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Gerrit Ecke on LinkedIn: #neuromorphic #adas #ad
Mercedes-Benz invests in #Neuromorphic Computing! The technology is key to overcome the issue of increasing compute requirements on a tight energy budget of…www.linkedin.com
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Neuromorphic computing has considerable potential for us across many areas.
By mimicking the functionality of the human brain, it can make next-generation AI computation considerably faster and more energy efficient.
One current research project is NAOMI4Radar funded by the German Federal Ministry for Economic Affairs and Climate Action. As consortium leader, we are working with partners to assess how neuromorphic computing can be used to optimise the processing chain for radar data in automated driving systems.
Current Mercedes-Benz models use frontal radar to see 200 metres in front of them. For instance, our DRIVE PILOT system uses radar data as one of its many sources for enabling conditionally automated driving.
The aim of the NAOMI4Radar project is to demonstrate that neuromorphic computing can bring fundamental benefits to future generations of automated and autonomous driving systems.
But as I said, this is just one current research. More on that soon.
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Loihi 2 - smoke screen or smoked
Over 12 months ago Mercedes served this up fingers crossed our time has arrived
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My new “In the Loop” series kicks off with #Neuromorphic Computing – the clear winner of my poll a few weeks ago.
For those unfamiliar, this highly significant field of computing strives to emulate the multi-tasking of the human brain. Traditional microprocessors function sequentially. However, as the complexity and scale of calculations sores, this way of doing things is rapidly running out of road.
The idea is not new, but trying to “put a brain on a chip” is a mammoth task. To put it into figures: the human brain has 86-100 billion neurons operating on around 20 watts. Current neural chips from leading developers such as BrainChip and Intel Corporation contain around 1 million neurons and consume roughly 1 watt of power.
So, you see, despite impressive advances, there is still a very long way to go. Neuromorphic computing goes well beyond chip design and includes a specific kind of artificial neural network called #spikingneuralnetworks (SNN). They consume far less energy because the neurons are silent most of the time, only firing (or spiking) when needed for events.
Together with intense parallel execution on neuromorphic chips, the new processing principles require us to go beyond the application of existing #AI frameworks to neuromorphic chips. We have to fundamentally rethink the algorithms that ultimately enable future AI functions in our cars, gathering joint inspiration from machine learning, chip design and neuroscience. Our experts are working closely with our partners to examine their potential in new applications.
The thing is, even a tiny fraction of the thinking capacity of the human brain can go a long way in several fields that are extremely relevant to automotive applications. Examples include advanced driving assistance systems #ADAS as well as the on-board analysis of speech and video data, which can unlock major advances in how we communicate with our cars.
We already made some interesting findings here with our VISION EQXX, where we applied neuromorphic principles to the “Hey Mercedes” hot-word detection. That alone made it five to ten times more energy efficient than conventional voice control. As AI and machine learning take on an increasingly important role in the software-defined vehicle, the energy this consumes is likely to become a critical factor.
I’ll touch on our latest findings in an upcoming “In the Loop” and tell you my thoughts on where this is taking us.
In the meantime, for those of you interested in reading up on neuromorphic computing, check out the slider for my recommended sources. I’ve graded them to ensure there’s something for everyone, from absolute beginner to true geeks.
Correct ! As I stated in my post
Lohi ?View attachment 71033
Neuromorphic computing has considerable potential for us across many areas.
By mimicking the functionality of the human brain, it can make next-generation AI computation considerably faster and more energy efficient.
One current research project is NAOMI4Radar funded by the German Federal Ministry for Economic Affairs and Climate Action. As consortium leader, we are working with partners to assess how neuromorphic computing can be used to optimise the processing chain for radar data in automated driving systems.
Current Mercedes-Benz models use frontal radar to see 200 metres in front of them. For instance, our DRIVE PILOT system uses radar data as one of its many sources for enabling conditionally automated driving.
The aim of the NAOMI4Radar project is to demonstrate that neuromorphic computing can bring fundamental benefits to future generations of automated and autonomous driving systems.
But as I said, this is just one current research. More on that soon.
View attachment 71034
Loihi 2 - smoke screen or smoked
Over 12 months ago Mercedes served this up fingers crossed our time has arrived
View attachment 71035
My new “In the Loop” series kicks off with #Neuromorphic Computing – the clear winner of my poll a few weeks ago.
For those unfamiliar, this highly significant field of computing strives to emulate the multi-tasking of the human brain. Traditional microprocessors function sequentially. However, as the complexity and scale of calculations sores, this way of doing things is rapidly running out of road.
The idea is not new, but trying to “put a brain on a chip” is a mammoth task. To put it into figures: the human brain has 86-100 billion neurons operating on around 20 watts. Current neural chips from leading developers such as BrainChip and Intel Corporation contain around 1 million neurons and consume roughly 1 watt of power.
So, you see, despite impressive advances, there is still a very long way to go. Neuromorphic computing goes well beyond chip design and includes a specific kind of artificial neural network called #spikingneuralnetworks (SNN). They consume far less energy because the neurons are silent most of the time, only firing (or spiking) when needed for events.
Together with intense parallel execution on neuromorphic chips, the new processing principles require us to go beyond the application of existing #AI frameworks to neuromorphic chips. We have to fundamentally rethink the algorithms that ultimately enable future AI functions in our cars, gathering joint inspiration from machine learning, chip design and neuroscience. Our experts are working closely with our partners to examine their potential in new applications.
The thing is, even a tiny fraction of the thinking capacity of the human brain can go a long way in several fields that are extremely relevant to automotive applications. Examples include advanced driving assistance systems #ADAS as well as the on-board analysis of speech and video data, which can unlock major advances in how we communicate with our cars.
We already made some interesting findings here with our VISION EQXX, where we applied neuromorphic principles to the “Hey Mercedes” hot-word detection. That alone made it five to ten times more energy efficient than conventional voice control. As AI and machine learning take on an increasingly important role in the software-defined vehicle, the energy this consumes is likely to become a critical factor.
I’ll touch on our latest findings in an upcoming “In the Loop” and tell you my thoughts on where this is taking us.
In the meantime, for those of you interested in reading up on neuromorphic computing, check out the slider for my recommended sources. I’ve graded them to ensure there’s something for everyone, from absolute beginner to true geeks.
hmmmThat's a research chip that Loihi2 from Intel.
View attachment 71033
Neuromorphic computing has considerable potential for us across many areas.
By mimicking the functionality of the human brain, it can make next-generation AI computation considerably faster and more energy efficient.
One current research project is NAOMI4Radar funded by the German Federal Ministry for Economic Affairs and Climate Action. As consortium leader, we are working with partners to assess how neuromorphic computing can be used to optimise the processing chain for radar data in automated driving systems.
Current Mercedes-Benz models use frontal radar to see 200 metres in front of them. For instance, our DRIVE PILOT system uses radar data as one of its many sources for enabling conditionally automated driving.
The aim of the NAOMI4Radar project is to demonstrate that neuromorphic computing can bring fundamental benefits to future generations of automated and autonomous driving systems.
But as I said, this is just one current research. More on that soon.
View attachment 71034
Loihi 2 - smoke screen or smoked
Over 12 months ago Mercedes served this up fingers crossed our time has arrived
View attachment 71035
My new “In the Loop” series kicks off with #Neuromorphic Computing – the clear winner of my poll a few weeks ago.
For those unfamiliar, this highly significant field of computing strives to emulate the multi-tasking of the human brain. Traditional microprocessors function sequentially. However, as the complexity and scale of calculations sores, this way of doing things is rapidly running out of road.
The idea is not new, but trying to “put a brain on a chip” is a mammoth task. To put it into figures: the human brain has 86-100 billion neurons operating on around 20 watts. Current neural chips from leading developers such as BrainChip and Intel Corporation contain around 1 million neurons and consume roughly 1 watt of power.
So, you see, despite impressive advances, there is still a very long way to go. Neuromorphic computing goes well beyond chip design and includes a specific kind of artificial neural network called #spikingneuralnetworks (SNN). They consume far less energy because the neurons are silent most of the time, only firing (or spiking) when needed for events.
Together with intense parallel execution on neuromorphic chips, the new processing principles require us to go beyond the application of existing #AI frameworks to neuromorphic chips. We have to fundamentally rethink the algorithms that ultimately enable future AI functions in our cars, gathering joint inspiration from machine learning, chip design and neuroscience. Our experts are working closely with our partners to examine their potential in new applications.
The thing is, even a tiny fraction of the thinking capacity of the human brain can go a long way in several fields that are extremely relevant to automotive applications. Examples include advanced driving assistance systems #ADAS as well as the on-board analysis of speech and video data, which can unlock major advances in how we communicate with our cars.
We already made some interesting findings here with our VISION EQXX, where we applied neuromorphic principles to the “Hey Mercedes” hot-word detection. That alone made it five to ten times more energy efficient than conventional voice control. As AI and machine learning take on an increasingly important role in the software-defined vehicle, the energy this consumes is likely to become a critical factor.
I’ll touch on our latest findings in an upcoming “In the Loop” and tell you my thoughts on where this is taking us.
In the meantime, for those of you interested in reading up on neuromorphic computing, check out the slider for my recommended sources. I’ve graded them to ensure there’s something for everyone, from absolute beginner to true geeks.
Looks like they went with Loihi on this Markus just postedView attachment 70974 View attachment 70975 View attachment 70976Gerrit Ecke on LinkedIn: #neuromorphic #adas #ad
Mercedes-Benz invests in #Neuromorphic Computing! The technology is key to overcome the issue of increasing compute requirements on a tight energy budget of…
Great find TTM,View attachment 71033
Neuromorphic computing has considerable potential for us across many areas.
By mimicking the functionality of the human brain, it can make next-generation AI computation considerably faster and more energy efficient.
One current research project is NAOMI4Radar funded by the German Federal Ministry for Economic Affairs and Climate Action. As consortium leader, we are working with partners to assess how neuromorphic computing can be used to optimise the processing chain for radar data in automated driving systems.
Current Mercedes-Benz models use frontal radar to see 200 metres in front of them. For instance, our DRIVE PILOT system uses radar data as one of its many sources for enabling conditionally automated driving.
The aim of the NAOMI4Radar project is to demonstrate that neuromorphic computing can bring fundamental benefits to future generations of automated and autonomous driving systems.
But as I said, this is just one current research. More on that soon.
View attachment 71034
Loihi 2 - smoke screen or smoked
Over 12 months ago Mercedes served this up fingers crossed our time has arrived
View attachment 71035
My new “In the Loop” series kicks off with #Neuromorphic Computing – the clear winner of my poll a few weeks ago.
For those unfamiliar, this highly significant field of computing strives to emulate the multi-tasking of the human brain. Traditional microprocessors function sequentially. However, as the complexity and scale of calculations sores, this way of doing things is rapidly running out of road.
The idea is not new, but trying to “put a brain on a chip” is a mammoth task. To put it into figures: the human brain has 86-100 billion neurons operating on around 20 watts. Current neural chips from leading developers such as BrainChip and Intel Corporation contain around 1 million neurons and consume roughly 1 watt of power.
So, you see, despite impressive advances, there is still a very long way to go. Neuromorphic computing goes well beyond chip design and includes a specific kind of artificial neural network called #spikingneuralnetworks (SNN). They consume far less energy because the neurons are silent most of the time, only firing (or spiking) when needed for events.
Together with intense parallel execution on neuromorphic chips, the new processing principles require us to go beyond the application of existing #AI frameworks to neuromorphic chips. We have to fundamentally rethink the algorithms that ultimately enable future AI functions in our cars, gathering joint inspiration from machine learning, chip design and neuroscience. Our experts are working closely with our partners to examine their potential in new applications.
The thing is, even a tiny fraction of the thinking capacity of the human brain can go a long way in several fields that are extremely relevant to automotive applications. Examples include advanced driving assistance systems #ADAS as well as the on-board analysis of speech and video data, which can unlock major advances in how we communicate with our cars.
We already made some interesting findings here with our VISION EQXX, where we applied neuromorphic principles to the “Hey Mercedes” hot-word detection. That alone made it five to ten times more energy efficient than conventional voice control. As AI and machine learning take on an increasingly important role in the software-defined vehicle, the energy this consumes is likely to become a critical factor.
I’ll touch on our latest findings in an upcoming “In the Loop” and tell you my thoughts on where this is taking us.
In the meantime, for those of you interested in reading up on neuromorphic computing, check out the slider for my recommended sources. I’ve graded them to ensure there’s something for everyone, from absolute beginner to true geeks.
A video going along with that paper was uploaded to YouTube yesterday:
Both paper and video relate to another paper and video published by the same Uni Tübingen authors earlier this year. At a cursory glance, at least the videos (posted about six months apart) appear to be VERY similar:
https://thestockexchange.com.au/threads/brn-discussion-ongoing.1/post-416900
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Now compare the slides to those in the video uploaded October 3:
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In fact, when I just tried to cursorily compare the new paper to the March 15 paper that @Fullmoonfever had linked at the time (https://thestockexchange.com.au/threads/brn-discussion-ongoing.1/post-416313), I discovered that the link he had posted then now connects directly to this new paper, published on September 16, so it seems to be an updated version of the previous paper.
I did notice the addition of another co-author, though: Sebastian Otte, who used to be a PhD student and postdoc at Uni Tübingen (2013-2023) and became Professor at Uni Lübeck’s Institute for Robotics and Cognitive Systems just over a year ago, where he heads the Adaptive AI research group.
To put the results that our competitors’ neuromorphic offerings fared worse in the benchmarking tests alongside Akida somewhat into perspective:
In all fairness, it should be highlighted that Akida’s superiority was at least partly due to the fact that AKD1000 is available as a PCIe Board, whereas SynSense’s DynapCNN was connected to the PC via USB and - as the excerpt Gazzafish already posted shows - the researchers did not have direct access to a Loihi 2 edge device, but merely through a virtual machine provided by Intel via their Neuromorphic Research Cloud. The benchmarking would obviously yield better comparable results if the actual hardware used were of a similar form factor:
“Our results show that the better a neuromorphic edge device is connected to the main compute unit, e.g., as a PCIe card, the better the overall run-time.”
Anyway, Akida undoubtedly impressed the researchers, and as a result they are considering further experiments: “(…) future work could involve evaluating the system with an additional Akida PCIe card.”
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In an earlier post (https://thestockexchange.com.au/threads/brn-discussion-ongoing.1/post-426404), I had already mentioned that the paper’s first author, Andreas Ziegler, who is doing a PhD in robotics and computer vision at Uni Tübingen, has meanwhile completed his internship at Sony AI in Switzerland (that - as we know - partially funded the paper’s research):
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Fun fact: One of his co-authors, Karl Vetter, however, is no longer with Uni Tübingen’s Cognitive Systems Lab, but has since moved to France, where he has been working as a research engineer for…
It’s a small world, isn’t it?!
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Hi Bravo yes very interesting & I see the post is causing much interest, from my point of view there’s a comment amongst it that may of been missed by some
This was uploaded on the website of Uni Lübeck, where MB is collaborating with Sebastian Otte (who leads the Adaptive AI research group at the Institute for Robotics and Cognitive Systems) and his doctoral student Saya Higuchi on the NAOMI4Radar project:
Autonomes Fahren: Intelligente Sensoren durch innovative neuronale Netzwerke: Universität zu Lübeck
Universität zu Lübeck. Im Focus das Lebenwww.uni-luebeck.de
Autonomes Fahren: Intelligente Sensoren durch innovative neuronale Netzwerke
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Dynamische Darstellung innovativer Radarsensoren und neuronaler Netze für autonomes Fahren (Bild: Anja Stähle, generiert mit Adobe Firefly)
Prof. Sebastian Otte ist am Projekt NAOMI4Radar beteiligt und entwickelt mit Projektpartnern aus Industrie und Hochschulen energieeffiziente Radarsensoren.
Autonome Fahrzeuge benötigen präzise Sensoren für eine schnelle und zuverlässige Umgebungserfassung. Im Projekt NAOMI4Radar arbeitet ein Forschungsteam der Universität zu Lübeck unter der Leitung von Prof. Sebastian Otte gemeinsam mit der Mercedes-Benz AG, TWT GmbH Science & Innovation, Intel Deutschland GmbH und der Technischen Universität München an einer energieeffizienten Radarsensorik. Durch den Einsatz von Neuromorphic Computing und Spiking Neural Networks (SNNs) soll die Batterielaufzeit optimiert, die Reaktionszeit verkürzt und die Sicherheit erhöht werden. Das Projekt wird vom Bundesministerium für Wirtschaft und Klimaschutz (BMWK) gefördert und durch den Projektträger TÜV Rheinland begleitet.
Autonome Fahrzeuge benötigen präzise arbeitenden Sensoren, um schnell und zuverlässig auf ihre Umgebung reagieren zu können. Aktuelle Forschung zielt darauf ab, die Energieeffizienz der Sensordatenverarbeitung zu verbessern, um beispielsweise die Batterielaufzeit zu maximieren und die CO₂-Emissionen zu verringern. Prof. Sebastian Otte vom Institut für Robotik und kognitive Systeme entwickelt mit seinem Team innovative Lösungen in diesem Bereich. Im Projekt NAOMI4Radar arbeitet das Team der Universität zu Lübeck gemeinsam mit der Mercedes-Benz AG, der TWT GmbH Science & Innovation sowie den assoziierten Partnern Intel Deutschland GmbH und der Technischen Universität München an der Optimierung der Radarsensorik für autonome Fahrzeuge durch den Einsatz von Neuromorphic Computing. Diese innovative Technologie orientiert sich an der Arbeitsweise des menschlichen Gehirns und ermöglicht eine energieeffiziente und schnelle Verarbeitung von Sensordaten. Das Lübecker Forschungsteam erhält dafür eine Fördersumme von rund 166.000 Euro.
Neuronale Netze für Radardatenverarbeitung
Die 2024 mit dem Nobelpreis für Physik ausgezeichneten Entwicklungen im Bereich künstlicher neuronaler Netze bilden auch im Projekt NAOMI4Radar eine Schlüsseltechnologie in der Radardatenverarbeitung. Ziel des Projekts ist es, die Radardatenverarbeitung durch Spiking Neural Networks (SNNs), einer speziellen Form neuronaler Netzwerke, effizienter zu gestalten. Im Vergleich zu herkömmlichen KI-Algorithmen bieten SNNs vereinfacht ausgedrückt den Vorteil, dass einzelne Neuronen nur dann aktiv werden, wenn sie tatsächlich gebraucht werden. Durch Einsatz in neuromorphen Prozessoren, wie es im Loihi 2 von Intel vorgesehen ist, kann dieses Potenzial ausgeschöpft werden. Das senkt nicht nur den Energieverbrauch, sondern ermöglicht prinzipiell auch eine schnellere Reaktionszeit der autonomen Fahrzeuge, und erhöht somit die Sicherheit im Straßenverkehr.
Energiesparende Neuronenmodelle
Prof. Otte und sein Team konzentrieren sich dabei insbesondere auf die Weiterentwicklung des Balanced Resonate-and-Fire (BRF) Modells, dessen spezielle Eigenschaften es besonders für die effiziente Verarbeitung von Radardaten interessant macht. Die Effizienz soll durch Verwendung von biologisch inspirierten Sparse Coding Ansätzen noch weiter gesteigert werden. Sparse Coding hat das Ziel, die Robustheit von neuronalen Netzen zu verbessern, um sie beispielsweise fehlertoleranter zu machen. Gleichzeitig wird die Aktivität, also die Menge der Spikes, die im Netzwerk zirkulieren, auf ein Minimum zu reduziert. In Zusammenarbeit mit den Projektpartnern soll eine vollständige Integration von neuromorpher Radardatenverarbeitung realisiert und in einem Prototypenfahrzeug getestet werden.
Das Projekt, das bis August 2025 läuft, wird vom Bundesministerium für Wirtschaft und Klimaschutz (BMWK) gefördert. Die Universität Lübeck bringt ihre Expertise im Bereich Künstlicher Intelligenz und neuromorphe Algorithmen in dieses praxisorientierte Forschungsprojekt ein und leistet damit einen Beitrag für die Entwicklung und Erprobung nachhaltiger KI-Lösungen im industriellen Kontext.
Originalpublikation zum Lübecker BRF-Modell
Higuchi et al. Balanced Resonate-and-Fire Neurons. Proceedings of the 41st International Conference on Machine Learning, Vienna, Austria, 2024.
Kontakt:
Prof. Sebastian Otte
Adaptive AI Forschungsgruppe
Institut für Robotik und Kognitive Systeme
Universität zu Lübeck
Email: sebastian.otte(at)uni-luebeck(dot)de
Autonomous driving: Intelligent sensors through innovative neural networks
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Prof. Sebastian Otte is involved in the NAOMI4Radar project and is developing energy-efficient radar sensors with project partners from industry and universities.
Autonomous vehicles require precise sensors for fast and reliable detection of their surroundings. In the NAOMI4Radar project, a research team from the University of Lübeck led by Prof. Sebastian Otte is working together with Mercedes-Benz AG, TWT GmbH Science & Innovation, Intel Deutschland GmbH and the Technical University of Munich on energy-efficient radar sensor technology. The use of neuromorphic computing and spiking neural networks (SNNs) is intended to optimize battery life, shorten reaction times and increase safety. The project is funded by the Federal Ministry for Economic Affairs and Climate Protection (BMWK) and supported by the project sponsor TÜV Rheinland.
Autonomous vehicles require precise sensors in order to react quickly and reliably to their environment. Current research aims to improve the energy efficiency of sensor data processing in order to maximize battery life and reduce CO₂ emissions, for example. Prof. Sebastian Otte from the Institute of Robotics and Cognitive Systems and his team are developing innovative solutions in this area. In the NAOMI4Radar project, the team at the University of Lübeck is working together with Mercedes-Benz AG, TWT GmbH Science & Innovation and associated partners Intel Deutschland GmbH and the Technical University of Munich to optimize radar sensor technology for autonomous vehicles through the use of neuromorphic computing. This innovative technology is based on the way the human brain works and enables energy-efficient and fast processing of sensor data. The Lübeck research team will receive funding of around 166,000 euros.
Neural networks for radar data processing
The developments in the field of artificial neural networks, which were awarded the Nobel Prize in Physics in 2024, are also a key technology in radar data processing in the NAOMI4Radar project. The aim of the project is to make radar data processing more efficient using spiking neural networks (SNNs), a special form of neural network. In comparison to conventional AI algorithms, SNNs offer the advantage that individual neurons only become active when they are actually needed. This potential can be exploited by using them in neuromorphic processors, as envisaged in Intel's Loihi 2. This not only reduces energy consumption, but in principle also enables autonomous vehicles to react more quickly, thereby increasing road safety.
Energy-saving neuron models
Prof. Otte and his team are focusing in particular on the further development of the Balanced Resonate-and-Fire (BRF) model, whose special properties make it particularly interesting for the efficient processing of radar data. Efficiency is to be increased even further by using biologically inspired sparse coding approaches. Sparse coding aims to improve the robustness of neural networks, for example to make them more fault-tolerant.
At the same time, the activity, i.e. the amount of spikes circulating in the network, is reduced to a minimum. In collaboration with the project partners, a complete integration of neuromorphic radar data processing is to be realized and tested in a prototype vehicle.
The project, which will run until August 2025, is funded by the Federal Ministry for Economic Affairs and Climate Protection (BMWK). The University of Lübeck is contributing its expertise in the field of artificial intelligence and neuromorphic algorithms to this practice-oriented research project, thereby contributing to the development and testing of sustainable AI solutions in an industrial context.
Original publication on the Lübeck BRF model
Higuchi et al. Balanced Resonate-and-Fire Neurons. Proceedings of the 41st International Conference on Machine Learning, Vienna, Austria, 2024.
Contact:
Prof. Sebastian Otte
Adaptive AI Research Group
Institute for Robotics and Cognitive Systems
University of Lübeck
Email: sebastian.otte(at)uni-luebeck(dot)de
(Translated by DeepL)
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Everything is a research regarding our technology
no products launched. So nothing wrong about it to post. Thanks @Bravo
Best post in this forum for months.
True but Loihi is a research chip and NOT AVAILABLE commercially.....
Interesting… the whole time there are so many amazing news and progress with our company… no reaction from your side. And suddenly, you find this is one of the best postings for months ?
congrats
