BRN Discussion Ongoing

IloveLamp

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Definitely been some interactions between T-Mobile staff and BrainChip on LinkedIn recently

Imo dyor

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Bravo

If ARM was an arm, BRN would be its biceps💪!
Definitely been some interactions between T-Mobile staff and BrainChip on LinkedIn recently

Imo dyor

View attachment 69604 View attachment 69605

Looking good @IloveLamp!

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Intel were helping Ericsson on this whole RAN thingamejig with Loihi 2 (see LinkedIn post below the article from approx 4 months ago), but, as we all know Loihi 2 is Intel's latest research chip and it isn't commercialised yet so that leaves...ummm...let me see...Oh, that's right, that leaves us...I hope ! 🤞

IMO. DYOR.




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T-Mobile Announces Technology Partnership with NVIDIA, Ericsson and Nokia to Advance the Future of Mobile Networking with AI at the Center​

September 18, 2024
New Cutting-Edge AI-RAN Innovation Center to Tap into Combination of 5G Advanced and AI to Revolutionize Customer Experiences and Unlock New Economic Opportunities
NVIDIA_Release-NR-500x250.jpg


6 min readListen


SAN FRANCISCOSept. 18, 2024 — To further advance its 5G leadership position that is already years ahead of its closest competitor, T-Mobile (NASDAQ: TMUS) today announced a collaboration with NVIDIA, Ericsson and Nokia to design and drive the future of mobile networks with AI at the center, revolutionizing the capabilities of radio access networks (RAN) to serve customers in unprecedented ways. Leveraging T-Mobile’s 5G leadership, the NVIDIA AI Aerial platform and Ericsson’s and Nokia’s global leadership in telecommunications solutions, the consortium of companies, all founding members of the AI-RAN Alliance, are investing in an industry-first AI-RAN Innovation Center based in Bellevue, Washington, focused on bringing RAN and AI innovation closer together to deliver transformational network experiences for customers through the development of AI-RAN.
AI-RAN will dramatically improve customers’ real-world network experiences and ever-growing demand for increased speeds, reduced latency, and increased reliability needed for the latest gaming, video, social media and augmented reality applications they like to enjoy on their mobile and fixed wireless devices. AI-RAN will do this by leveraging billions of data points to devise algorithms that determine optimal network adjustments for maximum performance and to predict real-time capacity where customers need it.

A
I will not only power RAN performance and automate operations but will supercharge mobile network infrastructure to simultaneously run third-party AI application workloads at the network edge. AI-RAN comes in conjunction with other 5G Advanced features being rapidly developed with T-Mobile’s partners. AI-RAN concepts will be built in an open and containerized manner like Open RAN, with virtualized RAN and Core components managed from a central cloud, but AI-RAN is a game-changing technology because it will enhance the current Open RAN architecture with the addition of the accelerated computing that GPUs can bring to the intense network processing workloads of the future. In other words, this partnership aims to show that AI-RAN will make the promises of Open RAN more viable, while also going beyond.
“Just like T-Mobile led in 5G, we intend to lead in the next wave of network technology, for the benefit of our customers,” said Mike Sievert, CEO of T-Mobile. “AI-RAN at T-Mobile will be all about unlocking the massive capacity and performance that customers increasingly demand from mobile networks. AI-RAN has tremendous potential to completely transform the future of mobile networks, but it will be difficult to get right. That’s why T-Mobile is jumping in now to help lead the way with our partners. This collaboration between T-Mobile, NVIDIA, Nokia and Ericsson will truly define what’s next in mobile networks in the 5G Advanced era and beyond, and drive real progress where it’s needed. This group of visionaries will work together at our new Bellevue AI-RAN Innovation Center, and the partnership will not only propel the mobile network industry forward, but also has the potential to eventually advance many others as well.”
“AI will reinvent the wireless communication network and industry — going beyond voice, data, and video to support a wide range of new applications like generative AI and robotics,” said Jensen Huang, founder and CEO of NVIDIA. "NVIDIA AI Aerial is a platform that unifies communications, computing and AI. Working closely with the industry’s leaders, we will extend AI traffic to wireless networks and use AI to reinvent wireless communications.”
“Ericsson is excited to contribute to the 'Joint AI-RAN Innovation Center', which is set to drive standardization, industry alignment, and accelerate the adoption of AI-RAN technologies. This paves the way for potentially limitless innovations in network performance, reliability, and efficiency,” said Börje Ekholm, President and CEO of Ericsson. “As a founding member of the AI-RAN Alliance, we are not only committed to positioning the United States as a leader in the commercialization of AI-RAN solutions but also to exploring and harnessing future opportunities in multi-purpose cellular and AI-optimized networks.”
“AI is a game-changer for every industry, but particularly in telecoms where it will revolutionize networks and enable a variety of new applications,” said Pekka Lundmark, President and CEO, Nokia. “Our U.S headquartered Nokia Bell Labs is leading our global AI research so it is a natural fit to extend our partnership with T-Mobile on the development of their AI-RAN Innovation Center in Bellevue, Washington. We look forward to collaborating on new AI-RAN innovations to transform network security, performance and efficiency with the aim of yielding savings in network operations and increasing monetization opportunities for operators.”
A first-of-its kind AI-RAN cloud-based multipurpose network will have the potential to support not only traditional telecommunications workloads (core network and radio access network: RAN) but also AI workloads (internal and external AI as a Service or AIaaS, a cloud-based paradigm that provides access to AI capabilities in T-Mobile’s network without the need for dedicated, in-house infrastructure). With increased capacity, energy efficiencies and improved resiliency, the same platform will carry voice, video, data, and also new generative AI applications, and have the ability to make contextual AI-powered decisions around network performance and traffic routing for different applications and circumstances. Customers will benefit from better contextual, predictive and frictionless experiences on their devices. AI-RAN will also create significant enterprise cost savings and revenue growth that could also be applied to a variety of other businesses and industries.
The new AI-RAN Innovation Center will further accelerate the mission of the AI-RAN Alliance, which was announced in February 2024 at GSMA Mobile World Congress in Barcelona with a mission to enhance mobile network efficiency, reduce power consumption and retrofit existing infrastructure to unlock new economic opportunities for telecommunications companies with AI, facilitated by 5G and setting the stage for global leadership on 6G. T-Mobile, NVIDIA, Ericsson and Nokia were all founding members, together with other technology and industry leaders.
More on NVIDIA’s AI Aerial platform here:
About T-Mobile
T-Mobile US, Inc. (NASDAQ: TMUS) is America’s supercharged Un-carrier, delivering a transformative nationwide 5G network that offers reliable connectivity for all. T-Mobile’s customers benefit from its unmatched combination of value and quality, unwavering obsession with offering them the best possible service experience and undisputable drive for disruption that creates competition and innovation in wireless and beyond. Based in Bellevue, Washington, T-Mobile provides services through its subsidiaries and operates its flagship brands, T-Mobile, Metro by T-Mobile and Mint Mobile. For more information please visit: https://www.t-mobile.com




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GStocks123

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JB49

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Not sure if shared yet...
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Bravo

If ARM was an arm, BRN would be its biceps💪!
Quantum Corporation as opposed to Quantum Ventura.

1.)
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2)

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3)

Screenshot 2024-09-21 at 6.56.22 pm.png


 
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Justchilln

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Quantum Corporation as opposed to Quantum Ventura.

1.)
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2)

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3)

View attachment 69620

That was for one of brainchips earliest failures “studio”
 

Bravo

If ARM was an arm, BRN would be its biceps💪!
That was for one of brainchips earliest failures “studio”

Then why is BrainChip listed on their website CURRENTLY as a partner?
 
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Diogenese

Top 20
Christmas past or Marley
Quantum Corporation as opposed to Quantum Ventura.

1.)
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2)

View attachment 69621




3)

View attachment 69620

Christmas Past or Marley's ghost?
 
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The AI Innovator
open menu

AI MODELS/TOOLS​

Read more

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Featured Videos: MIT Course on Deep Learning

Part 1: Introduction to Deep Learning

Part 2: Recurrent Neural Networks, Transformers and Attention​


Part 3: Deep Computer Vision / Convolutional Neural Networks​


For the rest of the series, click here.
computer chip image

Why the Future of AI Depends on Compound Semiconductors​

BY RODNEY PELZEL ON SEPTEMBER 18, 2024
AI’s on-screen iterations, including the use of generative AI in chatbots, have been grabbing the majority of the world’s attention since the debut of ChatGPT in late 2022. For its next act, AI will move out of the cloud to devices at the edge − with the future of AI being off-screen and in the physical world.
This move will supercharge robotics and IoT, and the technology driving it all will be compound semiconductors, including the material gallium nitride (GaN). Compound semiconductors, or computer chips made from two or more elements as opposed to single-element silicon, are becoming increasingly important in areas where higher performance is required.
AI’s future at the edge cannot happen without compound semiconductor materials that outperform silicon in three key ways. First, they operate very efficiently at RF frequencies. Second, they are effective emitters and detectors of light over a broad spectrum. Third, they are very efficient, particularly in harsh environments for such things as power conversion.

Increased functionality of devices at the edge

The transition of AI off-screen into the realm of IoT means that the devices at the edge will need to get more capable. For example, they will be required to ‘sense’ their environment in ultra-high resolution − meaning that 3D recognition and LiDAR systems like those employed in mobile handsets will become essential for all sorts of devices where this capability is not needed today.
This is a realm pioneered by compound semiconductor lasers and detectors that employ gallium arsenide (GaAs) and indium phosphide (InP), and improved versions of these devices will be necessary for AI progression. Furthermore, it is not only physically sensing things in three dimensions that will be important; wearable health monitors use compound semiconductor materials for biological sensing, an area poised to be revolutionized by off-screen AI.
Finally, the next version of AI will require ultra-fast, ultra-reliable, low latency connectivity even with things at the edge, and the excellent RF properties of compound semiconductors, particularly GaAs and GaN, will be exploited to enable this capability.

Increasing efficiency, reducing power demands

When it comes to AI, much of the world’s attention is currently focused on the energy needs of data centers, and rightly so – and if all data centers around the world were converted from silicon to GaN by the end of the decade, it is estimated that energy loss would be reduced by 30% to 40%. To put that into perspective, the conversion would save more than 100 terawatt hours and avoid 125 megatons of carbon dioxide emissions.
While much of the compute is expected to move to the edge for the next iteration of AI, the energy requirements will remain high; energy usage will scale regardless of whether the compute is occurring in the cloud or at the edge. This makes compound semiconductor materials a necessity for AI to progress.
In addition, the compound semiconductor material GaN is very robust, making it ideal for edge devices such as robotics that operate in harsh environments with elevated temperatures, higher humidities, and the like.
It is easy to see how this will benefit industrial robotics at the edge, but GaN’s capability could take things further, even as far as outer space. MIT researchers recently demonstrated that GaN was able to tolerate exposure to more than 900°F for 48 hours, making it a promising candidate for space exploration.

What’s next for AI

While the conversation around AI has certainly intensified over the last year and a half, one important thing to remember about this technology is that we are really at the beginning. Right now, generative AI applications are operating on massive amounts of data, pulling information from LLMs and incorporating clever algorithms to deliver a response.
In this next ‘off-screen’ phase of AI, different bottlenecks for the technology will be encountered. The limitations will move to the devices at the edge. The sense and connect demands on edge devices will become extreme, as the next version of AI will require devices to operate on real time data in ways never seen before.
This, in turn, will make these devices more power hungry. To address these new requirements, compound semiconductor materials will be required, and materials such as GaN will become indispensable.

Author​

  • Rodney Pelzel

    Rodney Pelzel
    Rodney Pelzel is the CTO of IQE plc, a global supplier of advanced wafer products and material solutions to the semiconductor industry. He has deep expertise in semiconductor materials engineering and the epitaxial growth of compound semiconductors. His work has been widely published and he is the co-inventor of over 30 patents.
 
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Encouraging words by our CTO:

View attachment 69586
I remember reading somewhere (thought it might be in the original CTO announcement, but it's not) that Tony has a strong commercialisation focus (especially for a CTO, who are known to like to "tinker")..

Whether it was actually said or it's just a figment of my imagination, it's clearly evidenced, by his statement, that he definitely is commercialisation focused!
 
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rgupta

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I remember reading somewhere (thought it might be in the original CTO announcement, but it's not) that Tony has a strong commercialisation focus (especially for a CTO, who are known to like to "tinker")..

Whether it was actually said or it's just a figment of my imagination, it's clearly evidenced, by his statement, that he definitely is commercialisation focused!
Not sure he is commercialisation focused or not but definately he is out spoken and wants to do something than to defend himself. To me he is an aggressor and will not stop from challenging himself and his competitors.
He is definately on a mission.
 
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Tezza

Regular
2024 is quickly coming to an end. It's probably time for Sean to come out and say 2025 is our year! 🙃
 
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Bravo

If ARM was an arm, BRN would be its biceps💪!

‘We’ve Fused Signal Processing and AI’: NVIDIA CEO Outlines Future of Telecom at T-Mobile’s Capital Markets Day​

Jensen Huang joined T-Mobile CEO Mike Sievert in a surprise fireside chat on how the companies are advancing telecommunications with AI.
September 19, 2024 by Brian Caulfield
t-mobile-capital-market-talking-2.jpg

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In a surprise appearance at T-Mobile’s Capital Markets Day, NVIDIA founder and CEO Jensen Huang shared a bold vision for the future of telecommunications.
“We’ve fused signal processing and AI,” Huang declared during a fireside chat with T-Mobile CEO Mike Sievert, speaking to an audience of press, analysts and investors. “This is going to be a great new growth opportunity for the telecommunications industry.”
Huang’s remarks came alongside NVIDIA’s announcement of its groundbreaking AI Aerial platform, which promises to reshape wireless networks by integrating AI and radio access networks, AI-RAN.
The platform is designed to optimize network performance, efficiency and new revenue potential, such as AI-computing-as-a-service during periods when network infrastructure is underutilized, maximizing the return on assets.
During the conversation, Huang emphasized the importance of AI in shaping the future of telecommunications, particularly highlighting the role of AI-RAN in optimizing and scaling network performance.
Fusing radio computing and AI computing into one architecture allows companies to apply AI models to optimize signal quality across diverse environments, Huang explained.
He emphasized that this fusion would lead to improved network efficiency and new growth opportunities for the telecommunications industry,
“We could teach these AI models how to optimize signal quality in hundreds of thousands of virtual cities,” Huang said.
AI-RAN aligns with NVIDIA’s broader vision to make AI an integral part of network infrastructure, enabling telecommunications providers to unlock new revenue streams and deliver enhanced experiences through generative AI, robotics and autonomous technologies.

Huang underscored the synergies between NVIDIA and T-Mobile, particularly their collaboration on the newly announced AI-RAN Innovation Center, as co-authors of transformation. The AI-RAN Innovation Center, developed with T-Mobile, Ericsson and Nokia, is set to accelerate the commercialization of AI-RAN technologies.
Every radio operates in a unique and constantly changing world environment. This is where deep reinforcement learning algorithms embedded into radio signal processing make complex computations simpler with AI to help deliver a customer-centric network experience.
Sievert emphasized how virtualizing RAN into the cloud will create new business opportunities. He explained that AI workloads will increasingly require compute power located close to the customer, leveraging underutilized network resources.
Huang also highlighted the crucial role AI will play in making networks more energy-efficient, emphasizing the need for sustainable technology as the demand for data and connectivity grows.
“We have to use AI to reduce energy consumption,” Huang said. “Everything that we accelerate, everything that we teach an AI model to do [we] will do a lot more energy efficiently.”
As Huang explained, by simulating AI models in virtual environments with accurate physics and then emulating them in the real world, NVIDIA maximizes energy efficiency. This approach underpins the NVIDIA AI Aerial suite of platforms for designing, training and deploying AI-driven cellular networks for AI.
With NVIDIA AI Aerial now supporting a growing ecosystem of partners this collaboration marks a milestone in the telecom industry’s journey toward a future powered by AI.





Does this 👇have something to do with that☝️?



Screenshot 2024-09-22 at 10.31.25 am.png






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rgupta

Regular

‘We’ve Fused Signal Processing and AI’: NVIDIA CEO Outlines Future of Telecom at T-Mobile’s Capital Markets Day​

Jensen Huang joined T-Mobile CEO Mike Sievert in a surprise fireside chat on how the companies are advancing telecommunications with AI.
September 19, 2024 by Brian Caulfield
t-mobile-capital-market-talking-2.jpg

Share

In a surprise appearance at T-Mobile’s Capital Markets Day, NVIDIA founder and CEO Jensen Huang shared a bold vision for the future of telecommunications.
“We’ve fused signal processing and AI,” Huang declared during a fireside chat with T-Mobile CEO Mike Sievert, speaking to an audience of press, analysts and investors. “This is going to be a great new growth opportunity for the telecommunications industry.”
Huang’s remarks came alongside NVIDIA’s announcement of its groundbreaking AI Aerial platform, which promises to reshape wireless networks by integrating AI and radio access networks, AI-RAN.
The platform is designed to optimize network performance, efficiency and new revenue potential, such as AI-computing-as-a-service during periods when network infrastructure is underutilized, maximizing the return on assets.
During the conversation, Huang emphasized the importance of AI in shaping the future of telecommunications, particularly highlighting the role of AI-RAN in optimizing and scaling network performance.
Fusing radio computing and AI computing into one architecture allows companies to apply AI models to optimize signal quality across diverse environments, Huang explained.
He emphasized that this fusion would lead to improved network efficiency and new growth opportunities for the telecommunications industry,
“We could teach these AI models how to optimize signal quality in hundreds of thousands of virtual cities,” Huang said.
AI-RAN aligns with NVIDIA’s broader vision to make AI an integral part of network infrastructure, enabling telecommunications providers to unlock new revenue streams and deliver enhanced experiences through generative AI, robotics and autonomous technologies.

Huang underscored the synergies between NVIDIA and T-Mobile, particularly their collaboration on the newly announced AI-RAN Innovation Center, as co-authors of transformation. The AI-RAN Innovation Center, developed with T-Mobile, Ericsson and Nokia, is set to accelerate the commercialization of AI-RAN technologies.
Every radio operates in a unique and constantly changing world environment. This is where deep reinforcement learning algorithms embedded into radio signal processing make complex computations simpler with AI to help deliver a customer-centric network experience.
Sievert emphasized how virtualizing RAN into the cloud will create new business opportunities. He explained that AI workloads will increasingly require compute power located close to the customer, leveraging underutilized network resources.
Huang also highlighted the crucial role AI will play in making networks more energy-efficient, emphasizing the need for sustainable technology as the demand for data and connectivity grows.
“We have to use AI to reduce energy consumption,” Huang said. “Everything that we accelerate, everything that we teach an AI model to do [we] will do a lot more energy efficiently.”
As Huang explained, by simulating AI models in virtual environments with accurate physics and then emulating them in the real world, NVIDIA maximizes energy efficiency. This approach underpins the NVIDIA AI Aerial suite of platforms for designing, training and deploying AI-driven cellular networks for AI.
With NVIDIA AI Aerial now supporting a growing ecosystem of partners this collaboration marks a milestone in the telecom industry’s journey toward a future powered by AI.





Does this 👇have something to do with that☝️?



View attachment 69633





View attachment 69632
I remember Sean used to say they are working with a big communication company, that was approx 18 months ago and since then there was no talk on that. May be it was T mobile and then chose to go with Nvidia later on. But definately that will mean Sean's claim we have not lost a customer will be wrong.
Anyway the things are so much wired that more you try to solve them, more entangled them become.
Let us wait and watch and look at financials.
Dyor
 
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2024 is quickly coming to an end. It's probably time for Sean to come out and say 2025 is our year! 🙃
1726972840503.gif



1726972924947.gif
 
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Not sure he is commercialisation focused or not but definately he is out spoken and wants to do something than to defend himself. To me he is an aggressor and will not stop from challenging himself and his competitors.
He is definately on a mission.
Let’s hope so

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Diogenese

Top 20

‘We’ve Fused Signal Processing and AI’: NVIDIA CEO Outlines Future of Telecom at T-Mobile’s Capital Markets Day​

Jensen Huang joined T-Mobile CEO Mike Sievert in a surprise fireside chat on how the companies are advancing telecommunications with AI.
September 19, 2024 by Brian Caulfield
t-mobile-capital-market-talking-2.jpg

Share

In a surprise appearance at T-Mobile’s Capital Markets Day, NVIDIA founder and CEO Jensen Huang shared a bold vision for the future of telecommunications.
“We’ve fused signal processing and AI,” Huang declared during a fireside chat with T-Mobile CEO Mike Sievert, speaking to an audience of press, analysts and investors. “This is going to be a great new growth opportunity for the telecommunications industry.”
Huang’s remarks came alongside NVIDIA’s announcement of its groundbreaking AI Aerial platform, which promises to reshape wireless networks by integrating AI and radio access networks, AI-RAN.
The platform is designed to optimize network performance, efficiency and new revenue potential, such as AI-computing-as-a-service during periods when network infrastructure is underutilized, maximizing the return on assets.
During the conversation, Huang emphasized the importance of AI in shaping the future of telecommunications, particularly highlighting the role of AI-RAN in optimizing and scaling network performance.
Fusing radio computing and AI computing into one architecture allows companies to apply AI models to optimize signal quality across diverse environments, Huang explained.
He emphasized that this fusion would lead to improved network efficiency and new growth opportunities for the telecommunications industry,
“We could teach these AI models how to optimize signal quality in hundreds of thousands of virtual cities,” Huang said.
AI-RAN aligns with NVIDIA’s broader vision to make AI an integral part of network infrastructure, enabling telecommunications providers to unlock new revenue streams and deliver enhanced experiences through generative AI, robotics and autonomous technologies.

Huang underscored the synergies between NVIDIA and T-Mobile, particularly their collaboration on the newly announced AI-RAN Innovation Center, as co-authors of transformation. The AI-RAN Innovation Center, developed with T-Mobile, Ericsson and Nokia, is set to accelerate the commercialization of AI-RAN technologies.
Every radio operates in a unique and constantly changing world environment. This is where deep reinforcement learning algorithms embedded into radio signal processing make complex computations simpler with AI to help deliver a customer-centric network experience.
Sievert emphasized how virtualizing RAN into the cloud will create new business opportunities. He explained that AI workloads will increasingly require compute power located close to the customer, leveraging underutilized network resources.
Huang also highlighted the crucial role AI will play in making networks more energy-efficient, emphasizing the need for sustainable technology as the demand for data and connectivity grows.
“We have to use AI to reduce energy consumption,” Huang said. “Everything that we accelerate, everything that we teach an AI model to do [we] will do a lot more energy efficiently.”
As Huang explained, by simulating AI models in virtual environments with accurate physics and then emulating them in the real world, NVIDIA maximizes energy efficiency. This approach underpins the NVIDIA AI Aerial suite of platforms for designing, training and deploying AI-driven cellular networks for AI.
With NVIDIA AI Aerial now supporting a growing ecosystem of partners this collaboration marks a milestone in the telecom industry’s journey toward a future powered by AI.





Does this 👇have something to do with that☝️?



View attachment 69633





View attachment 69632
Hot off the press:

This is Nvidia's recently published NN patent:

WO2024183052A1 FEDERATED LEARNING TECHNIQUE 20230309; pub 20240912
= US2024303504A1 FEDERATED LEARNING TECHNIQUE

1726972289781.png


1726972255193.png
FIG 9C

1726972944122.png


Apparatuses, systems, and techniques to train/use one or more neural networks. In at least one embodiment, a processor comprises one or more circuits to cause neural network training information to be aggregated based, at least in part, on contribution of the neural network training data and one or more performance metrics of the neural network.

It's a massive document 263 pages including 56 pages of drawings.

It's about federated learning using cloud-based processors to download, eg, NNs and maps to cars (Fig 9D). Fig 9C shows the vehicle's sensor/processor configuration. sensor fusion would be part of this.

1726973672664.png
 
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Terroni2105

Founding Member
Hot off the press:

This is Nvidia's recently published NN patent:

WO2024183052A1 FEDERATED LEARNING TECHNIQUE 20230309; pub 20240912
= US2024303504A1 FEDERATED LEARNING TECHNIQUE

View attachment 69635

View attachment 69634 FIG 9C

View attachment 69638

Apparatuses, systems, and techniques to train/use one or more neural networks. In at least one embodiment, a processor comprises one or more circuits to cause neural network training information to be aggregated based, at least in part, on contribution of the neural network training data and one or more performance metrics of the neural network.

It's a massive document 263 pages including 56 pages of drawings.

It's about federated learning using cloud-based processors to download, eg, NNs and maps to cars (Fig 9D). Fig 9C shows the vehicle's sensor/processor configuration. sensor fusion would be part of this.

View attachment 69639
What are you saying Dio?
 
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Diogenese

Top 20
What are you saying Dio?
Jensen was banging on about sensor fusion shortly after this patent was published, so it's within the realms of possibility that this patent is related to sensor fusion (in the car) among other things like federated learning (in the cloud).

As I recall, previous Nvidia patents do not get down to the details of the NN hardware, and I haven't read this one yet, but I don't think it gets down to that level of detail. Let's hope their AI engineers have been smart enough to make the right choice.
 
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Jensen was banging on about sensor fusion shortly after this patent was published, so it's within the realms of possibility that this patent is related to sensor fusion (in the car) among other things like federated learning (in the cloud).

As I recall, previous Nvidia patents do not get down to the details of the NN hardware, and I haven't read this one yet, but I don't think it gets down to that level of detail. Let's hope their AI engineers have been smart enough to make the right choice.
What are you saying Dio? 😂
 
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