BRN Discussion Ongoing

[Perhaps]

I don't get the point of that everlasting Qualcomm mumbo-jumbo.

Qualcomms first neuromorphic processor Zeroth appeared 2013:

Introducing Qualcomm Zeroth Processors: Brain-Inspired Computing | Qualcomm

For the past few years our Research and Development teams have been working on a new computer architecture that breaks the traditional mold...

www.qualcomm.com

In products available since 2015/16:

Zeroth (software) - Wikipedia

en.wikipedia.org

Qualcomm never did any marketing using the word neuromorphic, a look at their patent portfolio helps:

Search Patents - Justia Patents Search

patents.justia.com

Since the beginning I file Qualcomm under the biggest competitors. I can hear it coming, but...but... Akida is better. Who cares. Qualcomm has an inhouse solution for many years in use. Why should they integrate a costly IP from Brainchip, when they can do it on their own?
At the end it's all about business and profits, not about olympics counting neurons and synapses.

By the way, the IP business model is meant for the big players. These days are not comparable with, let's say ARM 1993. Today's big players usually run their own IP business. They are not famous for licensing IPs from small companies. When there is a new technology they need, they usually fix it with an acquisition, like Renesas did last year with Reality AI.
A look at the Qualcomm history of acquisitions shows that's the usual way:

List of 50 Acquisitions by Qualcomm (Apr 2025) - Tracxn

Qualcomm has made 50 acquisitions across sectors such as Analog and Mixed Signal ICs, Internet of Things Infrastructure and others. Edge Impulse, Foundries.io and Skyhook are its latest acquisitions.

tracxn.com

So for a small company selling IP is a risky business with no guarantee of success. The way of partnerships seem to work better than just selling IP to the big ones, but it needs some more time for financial results.

 

[DerAktienDude]

I don't get the point of that everlasting Qualcomm mumbo-jumbo.

Qualcomms first neuromorphic processor Zeroth appeared 2013:

Introducing Qualcomm Zeroth Processors: Brain-Inspired Computing | Qualcomm

For the past few years our Research and Development teams have been working on a new computer architecture that breaks the traditional mold...

www.qualcomm.com

In products available since 2015/16:

Zeroth (software) - Wikipedia

en.wikipedia.org

Qualcomm never did any marketing using the word neuromorphic, a look at their patent portfolio helps:

Search Patents - Justia Patents Search

patents.justia.com

Since the beginning I file Qualcomm under the biggest competitors. I can hear it coming, but...but... Akida is better. Who cares. Qualcomm has an inhouse solution for many years in use. Why should they integrate a costly IP from Brainchip, when they can do it on their own?
At the end it's all about business and profits, not about olympics counting neurons and synapses.

Stop being realistic in this forum. It's not worth it.

 

[Ian]

Trying to find info on this NEURON product from Elxsi

 

[Justchilln]

I don't get the point of that everlasting Qualcomm mumbo-jumbo.

Qualcomms first neuromorphic processor Zeroth appeared 2013:

Introducing Qualcomm Zeroth Processors: Brain-Inspired Computing | Qualcomm

For the past few years our Research and Development teams have been working on a new computer architecture that breaks the traditional mold...

www.qualcomm.com

In products available since 2015/16:

Zeroth (software) - Wikipedia

en.wikipedia.org

Qualcomm never did any marketing using the word neuromorphic, a look at their patent portfolio helps:

Search Patents - Justia Patents Search

patents.justia.com

Since the beginning I file Qualcomm under the biggest competitors. I can hear it coming, but...but... Akida is better. Who cares. Qualcomm has an inhouse solution for many years in use. Why should they integrate a costly IP from Brainchip, when they can do it on their own?
At the end it's all about business and profits, not about olympics counting neurons and synapses.

By the way, the IP business model is meant for the big players. These days are not comparable with, let's say ARM 1993. Today's big players usually run their own IP business. They are not famous for licensing IPs from small companies. When there is a new technology they need, they usually fix it with an acquisition, like Renesas did last year with Reality AI.
A look at the Qualcomm history of acquisitions shows that's the usual way:

List of 50 Acquisitions by Qualcomm (Apr 2025) - Tracxn

Qualcomm has made 50 acquisitions across sectors such as Analog and Mixed Signal ICs, Internet of Things Infrastructure and others. Edge Impulse, Foundries.io and Skyhook are its latest acquisitions.

tracxn.com

So for a small company selling IP is a risky business with no guarantee of success. The way of partnerships seem to work better than just selling IP to the big ones, but it needs some more time for financial results.

Pretty sure that Qualcomm zeroth is no longer around mate…..

 

[The Pope]

Rob Telson (Batman) likes this one (night vision mobile activity) for the dot joiners wishing to investigate further

#team #nightvision #mobile #snapdragonsummit | Oren Debbi | 22 comments

While Israel is going through one of the most difficult periods in history, and I'm out on the field during reserve duty, I'm proud of our #team who worked hard together with our partners Qualcomm to launch #Nightvision for #mobile yesterday... 🚀 What was showcased at #SnapdragonSummit in...

www.linkedin.com

 

[cosors]

I don't get the point of that everlasting Qualcomm mumbo-jumbo.

Qualcomms first neuromorphic processor Zeroth appeared 2013:

Introducing Qualcomm Zeroth Processors: Brain-Inspired Computing | Qualcomm

For the past few years our Research and Development teams have been working on a new computer architecture that breaks the traditional mold...

www.qualcomm.com

In products available since 2015/16:

Zeroth (software) - Wikipedia

en.wikipedia.org

Qualcomm never did any marketing using the word neuromorphic, a look at their patent portfolio helps:

Search Patents - Justia Patents Search

patents.justia.com

Since the beginning I file Qualcomm under the biggest competitors. I can hear it coming, but...but... Akida is better. Who cares. Qualcomm has an inhouse solution for many years in use. Why should they integrate a costly IP from Brainchip, when they can do it on their own?
At the end it's all about business and profits, not about olympics counting neurons and synapses.

By the way, the IP business model is meant for the big players. These days are not comparable with, let's say ARM 1993. Today's big players usually run their own IP business. They are not famous for licensing IPs from small companies. When there is a new technology they need, they usually fix it with an acquisition, like Renesas did last year with Reality AI.
A look at the Qualcomm history of acquisitions shows that's the usual way:

List of 50 Acquisitions by Qualcomm (Apr 2025) - Tracxn

Qualcomm has made 50 acquisitions across sectors such as Analog and Mixed Signal ICs, Internet of Things Infrastructure and others. Edge Impulse, Foundries.io and Skyhook are its latest acquisitions.

tracxn.com

So for a small company selling IP is a risky business with no guarantee of success. The way of partnerships seem to work better than just selling IP to the big ones, but it needs some more time for financial results.

Qualcomm never did any marketing using the word neuromorphic, a look at their patent portfolio helps:

but
"...
Our unique approach to accelerating complex AI models is by breaking down neural networks into micro tiles to speed up the inferencing process.
..."
https://thestockexchange.com.au/threads/tlg-discussion-2022.7072/post-389769

but Dio:
Probably more that it will affect Talga. (That's another "F" for multitasking for me)

 

[Gies]

AI Powered Automotive whitepaper | Markus May

this is going to be big🫣... pay attention to the comments! NVISO BrainChip

www.linkedin.com

I don’t understand why Markus is so excited about this news of 1 year old?
What do I miss?
The alliance with Brainchip is pretty clear

 

[Getupthere]

NVIDIA Brings The Power Of Generative AI To The Edge — Forbes

NVIDIA wants to turn the Jetson family of devices into powerful edge computing devices capable of running state-of-the-art foundation models.

apple.news

 

[charles2]

BRCHF traded a grand total of 1699 shares today.

Volume can dry up before a big move. Realistically though, ASX is the tail that wags the dog.

That may have to change before a serious appreciation of Brainchip's
value can be expected.

 

[charles2]

Wonder of wonders. INTC revenue guidance SOARS. Must be a good thing.

Intel (NASDAQ:INTC) Beats Expectations in Strong Q3, Stock Soars

Computer processor maker Intel (NASDAQ:INTC) reported Q3 FY2023 results topping analysts' expectations, with revenue down 7.69% year on year to $14.2 billion. On top of that, next quarter's revenue guidance ($15.1 billion at the midpoint) was surprisingly good and 5.26% above what analysts were...

finance.yahoo.com

 

[Frank Zappa]

Wonder of wonders. INTC revenue guidance SOARS. Must be a good thing.

Intel (NASDAQ:INTC) Beats Expectations in Strong Q3, Stock Soars

Computer processor maker Intel (NASDAQ:INTC) reported Q3 FY2023 results topping analysts' expectations, with revenue down 7.69% year on year to $14.2 billion. On top of that, next quarter's revenue guidance ($15.1 billion at the midpoint) was surprisingly good and 5.26% above what analysts were...

finance.yahoo.com

Linkin has a recent Brain chip post on Ai at the edge and its cost to get it up and going . Worth a read

 

[Tothemoon24]

SiFive Rolls Out RISC-V Cores Aimed at Generative AI and ML​

October 2023 by Jake Hertz

SiFive has released two new processors, one to target machine learning applications, and one to target general-purpose HPC.​

The RISC-V movement is one of the hottest things in the computing industry at the moment, and arguably no company is more synonymous with RISC-V than Si-Five. With a long history of producing industry-leading RISC-V processors, Si-Five has shown no signs of slowing down yet.

SiFive has released new processors to target machine learning and general-purpose HPC. ​

Last week, SiFive continued its momentum with yet another set of launches: one new processor to target high-performance general-purpose computing, and another to target machine learning and artificial intelligence tasks. In this piece, we’ll take a look at the two new offerings from Si-Five to see what the company is bringing to the table.

P870/P870A: General-Purpose High Performance SoC​

The first of the SiFive releases last week was the Performance P870/P870A processors. Engineered for consumer applications and data centers, the P870 features a number of architectural innovations that make it a formidable processor.
First, the device is built around a 64-bit six-wide out-of-order core architecture that adheres to the RVA 23 standard, while also featuring a shared cluster cache that enables configurations of up to a 32-core cluster.
This is further complemented by augmented Arithmetic Logic Units (ALUs) and additional branch units as compared to previous offerings. It also incorporates advanced features like 128b VLEN RVV, vector crypto and hypervisor extensions, IOMMU, and a non-inclusive L3 cache.

SiFive‘s P870/P870A embeds more features than its predecessors.​

Together, these architectural enhancements enable high throughput and low latency for the processor, making it ideal for applications that demand rapid data processing and real-time analytics.
The device is said to achieve a 50% peak single-thread performance upgrade over its predecessor, as measured by the specINT2k6 benchmark, while also offering improvements in execution throughput, facilitated by an increased number of instruction sets per cycle
The P870A variant, specifically designed for automotive applications, shares the core architecture with the P870 but is likely to include additional features tailored for the automotive industry, although detailed specifications are yet to be released.

Intelligence X390 Boosts Vector Computation​

The second major release from SiFive last week was the Intelligence X390 processor, designed to meet the escalating demands of AI and ML applications. The X390 builds upon the architectural foundation laid by its predecessor, the X280, but introduces several key enhancements that significantly boost its computational capabilities.
At the core of the X390 is a single-core configuration that offers a fourfold improvement in vector computation. This is achieved through a dual vector ALU setup and a doubled vector length, which collectively contribute to a quadruple increase in sustained data bandwidth.
The processor also incorporates SiFive's VCIX technology, allowing companies to add custom vector instructions or acceleration hardware, thereby offering unprecedented flexibility in performance optimization.
The processor's enhanced vector computation capabilities make it particularly well-suited for neural network training and inference tasks, which often require high computational power. Moreover, the X390's architecture is designed to facilitate a sustained data bandwidth increase, a critical factor for AI/ML applications that require rapid data ingestion and processing.
Performance metrics indicate that the X390 is a powerhouse in its category. It offers a quadruple increase in sustained data bandwidth due to architecture improvements and data width increases.

Pushing RISC-V Forward​

SiFive is playing a pivotal role in propelling the RISC-V industry into new frontiers of performance and applicability. By unveiling processors like the Performance P870/P870A and Intelligence X390, the company is not merely iterating on existing technology but is introducing transformative architectural innovations.
Moreover, by setting new performance standards and expanding the scope of RISC-V's applicability, SiFive is accelerating the industry's move towards more efficient, flexible, and powerful computing architectures.

 

[supersonic001]

The Synergy of Artificial Intelligence and the Internet of Things (IoT) -… | TEAL

TEAL’s “Question of the Week”: What is AIoT? IoT + AI = 🔥! Check out our latest blog post to learn more about this powerful synergy and the benefits that #AIoT can bring to your business. Read here 👉 https://lnkd.in/djwx2FKJ #AI #IoT #Connectivity #eSIM

www.linkedin.com

 

[Labsy]

Small power footprint is a necessity moving forward....

"Swart tells me that the Snapdragon AR2 Gen 1 chipset was designed for devices that will send only around 1 Watt of power or less to the CPU.

Compare that to today’s smartphones. The Snapdragon 8 Gen 2 chipset can draw almost 15 Watts of power. Mobile chipsets in laptops and tablets can draw 25 Watts or more. An Intel i9 processor draws 45 Watts. Qualcomm started with the idea that wearable computers will only consume 1 Watt of power for processing, and maybe everything on board."

Exclusive – Up close with Qualcomm Snapdragon AR2 smart glasses and why it's taking so long to perfect them

A look at the first Snapdragon AR 2 Gen 1 device

www.techradar.com

 

[Esq.111]

Morning Chippers ,

Just listening to the AVA Risk Group AGM from yesterday....

This company specialise in high security locks , perimeter detection over high value assets.

At the 22.40 mark mentions neural networks .... interesting co and tech.... think Akida would work well in their products.

* Disclosure , i hold a few of these shares.

Pinched from elsewhere so hope it works

https://hotcrapper.com.au/posts/70564487/single

sorry chaps am unable to transfer video .....useless....dam it

From the orifice,
Company , AVA
Poster fcmaster26,
Posted yesterday at 22:10
AGM Video.


Regards,
Esq

 

[Vladsblood]

Afternoon Chippers ,

Ray of sunshine , the US Government has elected a new House Speaker which should help alleviate some wobbles in the global markets a little.

American spending can resume.

Regards,
Esq

Morning Esq
Dunno about any good coming from the Socialist American Government Esq.

Socialist Governments stick their noses into Publicly listed Companies such as biden’s has just Ordered Nvidia to stop exporting chips to China . LOL…This is a Democracy???
Nope definitely not so!!!
Vlad

 

[Bravo]

NVIDIA Brings The Power Of Generative AI To The Edge — Forbes

NVIDIA wants to turn the Jetson family of devices into powerful edge computing devices capable of running state-of-the-art foundation models.

apple.news

Nice find @Getupthere,

NVIDIA wants to turn the Jetson family of devices into powerful edge computing devices capable of running state-of-the-art foundation models.

And the article mentions Edge Impulse's TAO toolkit!

Let's hope this opens up the number of Edge Impulse partners needing an AI accelerator (i.e BrainChip IP) to work with their NVIDIA model for lower power consumption and higher efficiency.

Extract from article

NVIDIA Brings The Power Of Generative AI To The Edge

NVIDIA wants to turn the Jetson family of devices into powerful edge computing devices capable of running state-of-the-art foundation models.

www.forbes.com

Edge Impulse Slide

 

[7für7]

Did Brainchip already respond to the email that was sent yesterday? Asking for a friend!

 

[Ian]

https://www.eetimes.com/neural-networks-can-help-keep-connected-vehicles-secure/

 

[Damo4]

Not specifically Brainchip related, but a good reminder of why Neural Networks are changing the world.
This example is more about replicating human thought, rather than rapid, low power, edge analysis of data sets.
2030 seems to be the goal for Artificial General Intelligence, still a lot on offer for NN to improve.

Link to the Article:
In a 1st, AI neural network captures 'critical aspect of human intelligence'

Link to the study:
SupplementaryInformation: Human-likesystematicgeneralizationthrougha meta-learningneuralnetwork


Snippet from the link, describing the process:

Neural networks somewhat mimic the human brain's structure because their information-processing nodes are linked to one another, and their data processing flows in hierarchical layers. But historically the AI systems haven't behaved like the human mind because they lacked the ability to combine known concepts in new ways — a capacity called "systematic compositionality."

For example, Lake explained, if a standard neural network learns the words "hop," "twice" and "in a circle," it needs to be shown many examples of how those words can be combined into meaningful phrases, such as "hop twice" and "hop in a circle." But if the system is then fed a new word, such as "spin," it would again need to see a bunch of examples to learn how to use it similarly.

In the new study, Lake and study co-author Marco Baroni of Pompeu Fabra University in Barcelona tested both AI models and human volunteers using a made-up language with words like "dax" and "wif." These words either corresponded with colored dots, or with a function that somehow manipulated those dots' order in a sequence. Thus, the word sequences determined the order in which the colored dots appeared.

So given a nonsensical phrase, the AI and humans had to figure out the underlying "grammar rules" that determined which dots went with the words.

The human participants produced the correct dot sequences about 80% of the time. When they failed, they made consistent types of errors, such as thinking a word represented a single dot rather than a function that shuffled the whole dot sequence.

After testing seven AI models, Lake and Baroni landed on a method, called meta-learning for compositionality (MLC), that lets a neural network practice applying different sets of rules to the newly learned words, while also giving feedback on whether it applied the rules correctly.

The MLC-trained neural network matched or exceeded the humans' performance on these tests. And when the researchers added data on the humans' common mistakes, the AI model then made the same types of mistakes as people did.