BRN Discussion Ongoing

[Food4 thought]

Now do you think if some one bought an IP licence could it be intel
Lots of dots

 

[Food4 thought]

Seen this as I walked down the street in PhuQuoc Vietnam
It made me laugh a little

Must have Brainchip on my mind again

 

[Getupthere]

Edge AI: The Future of Artificial Intelligence in embedded systems — Embedded

Artificial Intelligence (AI) has revolutionized many industries, enabling applications that seemed unlikely just a few years ago. At the same time, the exponential growth of data and the need for real-time responses have led to the emergence of a new paradigm: edge AI.

apple.news

According to estimates, global spending on edge computing is expected to exceed $200 billion in 2024, up 15.4% from the previous year. Embedded devices like microcontrollers don’t have the computing power of a data center, but with advances in AI algorithm efficiency and specialized hardware, it’s now possible to run models on these devices. New chips designed specifically for edge AI, such as neural processing units (NPUs) integrated into microcontrollers, are making it increasingly possible to implement models in embedded systems.

 

[CHIPS]

Another application for Akida

x.com

x.com

 

[Tothemoon24]

With cloud computing now so ubiquitous, and edge computing so well established, why, then, is endpoint computing becoming so important?

In a word: Latency.

If we're going to make true advancements toward meaningful artificial intelligence (AI) and machine learning (ML) in smart homes, smart health and smart cities, lag and latency is unacceptable.

Efficient AI inference demands efficient endpoints that can infer, pre-process, and filter data in real time. Unlike computing in the cloud or at the edge of the cloud, the endpoint represents, as Renesas CEO Hidetoshi Shibata has said, "the true point of action." For example, a home appliance that can predict when it will need maintenance or a voice-interactive wearable that can warn a user of a possible heart anomaly is more useful if it applies ML algorithms at the endpoint with near-zero latency.

And it is precisely at the endpoint where Renesas microcontrollers (MCUs) shine. AI models, trained on industry-standard networks, are embedded on chip to offer design engineers the performance, bandwidth, and responsiveness to effectively realize and enhance emerging smart applications. Advanced endpoint-compiler software enables customers "to test capabilities purely in the cloud before porting them over to our boards, and then, finally, to implement these seamlessly on our chips," Mr. Shibata told McKinsey analysts recently.

"While also providing the flexibility to change the functionality or algorithms," he said, "Such dynamically configurable hardware architectures let customers enjoy the benefit of hardware-processing speeds."

Indeed, users of smart homes, smart health and smart cities applications cannot or will not wait even a few milliseconds for AI data to be processed. Consequently, the latency inherent in transferring data to the cloud threatens to undermine progress in those consumer-focused areas. This may sound obvious, but few competitive MCUs are designed to enable fast processing.

As ML inference moves to device endpoints, "this integrated AI will be the foundation that powers a complex combination of 'sense' technologies to create smart applications with more natural, 'human-like' communication and interaction," Dr. Sailesh Chittipeddi, president and CEO of Renesas Electronics America, recently wrote in Embedded Computing magazine.

"In addition," wrote Dr. Chittipeddi, "A convergence of advancements around AI accelerators, adaptive and predictive control, and hardware and software for voice and vision open up new user interface capabilities for a wide range of smart devices."

Let's take a look at what those might be in the smart home, health and city segments.

Smart Home Applications​

The intelligent home is in many ways a "sensor-rich" application. Smart homes will use sensors to collect and process all manner of data, from environmental information to user activity. With the guiding principle of providing "total convenience," these applications tend to use AI for predictive analytics and for customizing user interaction with the home environment.

For example, a refrigerator will learn over time to adjust its temperature settings. Or the TV will learn to change the audio output based on what's happening in the room at the time -- so voices and music are not deafened during parties or cooking. Voice interfaces can also be used to teach or train devices, as well as to provide information when an appliance is offline.

Smart Health Applications​

Smart health applications will be even more demanding when it comes to latency. A pacemaker that can predict when a battery needs to be changed could well save lives. An electrocardiogram (ECG) sensor that can warn a user of a heart anomaly in real time could save lives as well. ML algorithms embedded in an ECG sensor could detect disruptions in heartbeat or rhythm and sound the alarm immediately.

AI algorithms are also being used to predict when an insulin pump is likely to run out of a patient's specific dosage and to anticipate when a dose is needed. Otherwise, the pump might not deliver the right dosage at the right time, putting patients at risk.

Smart City Applications​

In a smart city, sensors can collect all manner of information -- from pollution levels to traffic delays -- that could be used to forecast traffic conditions and even determine air quality alerts.

Smart city applications will be equally demanding of low latency. A smart streetlamp that can detect traffic density and adjust its brightness accordingly to prevent traffic accidents could save lives. An AI and ML-infused surveillance camera that can detect alterations in traffic flow, which may mean an accident has occurred and can alert emergency services immediately could also save lives.

In scenarios like city traffic, drivers would benefit from AI alerts that warn of slowdowns ahead. Smart parking garages could use AI algorithms to monitor a car's status and accurately charge or discharge a vehicle depending on a user's preferred levels of privacy or security.

In terms of cities and traffic control, AI algorithms embedded in smart traffic lights could similarly be able to notify drivers, pedestrians and bikers of slowing or stopping traffic ahead. Once again, the information would be relayed in near-zero latency. Smart vehicle routing -- say, to avoid heavy congestion -- may be the most challenging application of all.

Flexibility is Key​

As ML algorithms come to dominate smart home, health and city applications, the speed at which data is processed will be more critical than ever. For companies like Renesas, that means delivering the most intelligent and flexible MCUs that optimize data processing speed while also providing the flexibility to change functionality or algorithms.

The same MCU that enables fast processing today may support a completely different algorithm tomorrow. That makes the MCU platform, which includes a flexible CPU, an ideal choice for enabling endpoint ML applications.

Find the right MCU with this easy-to-use MCU selection tool, or download the Renesas MCU guide app and move your designs to the endpoint today!

 

[itsol4605]

View attachment 69290






With cloud computing now so ubiquitous, and edge computing so well established, why, then, is endpoint computing becoming so important?

In a word: Latency.

If we're going to make true advancements toward meaningful artificial intelligence (AI) and machine learning (ML) in smart homes, smart health and smart cities, lag and latency is unacceptable.

Efficient AI inference demands efficient endpoints that can infer, pre-process, and filter data in real time. Unlike computing in the cloud or at the edge of the cloud, the endpoint represents, as Renesas CEO Hidetoshi Shibata has said, "the true point of action." For example, a home appliance that can predict when it will need maintenance or a voice-interactive wearable that can warn a user of a possible heart anomaly is more useful if it applies ML algorithms at the endpoint with near-zero latency.

And it is precisely at the endpoint where Renesas microcontrollers (MCUs) shine. AI models, trained on industry-standard networks, are embedded on chip to offer design engineers the performance, bandwidth, and responsiveness to effectively realize and enhance emerging smart applications. Advanced endpoint-compiler software enables customers "to test capabilities purely in the cloud before porting them over to our boards, and then, finally, to implement these seamlessly on our chips," Mr. Shibata told McKinsey analysts recently.

"While also providing the flexibility to change the functionality or algorithms," he said, "Such dynamically configurable hardware architectures let customers enjoy the benefit of hardware-processing speeds."

Indeed, users of smart homes, smart health and smart cities applications cannot or will not wait even a few milliseconds for AI data to be processed. Consequently, the latency inherent in transferring data to the cloud threatens to undermine progress in those consumer-focused areas. This may sound obvious, but few competitive MCUs are designed to enable fast processing.

As ML inference moves to device endpoints, "this integrated AI will be the foundation that powers a complex combination of 'sense' technologies to create smart applications with more natural, 'human-like' communication and interaction," Dr. Sailesh Chittipeddi, president and CEO of Renesas Electronics America, recently wrote in Embedded Computing magazine.

"In addition," wrote Dr. Chittipeddi, "A convergence of advancements around AI accelerators, adaptive and predictive control, and hardware and software for voice and vision open up new user interface capabilities for a wide range of smart devices."

Let's take a look at what those might be in the smart home, health and city segments.

Smart Home Applications​

The intelligent home is in many ways a "sensor-rich" application. Smart homes will use sensors to collect and process all manner of data, from environmental information to user activity. With the guiding principle of providing "total convenience," these applications tend to use AI for predictive analytics and for customizing user interaction with the home environment.

For example, a refrigerator will learn over time to adjust its temperature settings. Or the TV will learn to change the audio output based on what's happening in the room at the time -- so voices and music are not deafened during parties or cooking. Voice interfaces can also be used to teach or train devices, as well as to provide information when an appliance is offline.

Smart Health Applications​

Smart health applications will be even more demanding when it comes to latency. A pacemaker that can predict when a battery needs to be changed could well save lives. An electrocardiogram (ECG) sensor that can warn a user of a heart anomaly in real time could save lives as well. ML algorithms embedded in an ECG sensor could detect disruptions in heartbeat or rhythm and sound the alarm immediately.

AI algorithms are also being used to predict when an insulin pump is likely to run out of a patient's specific dosage and to anticipate when a dose is needed. Otherwise, the pump might not deliver the right dosage at the right time, putting patients at risk.

Smart City Applications​

In a smart city, sensors can collect all manner of information -- from pollution levels to traffic delays -- that could be used to forecast traffic conditions and even determine air quality alerts.

Smart city applications will be equally demanding of low latency. A smart streetlamp that can detect traffic density and adjust its brightness accordingly to prevent traffic accidents could save lives. An AI and ML-infused surveillance camera that can detect alterations in traffic flow, which may mean an accident has occurred and can alert emergency services immediately could also save lives.

In scenarios like city traffic, drivers would benefit from AI alerts that warn of slowdowns ahead. Smart parking garages could use AI algorithms to monitor a car's status and accurately charge or discharge a vehicle depending on a user's preferred levels of privacy or security.

In terms of cities and traffic control, AI algorithms embedded in smart traffic lights could similarly be able to notify drivers, pedestrians and bikers of slowing or stopping traffic ahead. Once again, the information would be relayed in near-zero latency. Smart vehicle routing -- say, to avoid heavy congestion -- may be the most challenging application of all.

Flexibility is Key​

As ML algorithms come to dominate smart home, health and city applications, the speed at which data is processed will be more critical than ever. For companies like Renesas, that means delivering the most intelligent and flexible MCUs that optimize data processing speed while also providing the flexibility to change functionality or algorithms.

The same MCU that enables fast processing today may support a completely different algorithm tomorrow. That makes the MCU platform, which includes a flexible CPU, an ideal choice for enabling endpoint ML applications.

Find the right MCU with this easy-to-use MCU selection tool, or download the Renesas MCU guide app and move your designs to the endpoint today!

It remains to be seen whether and what role Brainchip Akida will play in Renesas' MCU in particular.
I believe that Tata plays a much more important role for Brainchip Akida than Renesas.

 

[rgupta]

This was posted on Bloomberg

Intel Corp. has officially qualified for as much as $3.5 billion in federal grants to make semiconductors for the Pentagon, according to people familiar with the matter, after the chipmaker reached a binding agreement with US officials.

Hopefully our d day is not much behind now. 2024 should be end of our long wait.
Having hope here.

 

[7für7]

Seen this as I walked down the street in PhuQuoc Vietnam
It made me laugh a little

Must have Brainchip on my mind again

Interesting they use Greek letters

 

[Smoothsailing]

Go brainchip

 

[Bravo]

Video published by TCS (Tata Consulting Services) 2 days ago.

Neural manufacturing????

 

[Bravo]

I'll be a monkey's uncle if this doesn't end up involving us in some way.

Tata electronics, TCS to develop India's first domestic chips by 2026​

Tata Electronics plans to launch its first chips from the Assam facility by late 2025 or early 2026, targeting sectors like automotive, electronics, power, consumer goods, and healthcare​

Rimjhim Singh New Delhi
2 min read Last Updated : Sep 13 2024 | 10:27 AM IST



Tata Consultancy Services (TCS) is collaborating with Tata Electronics Pvt Ltd, as the conglomerate aims to introduce India’s first domestically produced chips by 2026, according to a senior executive from TCS, as reported by Mint.
The news report quoted Sreenivasa Chakravarti, vice-president and global head of TCS’s digital engineering division, as saying that TCS, which specialises in semiconductor design and engineering for its clients, has multiple points of engagement in the chip manufacturing process, where Tata Electronics is leading the charge.

He also highlighted other shared services, including software solutions and intellectual property (IP)-based products for semiconductors.

Three semiconductor facilities​

In February, the India Semiconductor Mission approved the establishment of three semiconductor manufacturing facilities to advance this sector. Tata Electronics is leading the development of two of these projects. One is an $11-billion greenfield chip manufacturing plant in collaboration with Taiwan's Powerchip Semiconductor (PSMC) in Dholera, Gujarat, which will have an initial output capacity of 50,000 wafers per month. The second is a $3.26-billion facility in Assam, focused on chip assembly and testing, the report said.
Tata Electronics aims to have the first chips from the Assam facility available by late 2025 or early 2026, targetting industries such as automotive, electronics, power, consumer goods, and healthcare.

Garena Free Fire MAX redeem codes for September 13: Win in-game goodies and rewards | - Times of India

GAMING News: Garena Free Fire MAX has gained popularity among Indian gamers following the ban on Garena Free Fire. The game features impressive graphics and engagi

timesofindia.indiatimes.com

 

[hotty4040]

I'll be a monkey's uncle if this doesn't end up involving us in some way.


View attachment 69297

View attachment 69300

Tata electronics, TCS to develop India's first domestic chips by 2026​

Tata Electronics plans to launch its first chips from the Assam facility by late 2025 or early 2026, targeting sectors like automotive, electronics, power, consumer goods, and healthcare​

Rimjhim Singh New Delhi
2 min read Last Updated : Sep 13 2024 | 10:27 AM IST



Tata Consultancy Services (TCS) is collaborating with Tata Electronics Pvt Ltd, as the conglomerate aims to introduce India’s first domestically produced chips by 2026, according to a senior executive from TCS, as reported by Mint.
The news report quoted Sreenivasa Chakravarti, vice-president and global head of TCS’s digital engineering division, as saying that TCS, which specialises in semiconductor design and engineering for its clients, has multiple points of engagement in the chip manufacturing process, where Tata Electronics is leading the charge.

He also highlighted other shared services, including software solutions and intellectual property (IP)-based products for semiconductors.

Three semiconductor facilities​

In February, the India Semiconductor Mission approved the establishment of three semiconductor manufacturing facilities to advance this sector. Tata Electronics is leading the development of two of these projects. One is an $11-billion greenfield chip manufacturing plant in collaboration with Taiwan's Powerchip Semiconductor (PSMC) in Dholera, Gujarat, which will have an initial output capacity of 50,000 wafers per month. The second is a $3.26-billion facility in Assam, focused on chip assembly and testing, the report said.
Tata Electronics aims to have the first chips from the Assam facility available by late 2025 or early 2026, targetting industries such as automotive, electronics, power, consumer goods, and healthcare.

Garena Free Fire MAX redeem codes for September 13: Win in-game goodies and rewards | - Times of India

GAMING News: Garena Free Fire MAX has gained popularity among Indian gamers following the ban on Garena Free Fire. The game features impressive graphics and engagi

timesofindia.indiatimes.com

Excellent input from you today Bravo, much food for thought, for my ever diminishing neuron's to digest, and fathom - IMHO..

Brainchip, will/must, surely be in the thick edge of all these developments, that are on the near-term horizons' for us all, to savour, and will be our saviour, eventually, we hope. Go TATA.


Akida Ballista >>>>>> I'm still excited, I just can't help it <<<<<<


hotty...

 

[Getupthere]

In-car ads coming to new vehicles — news.com.au

Manufacturers are set to monetise new cars with in-vehicle connectivity and advertising that could drive you crazy.

apple.news

Ford made headlines recently when it took out a patent for in-car advertising that included the ability to eavesdrop on passengers and tailor content to their conversations.

Including the ability to “understand the user’s tolerance for a particular advertisement’s count”, or in other words, listen out for people saying things like “if I hear one more ad for this petrol company, I’ll buy 10 litres from their arch rival and use it to set this car on fire!”

This isn’t fiction.

 

[Bravo]

Great to see Tata & Brainchip ️

View attachment 69259 View attachment 69262

So, a coupla days ago we have TCS demoing our technology at Semicon India.

Then 20 hours ago, we have an announcement that TCS is teaming up with Tata Electronics to develop India's first domestic chips by 2026.

This is all tying in VERY nicely with the below article IMO.

Methinks TATA will likely be the next cab off the rank to get a licence to meet the 2026 deadline. Five-year licence looking like a real possibility IMO.

A Tata-Tesla deal has just won India a big strategic edge

Tata's likely deal with Tesla to supply semiconductor chips marks a major milestone for India, enhancing its strategic power globally. As countries invest in semiconductor manufacturing, India's focus on legacy chips positions it as a reliable supplier in the global chip value chain...

economictimes.indiatimes.com

 

[Diogenese]

So, a coupla days ago we have TCS demoing our technology at Semicon India.

Then 20 hours ago, we have an announcement that TCS is teaming up with Tata Electronics to develop India's first domestic chips by 2026.

This is all tying in VERY nicely with the below article IMO.

Methinks TATA will likely be the next cab off the rank to get a licence to meet the 2026 deadline. Five-year licence looking like a real possibility IMO.

View attachment 69306

A Tata-Tesla deal has just won India a big strategic edge

Tata's likely deal with Tesla to supply semiconductor chips marks a major milestone for India, enhancing its strategic power globally. As countries invest in semiconductor manufacturing, India's focus on legacy chips positions it as a reliable supplier in the global chip value chain...

economictimes.indiatimes.com

Hi Bravo,

TATA could obtain an ARM processor licence, for example. There is also the possibility of a SiFive licence ...

If they are (undoubtedly) going to include Akida/TeNNs in their processor, they will need a licence before they begin seriously designing the chip, although there are pre-existing proofs of compatibility, so the actual design may not take too long.

So have a look in your letter box on the odd day when Australia Post deigns to deliver letters for notice of the licence.

 

[IloveLamp]

New brain-on-a-chip platform to deliver 460x efficiency boost for AI tasks

Developed by the Indian Institute of Science, the neuromorphic computing platform is designed to work alongside existing AI hardware, rather than replace it.

www-networkworld-com.cdn.ampproject.org

Sign Up | LinkedIn

500 million+ members | Manage your professional identity. Build and engage with your professional network. Access knowledge, insights and opportunities.

www.linkedin.com

 

[Diogenese]

New brain-on-a-chip platform to deliver 460x efficiency boost for AI tasks

Developed by the Indian Institute of Science, the neuromorphic computing platform is designed to work alongside existing AI hardware, rather than replace it.

www-networkworld-com.cdn.ampproject.org

Sign Up | LinkedIn

500 million+ members | Manage your professional identity. Build and engage with your professional network. Access knowledge, insights and opportunities.

www.linkedin.com

View attachment 69310 View attachment 69311

Look!
Up there! ...
Is it a bird?...
Is it a plane? ...
No! It's a ...
pie.

 

[Diogenese]

New brain-on-a-chip platform to deliver 460x efficiency boost for AI tasks

Developed by the Indian Institute of Science, the neuromorphic computing platform is designed to work alongside existing AI hardware, rather than replace it.

www-networkworld-com.cdn.ampproject.org

Sign Up | LinkedIn

500 million+ members | Manage your professional identity. Build and engage with your professional network. Access knowledge, insights and opportunities.

www.linkedin.com

View attachment 69310 View attachment 69311

Hi ILL,

I can only find one patent naming S Goswami as inventor:

US10984859B2 Resistive memory devices based on metal coordinated redox active ligands 20170928 AZOMETRIX [US]

As you can see, it was filed by a US company in 2017. It relates to analog computing.

Maybe the Indian Institute of Science have a pending application within the last 18 months which has not yet been published.

 

[rgupta]

Eric Feuilleaubois (Ph.D) on LinkedIn: Comprehensive comparison between vision transformers and convolutional…

Comprehensive comparison between vision transformers and convolutional neural networks for face recognition tasks - Scientific Reports #DL #AI #ML…

www.linkedin.com

CNN VS ViTs
Looks like of late brainchip is comparing it's technology with existing technologies and shows the benefit.
Dyor

 

[Guzzi62]

Eric Feuilleaubois (Ph.D) on LinkedIn: Comprehensive comparison between vision transformers and convolutional…

Comprehensive comparison between vision transformers and convolutional neural networks for face recognition tasks - Scientific Reports #DL #AI #ML…

www.linkedin.com

CNN VS ViTs
Looks like of late brainchip is comparing it's technology with existing technologies and shows the benefit.
Dyor

That paper isn't written by BrainChip, why do you think that?

ViT seems best for face recognition tasks, they write in conclusion.

It's written by:

Authors and Affiliations​

1. Grupo de Tratamiento de Imágenes (GTI), Information Processing and Telecommunications Center (IPTC), Universidad Politécnica de Madrid (UPM), Madrid, 28040, Spain
   Marcos Rodrigo, Carlos Cuevas & Narciso García

Comprehensive comparison between vision transformers and convolutional neural networks for face recognition tasks - Scientific Reports

This paper presents a comprehensive comparison between Vision Transformers and Convolutional Neural Networks for face recognition related tasks, including extensive experiments on the tasks of face identification and verification. Our study focuses on six state-of-the-art models: EfficientNet...

www.nature.com