren Colvin
UNIVERSITY PARK, Pa. — Abhronil Sengupta, the Joseph R. and Janice M. Monkowski Career Development Assistant Professor of Electrical Engineering at Penn State, was granted a three-year, $360,000 Early Career Program Award from the Army Research Office (ARO). The award supports “early career scientists and engineers who show exceptional ability and promise for conducting basic research,” according to the Army Research Laboratory (ARL).
The ARO Early Career Program Award targets scientists and engineers who have held a tenure track position at a U.S. institution of higher education for fewer than five years at the time of application. According to an announcement published by the ARL, the award’s objective is “to foster creative basic research in science and engineering; enhance development of outstanding early career investigators; and increase opportunities for early career investigators to pursue research in areas relevant to the Army.”
Sengupta leads the Neuromorphic Computing Lab at Penn State, where his group explores next-generation, brain-inspired artificial intelligence systems that forge stronger connections with neuroscience in order to circumvent algorithmic and hardware scaling challenges of current deep learning solutions. His work on neuromorphic computing has also been recognized with a NSF CAREER Award, IEEE Electron Devices Society Early Career Award and the IEEE Circuits and Systems Society Outstanding Young Author Award, among others.
Penn State News spoke with Sengupta about the research to be conducted with the grant.
Q: What is the goal of this research?
Sengupta: Current brain-inspired engineered systems have primarily focused on the emulation of bio-plausible computational models of neurons and synapses, but incorporation of other cellular units from the brain is lacking. The Early Career project explores a holistic system-science enabled perspective to design brain-inspired learning systems by understanding the role of astrocytes. Astrocytes are an under-explored yet critical component of the brain responsible for enabling rich temporal dynamics such as neural synchronization that form the dynamical basis for learning and memory. Specifically, our project focuses on spinal central pattern generators (CPGs). CPGs are neural circuits that generate spontaneous rhythmic patterns and serve as a “brain-like” model system to design engineered learning platforms. Building upon theoretical neuroscience insights, the project will address the unmet need of understanding the key aspects of bio-fidelity required for the design of astromorphic algorithms and hardware in the context of autonomous robotic locomotion tasks.
Q: How do you plan to achieve these goals?
Sengupta: The research involves a transformative research agenda at the intersection of theoretical neuroscience, algorithms and hardware to decode and embed astrocyte functionality in brain-inspired algorithm and hardware design. The project spans complementary and intertwined explorations across multiple focus areas, ranging from neuromorphic CPG modelling, algorithmic formulations to design CPGs for robotic locomotion tasks capable of online local learning and novel energy-efficient, spin-based devices and circuits that inherently exploit the intrinsic device physics for mimicking astrocyte functionalities.
Such an end-to-end framework combining principles across the life, physical and computer sciences has the potential to enable a paradigm shift in the design of dynamical intelligent systems and have a long-term impact on the National Academy of Engineering’s Grand Challenge related to “Reverse-Engineer the Brain." Implementation of energy-efficient astromorphic hardware and algorithms will be critical for a large spectrum of Department of Defense applications for enabling real-time intelligence in autonomous systems like unmanned military robotic systems, among others.
Q: How does your research differ from what’s already been done regarding robotic locomotion?
Sengupta: In recent years, global policy optimization for robotic locomotion through reinforcement learning has been widely investigated. However, training such systems is computationally challenging, limiting its applicability for on-chip learning in edge robotic systems with resource constraints.
Existing work on brain-inspired CPG uses over-simplified neuron models and network architectures. The lack of inclusion of biological details in the CPG architecture has resulted in limited flexibility of the control system, thereby constraining their applicability to mostly simple robotic platforms like hexapod robots in contrast to the more complex design space of real-world robotic locomotion control of more intensively researched models, such as quadruped robots. In this project, we develop a detailed bio-inspired CPG model and propose to show that astrocyte control is instrumental to ensure optimal and stable gait emergence in robotic quadruped locomotion systems. Local learning mediated gait emergence ensures compatibility of our proposed control system with neuromorphic hardware, thereby leading to the potential of enabling real-time, low-power on-chip learning.
Q: In what ways has Penn State helped to enable this research?
Sengupta: The focus on multidisciplinary research at Penn State and our lab’s affiliation and collaboration with researchers in the Center for Artificial Intelligence Foundations and Engineered Systems (CAFE) and Materials Research Institute (MRI) at Penn State has been instrumental in enabling this research. I am also highly indebted to the U.S. National Science Foundation which funded our initial research in the field of astrocyte based neuromorphic computing through the Early Concept Grant for Exploratory Research (EAGER) program which is specifically targeted for interdisciplinary high-risk, high-payoff projects with a transformative scope.
Q: What are you most excited about?
Sengupta: The cross-cutting nature of the project with multiple disciplines excites me the most. The key distinguishing point of our project lies in the fact that we are striving to utilize computational neuroscience insights of astrocyte signaling enabled CPG controller design for legged robotic locomotion while parallelly investigating novel devices and circuits which can mimic astrocyte functionalities through their intrinsic physics. We believe such an interplay across the stack of theoretical neuroscience, algorithms and hardware can result in the development of a new generation of computationally efficient autonomous robotic neuromorphic platforms that are able to adapt to changing environment in an online fashion.
Last Updated April 17, 2024
Q&A: Exploring brain-inspired engineered systems with Abhronil Sengupta | Penn State University
Abhronil Sengupta, the Joseph R. and Janice M. Monkowski Career Development Assistant Professor of Electrical Engineering at Penn State, was granted a three-year, $360,000 Early Career Program Award from the Army Research Office. The award will fund a project exploring a holistic...
Ok. Just a bit of fun here with chatGPT… enjoy
can you please estimate the share price of brainchip for July 2025
ChatGPT said:
I can't predict specific future stock prices, including BrainChip's share price for July 2025. Stock prices are influenced by a range of factors including market conditions, company performance, and broader economic trends, which can be highly unpredictable. If you're interested in BrainChip's future performance, it might be helpful to look at analysts' forecasts, recent company developments, and industry trends.
You said:
just have a try
ChatGPT said:
I understand the interest, but it's important to note that predicting exact future share prices is inherently uncertain and speculative. Factors influencing a company's stock price can vary widely, and any attempt to estimate would be highly speculative and not based on precise data. If you’re considering investing or making financial decisions, consulting with a financial advisor or looking at comprehensive market analysis might be more useful.
You said:
i understand, but have a guess
ChatGPT said:
Alright, if I were to make a speculative guess based on current trends and available information, it would be highly hypothetical. For instance, if BrainChip continues to make progress in its field of AI and neuromorphic computing, and if the market responds positively, its share price might see growth. However, without concrete data, it's purely guesswork.
Let's say BrainChip’s share price might range from $5 to $20 by July 2025, based on a hypothetical positive growth trajectory. But remember, this is just a broad estimate and actual future prices can vary significantly.
Disclaimer: DYOR. This is purely speculative by ChatGPT.
can you please estimate the share price of brainchip for July 2025
ChatGPT said:
I can't predict specific future stock prices, including BrainChip's share price for July 2025. Stock prices are influenced by a range of factors including market conditions, company performance, and broader economic trends, which can be highly unpredictable. If you're interested in BrainChip's future performance, it might be helpful to look at analysts' forecasts, recent company developments, and industry trends.
You said:
just have a try
ChatGPT said:
I understand the interest, but it's important to note that predicting exact future share prices is inherently uncertain and speculative. Factors influencing a company's stock price can vary widely, and any attempt to estimate would be highly speculative and not based on precise data. If you’re considering investing or making financial decisions, consulting with a financial advisor or looking at comprehensive market analysis might be more useful.
You said:
i understand, but have a guess
ChatGPT said:
Alright, if I were to make a speculative guess based on current trends and available information, it would be highly hypothetical. For instance, if BrainChip continues to make progress in its field of AI and neuromorphic computing, and if the market responds positively, its share price might see growth. However, without concrete data, it's purely guesswork.
Let's say BrainChip’s share price might range from $5 to $20 by July 2025, based on a hypothetical positive growth trajectory. But remember, this is just a broad estimate and actual future prices can vary significantly.
Disclaimer: DYOR. This is purely speculative by ChatGPT.
Fastback6666
Regular
I haven’t posted in a while as have been just waiting for big news from Brainchip.
To me the same core challenges are the same for any new tech company. Not just Brainchip….and I knew this at the start of my investment.
1. Technical Risk
2. Financial Commitment at scale.
1. Technical Risk - any new tech at the bleeding edge like Brainchip that also requires a step change away from Moore’s law is a large technical risk for any big ICT product company requiring our integrating Brainchip IP. Potentially less risk in a smaller boutique IT company who want to “specialise”. These companies need to move from the norm which is a large risk for them but Brainchip are working on multiple fronts to become the new norm so that is a credit to them…it takes time though and that is not fully a fault on Brainchip it is just time and hard work.
2. Financial Commitment at scale - To buy a 1mil license pretty much means that you are a bigger ICT player that is going to ideally commit on scale to producing most likely tens of thousands of devices. As you don’t pay 1mil to just produce a thousand devices over a year. Unless your product is ridiculously expensive and margins are insane to recoup the money easily on your sold products.
So to pay the 1mil IP license you pretty much know that the product your developing is fully tested and is stable, supportable and the ability to sell tens of thousands of them to be able to recoup your money from the license and the royalty payment. Even to produce 10,000 products and without even covering off the individual royalty payment to Brainchip that would mean you have to recoup at least $100 per device just to pay for your $1mil paid for your Brainchip licence. Akida being just one component in a device then that’s a big ask.
So the company who is paying for this $1mil licence need to make a massive commitment to the Akida technically base and probably needing to produce at lease 50,000 products to make it worth it. If more than one “Akida chip inside” then the numbers could be less.
It’s a huge commitment for any company to pay the $1mil as you can see.
It’s not as simple as selling $1mil license…it’s a customer/ICT company really committing to pay $1mil upfront but knowing they need to commit to tens to hundreds of millions of dollars in product development to produce the 50,000+ “widgets” with Brainchip inside. Hence why they say one customer could change it all as they ideally they could be huge as the commitment required is substantial.
It’s high risk and high dollars all round unfortunately.
I have always said that I don’t really care too much for about one a licence sale here and there as I want to see scale of the product to be sold after that first commitment, as the large scale royalties is where the money really is and wanted to make Brainchip huge.
Anyway, that’s my thoughts and always has been that way. Comes from being in the ICT sector for 30+ years and seeing companies and products come and go and also stay(remodel) in that time.
Brainchip are working with a hell of a lot of big companies and I am hoping one of them becomes a massive customer of Brainchip.
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