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Bravo
If ARM was an arm, BRN would be its biceps💪!
Softbank Aims to Work With Arm on AI Revolution, CFO Says
LI AILIN
DATE: 11 HOURS AGO
HIROFUMI TAKEUCHI, Nikkei staff writerSeptember 15, 2023 03:27 JST
NEW YORK -- U.K. chip designer Arm is SoftBank Group's most important unit and key to its artificial intelligence strategy, the Japanese investor's finance chief said Thursday after Arm's Nasdaq debut.
Speaking to reporters outside the Nasdaq market site here, Yoshimitsu Goto said Arm is "in the leading position of the artificial intelligence revolution that we have been strategically advancing."
Softbank Aims to Work With Arm on AI Revolution, CFO Says
(Yicai) Sept. 15 -- Arm Holdings, a chip designer owned by Softbank Group, soared in value after its listing on the Nasdaq stock market yesterday, and Softbank’s chief financial officer told Yicai that Arm is a key part of the Japanese firm’s artificial intelligence ambitions.
As the world’s largest public offering so far this year, Arm raised USD4.9 billion at an initial offer price of USD51 per share, with the share price surging 25 percent to USD63.59 on its first day of trading, giving it a market capitalization of USD65 billion.
Softbank CFO Yoshimitsu Goto attended Arm’s bell-ringing ceremony yesterday.
In an interview with Yicai outside the exchange, Goto said that he hopes Softbank stays at the forefront of advancing the AI revolution and that Arm can help achieve the goal.
The two companies will work together to explore various AI applications and businesses, Goto noted. AI has the power to redefine the world and every industry, and Softbank is committed to realizing this vision, he added.
Founded in 1990 and based in Cambridge in the United Kingdom, Arm went public in London and on Nasdaq in 1998, but Softbank acquired it in 2016 for USD32 billion and took the company private. Softbank tried unsuccessfully to sell Arm to US chip giant Nvidia in 2020 and then started to promote its re-listing.
Arm’s cornerstone investors include some of the world’s leading tech and chip firms such as Apple, Google’s parent company Alphabet, Nvidia, Intel, and Taiwan Semiconductor Manufacturing, who bought up to USD735 million worth of shares at the initial offer price, according to Arm’s listing prospectus.
Arm has seen mediocre financial performance recently, with net profit in the quarter ended June 30 falling 53 percent from a year earlier to USD105 million and revenue down 2.5 percent at USD675 million.
Softbank Aims to Work With Arm on AI Revolution, CFO Says
www.yicaiglobal.com
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Perhaps
Regular
Just to clear things up, maybe you mixed some informations from the past. There is a very slight connection to Cadence/Tensilica, but no partnership.
"MegaChips has been an authorized Tensilica design center since 2008 and has completed many designs using Tensilica processors."
Bravo
If ARM was an arm, BRN would be its biceps💪!
Hi Cartagena,
There was the job ad for an IP Verification Engineer in Hyderabad India stating "The role would include functional verification of the IP solution of Siemens/Synopsys/Cadence".
Cartagena
Regular
Correct, thanks Bravo. That's what I was referring to when I made the connection. Yes the job ad on LinkedIn. I knew I saw it somewhere!
Last edited:
"Is anything worn under the kilt?"I’m needing a cold shower this morning after reading all these promising stories posted over the last 48 hours.this is getting very EXCITING.!!! I better put something on under my Kilt.
"Everrr*ything's in perrrfect worrrking orrrrder."
* rrr = Scottish accent.
Credit: Spike Milligan
Last edited:
A breakthrough way to train neuromorphic chips
Using a biosensor to detect cystic fibrosis as the test case, TU/e researchers have devised an innovative way to train neuromorphic chips as presented in a new paper in Nature Electronics.
"Until now, a neuromorphic chip's neural network would be trained using external software, which is a process that can be time-consuming and energy inefficient. "Now, our new chip can learn on-the-fly by processing patient data in real-time, which certainly speeds up the training process and helps promote the use of the chip in real interactive bioapplications," says the researcher."
equanimous
Norse clairvoyant shapeshifter goddess
[/QUOTE]
Usually a bagpipe
He's already on mine, has been for a long time. I do love the factual and insightful responses to his and others that I have on ignore though. Their posts in my mind only encourage all the generously shared info, it has also help me strengthen my grip on my diamond hands. So I appreciate all those with the technical know-how who know what to look for in response to their dribble that don't have them on ignore, you are all very kind to share your valuable time and effort.
HopalongPetrovski
I'm Spartacus!
Generosity is the first perfection
Bravo
If ARM was an arm, BRN would be its biceps💪!
I wonder if they are stepping our patents? Maybe one for @Diogenese to take a look at?
The neuromorphic biosensing chip. Credit: Eveline van Doremaele
Using a biosensor to detect cystic fibrosis as the test case, TU/e researchers have devised an innovative way to train neuromorphic chips as presented in a new paper in Nature Electronics.
Neuromorphic computers—which are based on the structure of the human brain—could revolutionize our future health care devices. However, their widespread use is hindered by the need to train neuromorphic computers using external training software, which can be time-consuming and energy inefficient.
Researchers from Eindhoven University of Technology and Northwestern University in the U.S. have developed a new neuromorphic biosensor capable of on-chip learning that doesn't need external training. As a proof-of-concept, the researchers used the biosensor to diagnose cystic fibrosis based on sweat samples.
"We have demonstrated that we can create a 'smart biosensor' that could learn to detect a disease, such as cystic fibrosis, without using a computer or software." That's how Eveline van Doremaele summarized their new paper with Yoeri van de Burgt from TU/e, as well as Xudong Ji and Jonathan Rivnay from Northwestern University in the U.S.
The "smart biosensor" in their research is a neuromorphic biosensing computer—a device whose operation takes inspiration from the way that neurons communicate with other neurons in the human brain.
"Neuromorphic computing could have a significant impact on health care for example, particularly when it comes to point-of-care devices to check for an illness or condition," says van Doremaele. "And in our research, we have solved a major problem with regards to the use of neuromorphic computers in health care."
Until now, a neuromorphic chip's neural network would be trained using external software, which is a process that can be time-consuming and energy inefficient. "Now, our new chip can learn on-the-fly by processing patient data in real-time, which certainly speeds up the training process and helps promote the use of the chip in real interactive bioapplications," says the researcher.
One existing way to test for the disease is via a sweat test where a high level of chloride anions is an indicator of cystic fibrosis. Reliable sensors are already available to test for cystic fibrosis, so this test provided the researchers with an easy-to-check case study for their on-chip learning sensor.
"For ease of implementation, we didn't work with real patient data. Instead, we used sweat samples from healthy donors," says van Doremaele. "One sample was a negative sample or healthy sample of donor sweat, while a second sample was prepared to have a very high concentration of chloride anions."
The researchers' neuromorphic biosensor consists of three main parts—the sensor module, the hardware neural network, and the output classification part. A drop of sweat is added to the sensor module after which chloride and other ion concentrations in the sweat are detected with ion-selective electrodes. These signals are then processed by the neuromorphic chip itself. Finally, the result of the analysis is displayed as a green or red light indicating a negative or positive result, respectively.
"We created a number of sweat samples with varying and known ion concentrations and then tested the samples on the chip. If the result from the chip for a test was wrong, we corrected the chip, which resulted in corrections to the weights between the nodes of the neural network," says van Doremaele. "Importantly, we train the chip on the hardware itself."
This is the major advancement in this research—the ability to train the neural network on the chip and all without the need for any external software. "When the chip is trained to the problem of interest (here detection of cystic fibrosis from sweat samples), there is no further external control or intervention needed," adds van Doremaele.
This new on-chip learning approach opens up the possibility of personalized implantable neural networks that are trained by the end user through the use of data directly from the user. "Such an approach to training neural networks for health care could have significant implications for people, and may someday provide a way to train chips in real-time to control prosthetics or other similar devices. The real novelty is that the chips can learn and adapt to their application and environment. They do not have to be programmed beforehand, as is the case today."
Published: 14 September 2023
www.nature.com
A breakthrough way to train neuromorphic chips
by Eindhoven University of Technology
Using a biosensor to detect cystic fibrosis as the test case, TU/e researchers have devised an innovative way to train neuromorphic chips as presented in a new paper in Nature Electronics.
Neuromorphic computers—which are based on the structure of the human brain—could revolutionize our future health care devices. However, their widespread use is hindered by the need to train neuromorphic computers using external training software, which can be time-consuming and energy inefficient.
Researchers from Eindhoven University of Technology and Northwestern University in the U.S. have developed a new neuromorphic biosensor capable of on-chip learning that doesn't need external training. As a proof-of-concept, the researchers used the biosensor to diagnose cystic fibrosis based on sweat samples.
"We have demonstrated that we can create a 'smart biosensor' that could learn to detect a disease, such as cystic fibrosis, without using a computer or software." That's how Eveline van Doremaele summarized their new paper with Yoeri van de Burgt from TU/e, as well as Xudong Ji and Jonathan Rivnay from Northwestern University in the U.S.
The "smart biosensor" in their research is a neuromorphic biosensing computer—a device whose operation takes inspiration from the way that neurons communicate with other neurons in the human brain.
"Neuromorphic computing could have a significant impact on health care for example, particularly when it comes to point-of-care devices to check for an illness or condition," says van Doremaele. "And in our research, we have solved a major problem with regards to the use of neuromorphic computers in health care."
Goodbye to external software
So, what is the problem that van Doremaele and her collaborators solved? "For practical use in health care devices, neuromorphic technologies need to have low power requirements, interface with a sensor, and be easily trained for use. The first two of these can be solved with organic-based electronics. But it's the training part that's the central issue."Until now, a neuromorphic chip's neural network would be trained using external software, which is a process that can be time-consuming and energy inefficient. "Now, our new chip can learn on-the-fly by processing patient data in real-time, which certainly speeds up the training process and helps promote the use of the chip in real interactive bioapplications," says the researcher.
Searching for chloride anions
To test the effectiveness of their brand new chip, the researchers used it to test for the genetic disease cystic fibrosis. Cystic fibrosis is a hereditary disease that can damage organs, such as the lungs and digestive system.One existing way to test for the disease is via a sweat test where a high level of chloride anions is an indicator of cystic fibrosis. Reliable sensors are already available to test for cystic fibrosis, so this test provided the researchers with an easy-to-check case study for their on-chip learning sensor.
"For ease of implementation, we didn't work with real patient data. Instead, we used sweat samples from healthy donors," says van Doremaele. "One sample was a negative sample or healthy sample of donor sweat, while a second sample was prepared to have a very high concentration of chloride anions."
The researchers' neuromorphic biosensor consists of three main parts—the sensor module, the hardware neural network, and the output classification part. A drop of sweat is added to the sensor module after which chloride and other ion concentrations in the sweat are detected with ion-selective electrodes. These signals are then processed by the neuromorphic chip itself. Finally, the result of the analysis is displayed as a green or red light indicating a negative or positive result, respectively.
Training at the 'data gym'
Before the chip was used to evaluate the main sweat samples, the neural network had to go the 'data gym' and undergo some supervised training."We created a number of sweat samples with varying and known ion concentrations and then tested the samples on the chip. If the result from the chip for a test was wrong, we corrected the chip, which resulted in corrections to the weights between the nodes of the neural network," says van Doremaele. "Importantly, we train the chip on the hardware itself."
This is the major advancement in this research—the ability to train the neural network on the chip and all without the need for any external software. "When the chip is trained to the problem of interest (here detection of cystic fibrosis from sweat samples), there is no further external control or intervention needed," adds van Doremaele.
The ease of retraining
In addition, even when trained, the chip can be used for another problem. "Say you want to use the same neural network hardware in a smart prosthetic hand or arm. All you have to do is retrain the neural network at the 'data gym' with information on hand or arm movements in this case," says van Doremaele.This new on-chip learning approach opens up the possibility of personalized implantable neural networks that are trained by the end user through the use of data directly from the user. "Such an approach to training neural networks for health care could have significant implications for people, and may someday provide a way to train chips in real-time to control prosthetics or other similar devices. The real novelty is that the chips can learn and adapt to their application and environment. They do not have to be programmed beforehand, as is the case today."
Published: 14 September 2023
A retrainable neuromorphic biosensor for on-chip learning and classification
Abstract
Neuromorphic computing could be used to directly perform complex classification tasks in hardware and is of potential value in the development of wearable, implantable and point-of-care devices. Successful implementation requires low-power operation, simple sensor integration and straightforward training. Organic materials are possible building blocks for neuromorphic systems, offering low-voltage operation and excellent tunability. However, systems developed so far still rely on external training in software. Here we report a neuromorphic biosensing platform that is capable of on-chip learning and classification. The modular biosensor consists of a sensor input layer, an integrated array of organic neuromorphic devices that form the synaptic weights of a hardware neural network and an output classification layer. We use the system to classify the genetic disease cystic fibrosis from modified donor sweat using ion-selective sensors; on-chip training is done using error signal feedback to modulate the conductance of the organic neuromorphic devices. We also show that the neuromorphic biosensor can be retrained on the chip, by switching the sensor input signals and alternatively through the formation of logic gates.
A retrainable neuromorphic biosensor for on-chip learning and classification - Nature Electronics
A neuromorphic biosensor that consists of a sensor input layer, an array of organic neuromorphic devices (forming a hardware neural network) and an output classification layer can be trained on the chip to classify a model disease and then retrained on the chip by switching the sensor input signals.
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Bravo
If ARM was an arm, BRN would be its biceps💪!
Oh Wow! Check this out Brain Fam!
Neuromorphic processing power will unlock new capabilities in pattern recognition and autonomous tasking.
Anastasia Obis
Fri, 09/15/2023 - 14:47
Photo credit: metamorworks / Shutterstock
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Satellite operators are looking at new brain-like processing power that promises to delivery greater intelligence capabilities and significantly reduce energy consumption, according to a leader at the U.S. Navy Research Laboratory's Naval Center for Space Technology.
The center is tasked with improving the Navy's autonomous systems in space and is investigating applications in this technology — what's called neuromorphic processors. The technology would offer major opportunities for AI and allow the Defense Department to advance pattern recognition, autonomous tasking, and reduction in energy and latency, all of which are critical for robotic applications where there is a need for a robot to immediately respond to constant, real-world change.
"We [the satellite community] are interested in using AI to control robots and other autonomous systems," Steven Meier, the center's director, told GovCIO Media and Research on GovCast. "This is a challenge, though, because AI and [machine learning] types of systems tend to be very brittle. They break, often catastrophically, when encountering situations it wasn't designed for."
Meier explained the necessity to introduce more modernization to these technologies as they become more widely used.
"As AI and ML is used today, we have to download all the data, analyze it on the ground using substantial computing resources. And it isn't really feasible to fly computers that big on most spacecraft. AI/ML systems take up too much space, require too much power, and generate far too much heat. So someday soon, we hope to fly computers and AI/ML algorithms that can recognize objects or phenomena of interest on board and just download the pertinent data directly to scientists and warfighters," Meier added.
The lab has been leading an effort to develop neuromorphic hardware elements based on a new building element for electrical circuits called "memristors," also known as memory resistors. If successful, memristors would revolutionize electronics and end the era of transistors made of silicon.
"In terms of what we're pushing forward at the state of the art at NCST to help facilitate this for satellite systems, or others, are some developing novel types of computers that … work more like a brain than a regular computer, like your laptop," Meier said. "We have ... several of these processors that are very unique, and they, we feel like, are the future of AI/ML type systems."
The Air Force Research Laboratory is also working on Neuromorphic Intelligent Computing Systems, a development program using neuromorphic technologies to advance capabilities like artificial intelligence and machine learning for edge computing.
While significant strides have been made around neuromorphic computing in the past several decades, there are a number of challenges that the satellite community would need to overcome before adopting and mass-producing the technology, including the general availability of neuromorphic hardware and developing algorithms that are capable of mimicking the human brain.
https://www.governmentciomedia.com/navy-future-ai-brain-computing
Remember this!
Navy: The Future of AI is 'Brain-Like' Computing
Neuromorphic processing power will unlock new capabilities in pattern recognition and autonomous tasking.
Anastasia Obis
Fri, 09/15/2023 - 14:47
Photo credit: metamorworks / Shutterstock
ShareFacebookTwitterLinkedIn
Satellite operators are looking at new brain-like processing power that promises to delivery greater intelligence capabilities and significantly reduce energy consumption, according to a leader at the U.S. Navy Research Laboratory's Naval Center for Space Technology.
The center is tasked with improving the Navy's autonomous systems in space and is investigating applications in this technology — what's called neuromorphic processors. The technology would offer major opportunities for AI and allow the Defense Department to advance pattern recognition, autonomous tasking, and reduction in energy and latency, all of which are critical for robotic applications where there is a need for a robot to immediately respond to constant, real-world change.
"We [the satellite community] are interested in using AI to control robots and other autonomous systems," Steven Meier, the center's director, told GovCIO Media and Research on GovCast. "This is a challenge, though, because AI and [machine learning] types of systems tend to be very brittle. They break, often catastrophically, when encountering situations it wasn't designed for."
Meier explained the necessity to introduce more modernization to these technologies as they become more widely used.
"As AI and ML is used today, we have to download all the data, analyze it on the ground using substantial computing resources. And it isn't really feasible to fly computers that big on most spacecraft. AI/ML systems take up too much space, require too much power, and generate far too much heat. So someday soon, we hope to fly computers and AI/ML algorithms that can recognize objects or phenomena of interest on board and just download the pertinent data directly to scientists and warfighters," Meier added.
The lab has been leading an effort to develop neuromorphic hardware elements based on a new building element for electrical circuits called "memristors," also known as memory resistors. If successful, memristors would revolutionize electronics and end the era of transistors made of silicon.
"In terms of what we're pushing forward at the state of the art at NCST to help facilitate this for satellite systems, or others, are some developing novel types of computers that … work more like a brain than a regular computer, like your laptop," Meier said. "We have ... several of these processors that are very unique, and they, we feel like, are the future of AI/ML type systems."
The Air Force Research Laboratory is also working on Neuromorphic Intelligent Computing Systems, a development program using neuromorphic technologies to advance capabilities like artificial intelligence and machine learning for edge computing.
While significant strides have been made around neuromorphic computing in the past several decades, there are a number of challenges that the satellite community would need to overcome before adopting and mass-producing the technology, including the general availability of neuromorphic hardware and developing algorithms that are capable of mimicking the human brain.
https://www.governmentciomedia.com/navy-future-ai-brain-computing
Remember this!
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Last edited:
Bravo
If ARM was an arm, BRN would be its biceps💪!
Cor blimey!!!!!!
At 30.27 mins into this discussion Steven Meier of the Naval Research Laboratory’s Naval Center for Space Technology says something like this:
"So one type of novel computer we're investigating, it's called neuromorphic processors that you put on your computer. These type of processors implement artificial neurons in this way that turn out to be 100 times more energy efficient than the same ML system being run on a standard processor. So we have several of these processors that are very unique and that we feel are the future of AI and ML type systems."
30.27 mins
www.governmentciomedia.com
At 30.27 mins into this discussion Steven Meier of the Naval Research Laboratory’s Naval Center for Space Technology says something like this:
"So one type of novel computer we're investigating, it's called neuromorphic processors that you put on your computer. These type of processors implement artificial neurons in this way that turn out to be 100 times more energy efficient than the same ML system being run on a standard processor. So we have several of these processors that are very unique and that we feel are the future of AI and ML type systems."
30.27 mins
Listen: Navy Eyes AI, Hypersonics in Future Space Technology and Research
The Naval Center for Space Technology’s director highlights the programs and developments that are boosting Navy priorities.
Last edited:
GStocks123
Regular
Webpage update looking very professional!
brainchip.com
Akida Foundations
Immerse yourself in the powerful new world of the akida™ neuromorphic processor IP! The future of AI is NOW!
brainchip.com
Hi Bravo,I wonder if they are stepping our patents? Maybe one for @Diogenese to take a look at?
A breakthrough way to train neuromorphic chips
by Eindhoven University of Technology
The neuromorphic biosensing chip. Credit: Eveline van Doremaele
Using a biosensor to detect cystic fibrosis as the test case, TU/e researchers have devised an innovative way to train neuromorphic chips as presented in a new paper in Nature Electronics.
Neuromorphic computers—which are based on the structure of the human brain—could revolutionize our future health care devices. However, their widespread use is hindered by the need to train neuromorphic computers using external training software, which can be time-consuming and energy inefficient.
Researchers from Eindhoven University of Technology and Northwestern University in the U.S. have developed a new neuromorphic biosensor capable of on-chip learning that doesn't need external training. As a proof-of-concept, the researchers used the biosensor to diagnose cystic fibrosis based on sweat samples.
"We have demonstrated that we can create a 'smart biosensor' that could learn to detect a disease, such as cystic fibrosis, without using a computer or software." That's how Eveline van Doremaele summarized their new paper with Yoeri van de Burgt from TU/e, as well as Xudong Ji and Jonathan Rivnay from Northwestern University in the U.S.
The "smart biosensor" in their research is a neuromorphic biosensing computer—a device whose operation takes inspiration from the way that neurons communicate with other neurons in the human brain.
"Neuromorphic computing could have a significant impact on health care for example, particularly when it comes to point-of-care devices to check for an illness or condition," says van Doremaele. "And in our research, we have solved a major problem with regards to the use of neuromorphic computers in health care."
Goodbye to external software
So, what is the problem that van Doremaele and her collaborators solved? "For practical use in health care devices, neuromorphic technologies need to have low power requirements, interface with a sensor, and be easily trained for use. The first two of these can be solved with organic-based electronics. But it's the training part that's the central issue."
Until now, a neuromorphic chip's neural network would be trained using external software, which is a process that can be time-consuming and energy inefficient. "Now, our new chip can learn on-the-fly by processing patient data in real-time, which certainly speeds up the training process and helps promote the use of the chip in real interactive bioapplications," says the researcher.
Searching for chloride anions
To test the effectiveness of their brand new chip, the researchers used it to test for the genetic disease cystic fibrosis. Cystic fibrosis is a hereditary disease that can damage organs, such as the lungs and digestive system.
One existing way to test for the disease is via a sweat test where a high level of chloride anions is an indicator of cystic fibrosis. Reliable sensors are already available to test for cystic fibrosis, so this test provided the researchers with an easy-to-check case study for their on-chip learning sensor.
"For ease of implementation, we didn't work with real patient data. Instead, we used sweat samples from healthy donors," says van Doremaele. "One sample was a negative sample or healthy sample of donor sweat, while a second sample was prepared to have a very high concentration of chloride anions."
The researchers' neuromorphic biosensor consists of three main parts—the sensor module, the hardware neural network, and the output classification part. A drop of sweat is added to the sensor module after which chloride and other ion concentrations in the sweat are detected with ion-selective electrodes. These signals are then processed by the neuromorphic chip itself. Finally, the result of the analysis is displayed as a green or red light indicating a negative or positive result, respectively.
Training at the 'data gym'
Before the chip was used to evaluate the main sweat samples, the neural network had to go the 'data gym' and undergo some supervised training.
"We created a number of sweat samples with varying and known ion concentrations and then tested the samples on the chip. If the result from the chip for a test was wrong, we corrected the chip, which resulted in corrections to the weights between the nodes of the neural network," says van Doremaele. "Importantly, we train the chip on the hardware itself."
This is the major advancement in this research—the ability to train the neural network on the chip and all without the need for any external software. "When the chip is trained to the problem of interest (here detection of cystic fibrosis from sweat samples), there is no further external control or intervention needed," adds van Doremaele.
The ease of retraining
In addition, even when trained, the chip can be used for another problem. "Say you want to use the same neural network hardware in a smart prosthetic hand or arm. All you have to do is retrain the neural network at the 'data gym' with information on hand or arm movements in this case," says van Doremaele.
This new on-chip learning approach opens up the possibility of personalized implantable neural networks that are trained by the end user through the use of data directly from the user. "Such an approach to training neural networks for health care could have significant implications for people, and may someday provide a way to train chips in real-time to control prosthetics or other similar devices. The real novelty is that the chips can learn and adapt to their application and environment. They do not have to be programmed beforehand, as is the case today."
Published: 14 September 2023
A retrainable neuromorphic biosensor for on-chip learning and classification
Abstract
Neuromorphic computing could be used to directly perform complex classification tasks in hardware and is of potential value in the development of wearable, implantable and point-of-care devices. Successful implementation requires low-power operation, simple sensor integration and straightforward training. Organic materials are possible building blocks for neuromorphic systems, offering low-voltage operation and excellent tunability. However, systems developed so far still rely on external training in software. Here we report a neuromorphic biosensing platform that is capable of on-chip learning and classification. The modular biosensor consists of a sensor input layer, an integrated array of organic neuromorphic devices that form the synaptic weights of a hardware neural network and an output classification layer. We use the system to classify the genetic disease cystic fibrosis from modified donor sweat using ion-selective sensors; on-chip training is done using error signal feedback to modulate the conductance of the organic neuromorphic devices. We also show that the neuromorphic biosensor can be retrained on the chip, by switching the sensor input signals and alternatively through the formation of logic gates.
A retrainable neuromorphic biosensor for on-chip learning and classification - Nature Electronics
A neuromorphic biosensor that consists of a sensor input layer, an array of organic neuromorphic devices (forming a hardware neural network) and an output classification layer can be trained on the chip to classify a model disease and then retrained on the chip by switching the sensor input signals.
No relevant patents for Eindhoven Uni, but there is the 18 month patent office NDA period.
wilzy123
Founding Member
Funny how you come out of the woodwork now, and of all the things you choose to post is this senselessly long diatribe.
Also - what 'direct messages' are you referring to that are childish? Now you are just making stuff up... and what do you have to gain from it? More misleading posts from you... am not surprised you have appeared.
Bravo
If ARM was an arm, BRN would be its biceps💪!
Cor blimey!!!!!!
View attachment 44727
At 30.27 mins into this discussion Steven Meier of the Naval Research Laboratory’s Naval Center for Space Technology says something like this:
"So one type of novel computer we're investigating, it's called neuromorphic processors that you put on your computer. These type of processors implement artificial neurons in this way that turn out to be 100 times more energy efficient than the same ML system being run on a standard processor. So we have several of these processors that are very unique and that we feel are the future of AI and ML type systems."
30.27 mins
Listen: Navy Eyes AI, Hypersonics in Future Space Technology and Research
The Naval Center for Space Technology’s director highlights the programs and developments that are boosting Navy priorities.
Directly after talking about neuromorphic computing improving AI and ML, Steven Meier (AFRL) goes on to mention RSGS operation space craft and the NTS-3 program called "AUTO SAT", some sort of lab demonstration, so it's quite possible we are involved in both of these projects IMO. When he talks about AUTO SAT he says it operates autonomously, learns as it goes and gets smarter and smarter as it moves around, which might be a little hint.
BTW, he also mentions something about working with the Space Development Agency on satellite launches in tandem with SpaceX, Lockheed Martin L3Harris and York Space.
If he thinks neuromorphic computing is the future of AI and ML then, I can't see any reason why all of these companies wouldn't know about us through him at the very least.
@Bravo I love your research. Great stuff!
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