BRN Discussion Ongoing

[Foxdog]

.

 

[jtardif999]

Patents are worthless if they're defending a concept that's not commercially viable. If BRN doesn't start making serious revenue soon then it won't have the resources to defend patent infringements anyway. This is simply a red herring designed to distract would-be investors from what is actually going on.
'The cornerstone of BRN value' what a bloody joke. The actual cornerstone of BRN value is IP licences, royalties and big tier 1 companies shouting from the rooftops about how amazing akida is. None of which appear to be happening.

You are completely wrong imo, BrainChips patents are everything, so important to our future worth and not only from the point of defending but as an attraction in takeover. It seems to me that you don’t really get how unique BrainChips technology is and how important it could likely become. Let’s for a moment think of the time when inevitably the limits and sustainability of current AI solutions is exhausted. It will happened, it can’t go on the present path that Nvidia are squeezing out atm. The time will come when BRNs tech is suddenly the tech of choice (they are setting themselves up for it) and then we will get very big very quickly or, someone will want control of our patents so badly they will be willing to pay an incredible premium for them. AIMO.

 

[IloveLamp]

Arm IPO Underscores the Ongoing Compute Supercycle

www.sifive.com

 

[IloveLamp]

 

[Schnitzel lover]

Maximum

This brand does door bells too, and very interesting is the 1 TOPS NPU.

My spidey sense is tingling.

Bring it on shorters!
I have no doubt in my mind this has Brainchip Akida S.
I’d like someone to prove me wrong.

Not financial advice.

i have the current version. It requires internet connectivity, power inefficient, doesn’t have specific facial recognition & requires fairly regular recharging, 1-2 x monthly.

If the Internet cuts out it has to be reset and takes ages fluffing around re-setting it it.

I’ve been banging on to my wife about how good it would be if it had Akida inside, so in my view the newest versions would be screaming out for the efficiency benefits of Akida.

Whilst on that topic, the Dee-bot vacuums are in the same boat- rely on internet connection, constant resetting. Another strong use case for Akida..

 

[Frangipani]

Hadn't personally seen this project over at Edge Impulse before.

Google search link said late May 23 but who knows.

Have taken all the code sections etc out but full read at the link. Pretty cool running with FOMO.

https://docs.edgeimpulse.com/experts/featured-machine-learning-projects/brainchip-akida-traffic-monitoring

Here is another article about Naveen Kumar’s traffic monitoring project, published on Wevolver today - they really seem to love Akida!

Real-Time Traffic Monitoring with Neuromorphic Computing

Article #5 of Spotlight on Innovations in Edge Computing and Machine Learning: A computer vision project that monitors vehicle traffic in real-time using video inferencing performed on the Brainchip Akida Development Kit.

www.wevolver.com

Real-Time Traffic Monitoring with Neuromorphic Computing​

David Tischler
14 Sep, 2023
FOLLOW

Article #5 of Spotlight on Innovations in Edge Computing and Machine Learning: A computer vision project that monitors vehicle traffic in real-time using video inferencing performed on the Brainchip Akida Development Kit.​

Artificial Intelligence
- Edge Processors
- Embedded Machine Learning
- Neural Network
- Transportation

This article is part of Spotlight on Innovations in Edge Computing and Machine Learning. The series features some unique projects from around the world that leverage edge computing and machine learning, showcasing the ways these technological advancements are driving growth, efficiency, and innovation across various domains.
This series is made possible through the sponsorship of Edge Impulse, a leader in providing the platform for building smarter, connected solutions with edge computing and machine learning.

In the ever-evolving landscape of urban planning and development, the significance of efficient real-time traffic monitoring cannot be overstated. Traditional systems, while functional, often fall short when high-performance data processing is required in a low-power budget. Enter neuromorphic computing—a technology inspired by the neural structure of the brain, aiming to combine efficiency with computational power. This article delves into an interesting computer vision project that monitors vehicle traffic using this paradigm.

Utilizing aerial camera feeds, the project can detect moving vehicles with exceptional precision, making it a game-changer for city planners and governments aiming to optimize urban mobility. The key lies in the advanced neuromorphic processor that serves as the project's backbone. This processor is not just about low power consumption—it also boasts high-speed inference capabilities, making it ideal for real-time video inferencing tasks.

But the journey doesn't end at hardware selection. This article covers the full spectrum of the project, from setting up the optimal development environment and data collection methods to model training and deployment strategies. It offers a deep dive into how neuromorphic computing can be applied in real-world scenarios, shedding light on the processes of data acquisition, labeling, model training, and final deployment. As we navigate through the complexities of urban challenges, such insights pave the way for smarter, more efficient solutions in traffic monitoring and beyond.

---

Traffic Monitoring using the Brainchip Akida Neuromorphic Processor​

Created By: Naveen Kumar
Public Project Link: https://studio.edgeimpulse.com/public/222419/latest

Overview​

A highly efficient computer-vision system that can detect moving vehicles with great accuracy and relative motion, all while consuming minimal power.

By capturing moving vehicle images, aerial cameras can provide information about traffic conditions, which is beneficial for governments and planners to manage traffic and enhance urban mobility. Detecting moving vehicles with low-powered devices is still a challenging task. We are going to tackle this problem using a Brainchip Akida neural network accelerator.

Hardware Selection​

In this project, we'll utilize BrainChip’s Akida Development Kit. BrainChip's neuromorphic processor IP uses event-based technology for increased energy efficiency. It allows incremental learning and high-speed inference for various applications, including convolutional neural networks, with exceptional performance and low power consumption.

The kit consists of an Akida PCie board, a Raspberry Pi Compute Module 4 with Wi-Fi and 8 GB RAM, and a Raspberry Pi Compute Module 4 I/O Board. The disassembled kit is shown below.

Hardware UnassembledThe Akida PCIe board can be connected to the Raspberry Pi Compute Module 4 IO Board through the PCIe Gen 2 x1 socket available onboard.

Hardware Closeup

Setting up the Development Environment​

(…)

Conclusion​

This project highlights the impressive abilities of the Akida PCIe board. Boasting low power consumption, it could be used as a highly effective device for real-time object detection in various industries for numerous use cases.

---

This article is based on: Traffic Monitoring using the Brainchip Akida Neuromorphic Processor - Expert Projects, a blog by Edge Impulse. It has been edited by the Wevolver team and Electrical Engineer Ravi Y Rao. It's the third article from the Spotlight on Innovations in Edge Computing and Machine Learning Series.
The first article introduced the series and explored the implementation of Predictive Maintenance system using a Nordic Thingy:91.
The second article described the implementation of voice control for making appliances smarter using a Nordic Thingy:53.
The third article dives deep into the application of EdgeAI for surface crack detection, showcasing its transformative role in modern industrial predictive maintenance systems.
The fourth article explains the integration of neuromorphic computing for real-time traffic monitoring, offering a technical blueprint for revolutionizing urban management.

---

About the sponsor: Edge Impulse​

Edge Impulse is the leading development platform for embedded machine learning, used by over 1,000 enterprises across 200,000 ML projects worldwide. We are on a mission to enable the ultimate development experience for machine learning on embedded devices for sensors, audio, and computer vision, at scale.
From getting started in under five minutes to MLOps in production, we enable highly optimized ML deployable to a wide range of hardware from MCUs to CPUs, to custom AI accelerators. With Edge Impulse, developers, engineers, and domain experts solve real problems using machine learning in embedded solutions, speeding up development time from years to weeks. We specialize in industrial and professional applications including predictive maintenance, anomaly detection, human health, wearables, and more.

More by David Tischler​

FOLLOW
David is a Senior Developer Program Manager helping to take care of the Edge Impulse community of developers, and is a fan of computing on small, low power devices. He's also an extreme recycler, so use caution if trying to throw away recyclable objects if he's around.

 

[Esq.111]

Good Morning Chippers ,

Well ARM is now a listed / tradable stock.

Market liked it.

Arm climbs 25% in Nasdaq debut after pricing IPO at $51 a share

Arm Holdings, the chip design firm, has begun trading on the Nasdaq in a long-anticipated IPO at a $54 billion valuation.

www.cnbc.com

Regards,
Esq

 

[Foxdog]

.

 

[Labsy]

iPhone 15 USES akida and so does the new Apple Watch, and AirPods Pro.

Apple revenue is what Sean has been referring to cryptically.

Mark my words.

Will go out on a limb to say iPhone 16 will have akida 2.0 P in it.

Monday 18/09/23 is the date we’ve been waiting for!!! I feel it.

Time to shift gears and SEND ITTTTTT !!!
Go Brainchip!!!!


Not financial advice.

Apple debuts iPhone 15 and iPhone 15 Plus

Apple today announced iPhone 15 and iPhone 15 Plus, featuring an industry-first colour-infused back glass with a stunning, textured matte finish.

www.apple.com

" A new 16-core Neural Engine is capable of nearly 17 trillion operations per second, enabling even faster machine learning computations for features like Live Voicemail transcriptions in iOS 17 and third-party app experiences — all while protecting critical privacy and security features using the Secure Enclave."
Wouldn't it be nice......

 

[Frangipani]

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.

www.nature.com

A retrainable neuromorphic biosensor for on-chip learning and classification? Mmmmh, friend or foe?

Does anyone here have institutional access to delve a little deeper?

The paper’s authors are two researchers each from Eindhoven University of Technology (https://www.tue.nl/en/research/rese...stems/neuromorphic-edge-computing-systems-lab ) and Northwestern University (https://rivnay.northwestern.edu/ and http://nu-vlsi.eecs.northwestern.edu/). The latter research group states on its website that they have been collaborating with IBM, Texas Instruments (!) and Intel.

• 4/2023 Three papers are accepted into the prestigious VLSI Symposium'23. Congratulate to Yuhao, Xi, Yijie and all the authors. Special thanks to our collaborators Xin Zhang from IBM, Raveesh Magod from TI, and Nachiket Desai from Intel.

Published: 14 September 2023

A retrainable neuromorphic biosensor for on-chip learning and classification​

• E. R. W. van Doremaele,
• X. Ji,
• J. Rivnay &
• Y. van de Burgt

Nature Electronics (2023)

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.

This is a preview of subscription content, access via your institution

---

Just discovered an article on the novel biosensor on the TU Eindhoven website as well:

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.

www.tue.nl

Breakthrough way to train neuromorphic chips​

SEPTEMBER 14, 2023
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.

Photo: iStockPhoto

Neuromorphic computers – which are based on the structure of the human brain – could revolutionize our future healthcare 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 US 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 US that has just been published in Nature Electronics,

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 healthcare 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 healthcare.”

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.

Eveline van Doremaele

Goodbye to external software​

So, what is the problem that van Doremaele and her collaborators solved? “For practical use in healthcare 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.

The neuromorphic biosensing chip. Image: Eveline van Doremaele

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​

The real novelty is that the chips can learn and adapt to their application and environment.

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 healthcare 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.”

​

Further information​

A retrainable neuromorphic biosensor for on-chip learning and classification, van Doremaele et al., Nature Electronics, (2023).

​

Media contact​

Barry Fitzgerald

(Science Information Officer)
+31 40 247 8067 B.Fitzgerald@tue.nl

 

[Boab]

" A new 16-core Neural Engine is capable of nearly 17 trillion operations per second, enabling even faster machine learning computations for features like Live Voicemail transcriptions in iOS 17 and third-party app experiences — all while protecting critical privacy and security features using the Secure Enclave."
Wouldn't it be nice......

17 trillion operations per second.....Pfffft
We do 131

 

[Rach2512]

Here is another article about Naveen Kumar’s traffic monitoring project, published on Wevolver today - they really seem to love Akida!

Real-Time Traffic Monitoring with Neuromorphic Computing

Article #5 of Spotlight on Innovations in Edge Computing and Machine Learning: A computer vision project that monitors vehicle traffic in real-time using video inferencing performed on the Brainchip Akida Development Kit.

www.wevolver.com

Real-Time Traffic Monitoring with Neuromorphic Computing​

David Tischler
14 Sep, 2023
FOLLOW

Article #5 of Spotlight on Innovations in Edge Computing and Machine Learning: A computer vision project that monitors vehicle traffic in real-time using video inferencing performed on the Brainchip Akida Development Kit.​

Artificial Intelligence
- Edge Processors
- Embedded Machine Learning
- Neural Network
- Transportation

This article is part of Spotlight on Innovations in Edge Computing and Machine Learning. The series features some unique projects from around the world that leverage edge computing and machine learning, showcasing the ways these technological advancements are driving growth, efficiency, and innovation across various domains.
This series is made possible through the sponsorship of Edge Impulse, a leader in providing the platform for building smarter, connected solutions with edge computing and machine learning.

In the ever-evolving landscape of urban planning and development, the significance of efficient real-time traffic monitoring cannot be overstated. Traditional systems, while functional, often fall short when high-performance data processing is required in a low-power budget. Enter neuromorphic computing—a technology inspired by the neural structure of the brain, aiming to combine efficiency with computational power. This article delves into an interesting computer vision project that monitors vehicle traffic using this paradigm.

Utilizing aerial camera feeds, the project can detect moving vehicles with exceptional precision, making it a game-changer for city planners and governments aiming to optimize urban mobility. The key lies in the advanced neuromorphic processor that serves as the project's backbone. This processor is not just about low power consumption—it also boasts high-speed inference capabilities, making it ideal for real-time video inferencing tasks.

But the journey doesn't end at hardware selection. This article covers the full spectrum of the project, from setting up the optimal development environment and data collection methods to model training and deployment strategies. It offers a deep dive into how neuromorphic computing can be applied in real-world scenarios, shedding light on the processes of data acquisition, labeling, model training, and final deployment. As we navigate through the complexities of urban challenges, such insights pave the way for smarter, more efficient solutions in traffic monitoring and beyond.

---

Traffic Monitoring using the Brainchip Akida Neuromorphic Processor​

Created By: Naveen Kumar
Public Project Link: https://studio.edgeimpulse.com/public/222419/latest

Overview​

A highly efficient computer-vision system that can detect moving vehicles with great accuracy and relative motion, all while consuming minimal power.

By capturing moving vehicle images, aerial cameras can provide information about traffic conditions, which is beneficial for governments and planners to manage traffic and enhance urban mobility. Detecting moving vehicles with low-powered devices is still a challenging task. We are going to tackle this problem using a Brainchip Akida neural network accelerator.

Hardware Selection​

In this project, we'll utilize BrainChip’s Akida Development Kit. BrainChip's neuromorphic processor IP uses event-based technology for increased energy efficiency. It allows incremental learning and high-speed inference for various applications, including convolutional neural networks, with exceptional performance and low power consumption.

The kit consists of an Akida PCie board, a Raspberry Pi Compute Module 4 with Wi-Fi and 8 GB RAM, and a Raspberry Pi Compute Module 4 I/O Board. The disassembled kit is shown below.

Hardware UnassembledThe Akida PCIe board can be connected to the Raspberry Pi Compute Module 4 IO Board through the PCIe Gen 2 x1 socket available onboard.

Hardware Closeup

Setting up the Development Environment​

(…)

Conclusion​

This project highlights the impressive abilities of the Akida PCIe board. Boasting low power consumption, it could be used as a highly effective device for real-time object detection in various industries for numerous use cases.

---

This article is based on: Traffic Monitoring using the Brainchip Akida Neuromorphic Processor - Expert Projects, a blog by Edge Impulse. It has been edited by the Wevolver team and Electrical Engineer Ravi Y Rao. It's the third article from the Spotlight on Innovations in Edge Computing and Machine Learning Series.
The first article introduced the series and explored the implementation of Predictive Maintenance system using a Nordic Thingy:91.
The second article described the implementation of voice control for making appliances smarter using a Nordic Thingy:53.
The third article dives deep into the application of EdgeAI for surface crack detection, showcasing its transformative role in modern industrial predictive maintenance systems.
The fourth article explains the integration of neuromorphic computing for real-time traffic monitoring, offering a technical blueprint for revolutionizing urban management.

---

About the sponsor: Edge Impulse​

Edge Impulse is the leading development platform for embedded machine learning, used by over 1,000 enterprises across 200,000 ML projects worldwide. We are on a mission to enable the ultimate development experience for machine learning on embedded devices for sensors, audio, and computer vision, at scale.
From getting started in under five minutes to MLOps in production, we enable highly optimized ML deployable to a wide range of hardware from MCUs to CPUs, to custom AI accelerators. With Edge Impulse, developers, engineers, and domain experts solve real problems using machine learning in embedded solutions, speeding up development time from years to weeks. We specialize in industrial and professional applications including predictive maintenance, anomaly detection, human health, wearables, and more.

More by David Tischler​

FOLLOW
David is a Senior Developer Program Manager helping to take care of the Edge Impulse community of developers, and is a fan of computing on small, low power devices. He's also an extreme recycler, so use caution if trying to throw away recyclable objects if he's around.

https://www.wevolver.com/article/implementing-smart-voice-control-in-appliances-a-comprehensive-guide

Article #2
Sorry not sure if already posted before. Could this be a use case for Akida as uses an Arm Cortex M33.



The Nordic Thingy:53™ is an IoT prototyping platform that enables users to create prototypes and proofs of concept without the need for custom hardware. The Thingy:53 is built around the nRF5340 SoC, Nordic Semiconductor’s flagship dual-core wireless SoC. Its dual Arm Cortex-M33 processors provide ample processing power and memory size to run embedded machine learning (ML) models directly on the device with no constraints.

 

[skutza]

Lets hope that Patterns knows what he's talking about.....

 

[Xray1]

Last trading day today before BRN offically leave's the ASX200 index.

IMO..... Will be most interesting to see how the s/price performs today and how the shorter's and institutions handle the situation especially if they haven't already departed / cleared their holding books.

I personally would like to hope and see a .30 cents close today.

 

[Bravo]

" A new 16-core Neural Engine is capable of nearly 17 trillion operations per second, enabling even faster machine learning computations for features like Live Voicemail transcriptions in iOS 17 and third-party app experiences — all while protecting critical privacy and security features using the Secure Enclave."
Wouldn't it be nice......

Sorry to be a party-pooper but pretty sure Apple uses Qualcomm's Snapdragon.

 

[Schnitzel lover]

Lets hope that Patterns knows what he's talking about.....

View attachment 44636

Plus the quad ex witching. Its going to be a wild auction close today.

 

[jtardif999]

ARM up 25%

Arm climbs 25% in Nasdaq debut after pricing IPO at $51 a share

Arm Holdings has started trading on the Nasdaq under the ticker "ARM."

The chip design company is valued at a steep premium relative to the rest of the semiconductor market.

SoftBank still holds about 90% of Arm's stock.

Arm Holdings, the chip design company controlled by SoftBank, jumped nearly 25% during its first day of trading Thursday after selling shares at $51 a piece in its initial public offering.

At the open, Arm was valued at almost $60 billion. The company, trading under ticker symbol "ARM," sold about 95.5 million shares. SoftBank, which took the company private in 2016, controls about 90% of shares outstanding.

On Wednesday, Arm priced shares at the upper end of its expected range. On Thursday, the stock first traded at $56.10 and ended the day at $63.59.

It's a hefty premium for the British chip company. At a $60 billion valuation, Arm's price-to-earnings multiple would be over 110 based on the most recent fiscal year profit. That's comparable to Nvidia's valuation, which trades at 108 times earnings, but without Nvidia's 170% growth forecast for the current quarter.

Arm Chief Financial Officer Jason Child told CNBC in an interview that the company is focusing on royalty growth and providing products to its customers that cost and do more.

Many of Arm's royalties come from products released decades ago. About half the company's royalty revenue, which totaled $1.68 billion in 2022, comes from products released between 1990 and 2012.

"As a CFO, it's one of the better business models I've seen. I joke sometimes that those older products are like the Beatles catalog, they just keep delivering royalties. Some of those products are three decades old," Child said.

In a presentation to investors, Arm said it expects the total market for its chip designs to be worth about $250 billion by 2025, including growth in chip designs for data centers and cars. Arm's revenue in its fiscal year that ended in March slipped less than 1% from the prior year to $2.68 billion.

Arm's architecture is used in nearly every smartphone chip and outlines how a central processor works at its most basic level, such as doing arithmetic or accessing computer memory.

Child said the company sold $735 million in shares to a group of strategic investors comprising Apple, Google, Nvidia, Samsung, AMD, Intel, Cadence, Synopsis, Samsung and Taiwan Semiconductor Manufacturing Company. It's a testament to Arm's influence among chip companies, which rely on Arm's technology to design and build their own chips.

"There was interest to buy more than what was indicated, but we wanted to make sure we had a diverse set of shareholders," Child said.

In an interview with CNBC on Thursday, SoftBank CEO Masayoshi Son emphasized how Arm's technology is used in artificial intelligence chips, as he seeks to tie the firm to the recent boom in AI and machine learning. He also said he wanted to keep the company's remaining Arm stake as long as possible.

The debut could kick open the market for technology IPOs, which have been paused for nearly two years. It's the biggest technology offering of 2023.

 

[HopalongPetrovski]

Did anyone manage to get any shares in the ARM IPO?
Congrats if you did.
Might possibly be good timing from anywhere around about nowish for a follow up from one of the great enablers.....ahem BRAINCHIP!
Bring It, Buddy.

 

[Bravo]

PUBLISHED THU, SEP 14 202312:28 PM EDTUPDATED 6 HOURS AGO

SoftBank's Masayoshi Son says AI-fueled technology can surpass human intelligence 'big time'

AI is capable of helping solve the world's biggest problems and could potentially surpass the intelligence of humans, says SoftBank's Masayoshi Son.

www.cnbc.com

Extract

 

[Bravo]

SoftBank's Masayoshi Son Talks AI, Future At Arm's Historic IPO

Arm Holdings PLC (NASDAQ: ARM), known for its CPUs that Nvidia Corp.'s (NASDAQ: NVDA) GPUs rely on for artificial intelligence, took center stage Thursday with its IPO at a valuation of $54.5 billion,

capedge.com

Extract