BRN Discussion Ongoing

[Fact Finder]

An interesting place for an article about SiFive and Brainchip. It is from last year:


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May 2, 2022

BrainChip Advances AI and ML to Edge Computing​

Credit: spainter_vfx/Shutterstock

Concept:
American technology startup BrainChip and semiconductor startup SiFive have partnered to combine their technologies to offer chip designers optimized AI and ML for edge computing. BrainChip’s Akida technology and SiFive’s multi-core capable RISC-V processors have been combined to create a highly efficient solution for integrated edge AI computation.

Nature of Disruption:
With high performance, ultra-low power, and on-chip learning, BrainChip’s Akida is an advanced neural networking processor architecture that takes AI to the edge. SiFive Intelligence solutions combine software and hardware to accelerate AI or ML applications with its highly configurable multi-core, multi-cluster capable design. For AI and ML workloads, SiFive Intelligence-based processors can provide industry-leading performance and efficiency. The highly programmable multi-core, multi-cluster capable design can be used for a range of applications requiring high-throughput, single-thread performance while operating within the most stringent power and area limitations. Akida acts like a human brain, analyzing only the most important sensor inputs at the time of acquisition and processing data with unmatched efficiency, precision, and energy efficiency. BrainChip’s technology is based on its SNAP (spiking neuron adaptive processor) technology, which it licenses to other companies. RISC-V is an open instruction-set computing architecture based on well-known RISC ideas. It provides the high data processing speed that all new and heavier applications require.

Outlook:
The duo aims to help companies looking to seamlessly integrate an optimized processor with dedicated ML accelerators, which are required for the demanding requirements of edge AI computing. They plan to use Akida, BrainChip’s specialized, differentiated AI engine, in conjunction with high-performance RISC-V processors like the SiFive Intelligence Series to achieve this. For organizations looking to enter the neuromorphic semiconductor chip market, SNAP provides a development option. It is a key feature of neuromorphic semiconductor circuits that allows for a variety of applications, including cybersecurity, gaming, robotics, and stock market forecasting

My opinion only DYOR
FF

AKIDA BALLISTA

 

[Realinfo]

Whose Zoomin’ Who ??

Me myself personally thought people here (especially those who boarded the bus in the last 15 months) might like a run through of some of the more interesting ASX announcements between 2018 and 2020.

July 2018 - Akida Development Environment (ADE) introduced.

September 2018 - First company to bring a production SNN architecture (Akida Neuromorphic SoC) to market.

June 2019 - CNN-to-SNN introduced.

June 2019 - Socionext Agreement. Noriaki Kubo, VP said…”we are excited to join Brainchip in the design, development and introduction of Akida SoC…bringing AI to edge applications is a major industry development and also a strategic application segment for Socionext”.

April 2020 - Brainchip and Socionext (development, manufacturing and COMMERCIAL partners) have completed tape-out and provided the Akida wafer fab to TSMC. Socionext will offer their customers an AI Platform that includes Akida SoC.

May 2020 - Joint agreement to evaluate Akida for ADAS and AV signed with Ford.

June 2020 - Joint Development agreement signed with Valero.

July 2020 - wafer fab completed.

August 2020 - Partnership agreement signed with Magic Eye.

September 2020 - Vorago sign EAP agreement.

December 2020 - Renasas sign first IP license agreement. First tape-out December 2022.

December 2020 - NASA places order under EAP agreement.

For mine, if these events were considered appropriate for an ASX announcement, one would think that the partnership and patent announcements of more recent times would be similarly suitable.

 

[robsmark]

Whose Zoomin’ Who ??

Me myself personally thought people here (especially those who boarded the bus in the last 15 months) might like a run through of some of the more interesting ASX announcements between 2018 and 2020.

July 2018 - Akida Development Environment (ADE) introduced.

September 2018 - First company to bring a production SNN architecture (Akida Neuromorphic SoC) to market.

June 2019 - CNN-to-SNN introduced.

June 2019 - Socionext Agreement. Noriaki Kubo, VP said…”we are excited to join Brainchip in the design, development and introduction of Akida SoC…bringing AI to edge applications is a major industry development and also a strategic application segment for Socionext”.

April 2020 - Brainchip and Socionext (development, manufacturing and COMMERCIAL partners) have completed tape-out and provided the Akida wafer fab to TSMC. Socionext will offer their customers an AI Platform that includes Akida SoC.

May 2020 - Joint agreement to evaluate Akida for ADAS and AV signed with Ford.

June 2020 - Joint Development agreement signed with Valero.

July 2020 - wafer fab completed.

August 2020 - Partnership agreement signed with Magic Eye.

September 2020 - Vorago sign EAP agreement.

December 2020 - Renasas sign first IP license agreement. First tape-out December 2022.

December 2020 - NASA places order under EAP agreement.

For mine, if these events were considered appropriate for an ASX announcement, one would think that the partnership and patent announcements of more recent times would be similarly suitable.

Woo woo wooooo RealInfo!… you know that kind of negativity is prohibited. Stick to the script Buddy.

 

[Slade]

After spending a lot of time in the sun today my mind is telling me that we a working with Texas Instruments.

 

[TopCat]

Sounds like brute force AI to me and no mention of power savings.

Have just finished listening to a podcast about it. The new processor has halved power consumption.

Starkey's Genesis AI: The Brain-Mimicking Hearing Aid

Starkey has launched Genesis AI, a hearing aid with advanced neural network technology mimicking the brain, after five years of development.

hearingreview.com

 

[Deleted member 118]

Afternoon Steve10,

I'll have a stab...

Bargain price....

AU$4.7325 = AU$8,828,150,300.00 Market Cap.

Converted to US currency...

US$3.1653874 = US$5,907,930,659.00 Market Cap.


* Total Shares, Options etc as of 2nd Feb 2023 is
1,856,430,614 .

* Pressent AU$ to US$ = 0.669215. As of 28/3/2022.

At the above prices one would have to think that the world's top ten company's CEO's & Boards would be displaying gross negligence to their Shareholders by not attempting a cheeky Buyout offer for Brainchip.

Regards,
Esq.

I’m going to have a guess and my guess is

 

[Fact Finder]

Have just finished listening to a podcast about it. The new processor has halved power consumption.

Starkey's Genesis AI: The Brain-Mimicking Hearing Aid

Starkey has launched Genesis AI, a hearing aid with advanced neural network technology mimicking the brain, after five years of development.

hearingreview.com

Did they say what the actual power use was and now is?

If not I would have thought if they were not using AKIDA and now are the power advantage would involve a five to ten times improvement as exampled by Mercedes Benz.

My opinion only DYOR
FF

AKIDA BALLISTA

 

[TopCat]

Did they say what the actual power use was and now is?

If not I would have thought if they were not using AKIDA and now are the power advantage would involve a five to ten times improvement as exampled by Mercedes Benz.

My opinion only DYOR
FF

AKIDA BALLISTA

No sorry , no figures were given

 

[alwaysgreen]

Nope:

What Is Fair Value?​

Fair value is the estimated price at which an asset is bought or sold when both the buyer and seller freely agree on a price.


To determine the fair value of a product or financial investment, an individual or business may look at actual market transactions for similar assets, estimate the expected earnings of the asset, and determine the cost to replace the asset.

KEY TAKEAWAYS​

• Fair value is the estimated price at which an asset is bought or sold when both the buyer and seller freely agree on a price.
• Individuals and businesses may compare current market value, growth potential, and replacement cost to determine the fair value of an asset.
• Fair value is a measure of an asset's worth and market value is the price of an asset in the marketplace.
• Fair value accounting is the practice of measuring a business's liabilities and assets at their current market value”

The ASX is not a market that determines Fair Value.

The fact that the ASX permits robotic trading under licence as well as short selling makes it anything but a place where the “buyer and seller freely agree on a price”.

The ASX is simply a place where you go to convert your shares into cash which is not a transaction at fair value.

My opinion only DYOR
FF

AKIDA BALLISTA

The unfortunate problem is, we don't really have a choice with how we buy Brainchip shares. ASX it is. And regardless of how "fair" our current value is, that is what the market and indices we trade on states is our current value.

I went camping last weekend and had a chat with a bloke who worked at and subsequently left the ASX because he couldn't deal with the garbage and carry on that went on there. A blatant disregard for other people's money and laughing at the misfortune of others.

Hopefully, when we are ready and revenue is in the $100s of millions, we will completely rid ourselves of the ASX entirely. And hopefully it isn't too far away.

 

[Fact Finder]

Whose Zoomin’ Who ??

Me myself personally thought people here (especially those who boarded the bus in the last 15 months) might like a run through of some of the more interesting ASX announcements between 2018 and 2020.

July 2018 - Akida Development Environment (ADE) introduced.

September 2018 - First company to bring a production SNN architecture (Akida Neuromorphic SoC) to market.

June 2019 - CNN-to-SNN introduced.

June 2019 - Socionext Agreement. Noriaki Kubo, VP said…”we are excited to join Brainchip in the design, development and introduction of Akida SoC…bringing AI to edge applications is a major industry development and also a strategic application segment for Socionext”.

April 2020 - Brainchip and Socionext (development, manufacturing and COMMERCIAL partners) have completed tape-out and provided the Akida wafer fab to TSMC. Socionext will offer their customers an AI Platform that includes Akida SoC.

May 2020 - Joint agreement to evaluate Akida for ADAS and AV signed with Ford.

June 2020 - Joint Development agreement signed with Valero.

July 2020 - wafer fab completed.

August 2020 - Partnership agreement signed with Magic Eye.

September 2020 - Vorago sign EAP agreement.

December 2020 - Renasas sign first IP license agreement. First tape-out December 2022.

December 2020 - NASA places order under EAP agreement.

For mine, if these events were considered appropriate for an ASX announcement, one would think that the partnership and patent announcements of more recent times would be similarly suitable.

Well MegaChips was which is basically identical to Renesas so they did announce that one exactly the same way:

https://www.asx.com.au/asxpdf/20211125/pdf/453dfzhh33r6xy.pdf

As for patents you have forgotten that they have announced patents and the one they did not announce their in house lawyers advised it was not so significant as to meet the definition of material.

As for the other categories most were subject of price sensitive ASX announcement in the following 4C’s so again not quite accurate to say they were not announced on the ASX.

I have argued from time to time directly with the company and mentioned it here that they could be more aggressive with the ASX.

They say they are not prepared to risk ASX sanctions as they were warned about the announcements made by the former CEO Mr. Dinardo who I recall you often criticised for a range of his failings as you then saw it.

So while I see merit in challenging the company over what it announces it undermines the strength of the argument by not properly marshalling the facts one relies upon.

My opinion only DYOR
FF

AKIDA BALLISTA

 

[Getupthere]

Uncovering new opportunities with edge AI — VentureBeat

In the current economic climate, R&D dollars must stretch further than ever. Companies are frowning on investments in large greenfield technology and infrastructure, while the risk of failure is contributing significant pressure to project stakeholders.

apple.news

Uncovering new opportunities with edge AI


In the current economic climate, R&D dollars must stretch further than ever. Companies are frowning on investments in large greenfield technology and infrastructure, while the risk of failure is contributing significant pressure to project stakeholders.


However, this does not mean that innovation should stop or even slow down. For startups and large enterprises alike, working on new and transformative technologies is essential to securing current and future competitiveness. Artificial intelligence (AI) offers multifaceted solutions across a widening range of industries.


In the past decade, AI has played a significant role in unlocking a whole new class of revenue opportunities. From understanding and predicting user behavior to assisting in the generation of code and content, the AI and machine learning (ML) revolution has multiplied many times over the value that consumers get from their apps, websites and online services.


Yet, this revolution has largely been limited to the cloud, where virtually unlimited storage and compute — together with the convenient hardware abstraction that the primary public cloud services providers offer — make it relatively easy to establish best-practice patterns for every AI/ML application imaginable.


AI: Moving to the edge


With AI processing principally happening in the cloud, the AI/ML revolution has remained largely out of reach for edge devices. These are the smaller, low-power processors found on the factory floor, at the construction site, in the research lab, in the natural reserve, on the accessories and clothes we wear, inside the packages we ship and in any other context where connectivity, storage, compute and energy are limited or cannot be taken for granted. In their environments, compute cycles and hardware architectures matter, and budgets are not measured in number of endpoint or socket connections, but in watts and nanoseconds.


CTOs, engineering, data and ML leaders and product teams looking to break the next technology barrier in AI/ML must look towards the edge. Edge AI and edge ML present unique and complex challenges that require the careful orchestration and involvement of many stakeholders with a wide range of expertise from systems integration, design, operations and logistics to embedded, data, IT and ML engineering.


Edge AI implies that algorithms must run in some kind of purpose-specific hardware ranging from gateways or on-prem servers on the high end to energy-harvesting sensors and MCUs on the low end. Ensuring the success of such products and applications requires that data and ML teams work closely with product and hardware teams to understand and consider each other’s needs, constraints and requirements.


While the challenges of building a bespoke edge AI solution aren’t insurmountable, platforms for edge AI algorithm development exist that can help bridge the gap between the necessary teams, ensure higher levels of success in a shorter period of time, and validate where further investment should be made. Below are additional considerations.


Testing hardware while developing algorithms


It’s not efficient nor always possible for algorithms to be developed by data science and ML teams, then passed to firmware engineers to fit it on device. Hardware-in-the-loop testing and deployment should be a fundamental part of any edge AI development pipeline. It is hard to foresee the memory, performance, and latency constraints that may arise while developing an edge AI algorithm without simultaneously having a way to run and test the algorithm on hardware.


Some cloud-based model architectures are also just not meant to run on any sort of constrained or edge device, and anticipating this ahead of time can save months of pain down the road for the firmware and ML teams.


IoT data does not equal big data


Big data refers to large datasets that can be analyzed to reveal patterns or trends. However, Internet of Things (IoT) data is not necessarily about quantity, but the quality of the data. Furthermore, this data can be time series sensor or audio data, or images, and pre-processing may be necessary.


Combining traditional sensor data processing techniques like digital signal processing (DSP) with AI/ML can yield new edge AI algorithms that provide accurate insights that were not possible with previous techniques. But IoT data is not big data, and so the quantity and analysis of these datasets for edge AI development will be different. Rapidly experimenting with dataset size and quality against the resulting model accuracy and performance is an important step on the path to production-deployable algorithms.


Developing hardware is difficult enough


Building hardware is difficult, without the added variable of knowing if the hardware selected can run edge AI software workloads. It is critical to begin benchmarking hardware even before the bill of materials has been selected. For existing hardware, constraints around the available memory on device may be even more critical.


Even with early, small datasets, edge AI development platforms can begin providing performance and memory estimates of the type of hardware required to run AI workloads.


Having a process to weigh device selection and benchmarking against an early version of the edge AI model can ensure the hardware support is in place for the desired firmware and AI models that will run on-device.


Build, validate and push new edge AI software to production


When selecting a development platform, it is also worth considering the engineering support provided by different vendors. Edge AI encompasses data science, ML, firmware and hardware, and it is important that vendors provide guidance in areas where internal development teams may need a bit of extra support.


In some cases, it is less about the actual model that will be developed, and more about the planning that goes into a system-level design flow incorporating data infrastructure, ML development tooling, testing, deployment environments and continuous integration, continuous deployment (CI/CD) pipelines.


Finally, it is important for edge AI development tools to accommodate different users across a team — from ML engineers to firmware developers. Low code/no code user interfaces are a great way to quickly prototype and build new applications, while APIs and SDKs can be useful for more experienced ML developers who may work better and faster in Python from Jupyter notebooks.


Platforms provide the benefit of flexibility of access, catering to multiple stakeholders or developers that may exist in cross-functional teams building edge AI applications.


Sheena Patel is senior enterprise account executive for Edge Impulse.

 

[Kachoo]

So I just looked at the Starkey hearing aid app. They have an edge mode but it's not edge AI you set the setting up and change them manually each time. Boy it be great if it was trainned over time to what works for you and automatically change on its own based on back ground noise. Akida Party lol.

 

[VictorG]

@Rocket577
It was posted a few days ago. Brainchip is independent of the other 2 but a good article all the same.

 

[Deleted member 118]

@Rocket577
It was posted a few days ago. Brainchip is independent of the other 2 but a good article all the same.

What was lol

 

[VictorG]

What was lol

LOL good one.

 

[mrgds]

What was lol

IT .........................

 

[Learning]

An interesting podcast with an intellectual intelligent super NERD regarding material for Neuromorphic Computing. Brainchip's should recruit her to the team.



Learning 🏖

 

[Steve10]

FYI

BRN ranked number 4 for likelihood of short squeeze. Current short borrow fee for BRN is 16.01% pa & was 15.56% a few weeks ago.

Most Shorted Stocks Australia (ASX)​

The Most Shorted Stocks Australia (ASX) uses an advanced quantitative model to determine companies that have the highest likelihood of experiencing a short squeeze. This model is a proprietary, multi-factor model that uses a number of factors, including Short Interest % Float, Short Borrow Fee Rates, and others.

Note that these rankings are updated throughout the day but lag the actual Short Squeeze Score displayed on company pages which are calculated on the fly. This means that it is possible to see a score on a company page that does not match a score here. In those situations, the score displayed on the company page should be considered the most current.

Rank Security MC ($M)

1	HVN / Harvey Norman Holdings Ltd	4,599.79
2	LTR / LIONTOWN RESOURCES LIMITED	3,352.64
3	YAL / YANCOAL AUSTRALIA LIMITED	7,117.17
4	BRN / BrainChip Holdings Ltd	803.97
5	CXO / CORE EXPLORATION LIMITED	1,446.91
6	FLT / Flight Centre Travel Group Ltd	3,969.01
7	BLD / Boral Ltd	3,938.03
8	MP1 / MEGAPORT LIMITED	641.62
9	REH / Reece Ltd	10,548.92
10	SYA / SAYONA MINING LIMITED COM	1,641.89

Most Shorted Stocks Australia (ASX)

Most Shorted Stocks Australia (ASX)

fintel.io

 

[VictorG]

FYI

BRN ranked number 4 for likelihood of short squeeze.

Most Shorted Stocks Australia (ASX)​

The Most Shorted Stocks Australia (ASX) uses an advanced quantitative model to determine companies that have the highest likelihood of experiencing a short squeeze. This model is a proprietary, multi-factor model that uses a number of factors, including Short Interest % Float, Short Borrow Fee Rates, and others.

Note that these rankings are updated throughout the day but lag the actual Short Squeeze Score displayed on company pages which are calculated on the fly. This means that it is possible to see a score on a company page that does not match a score here. In those situations, the score displayed on the company page should be considered the most current.

Rank Security MC ($M)

1	HVN / Harvey Norman Holdings Ltd	4,599.79
2	LTR / LIONTOWN RESOURCES LIMITED	3,352.64
3	YAL / YANCOAL AUSTRALIA LIMITED	7,117.17
4	BRN / BrainChip Holdings Ltd	803.97
5	CXO / CORE EXPLORATION LIMITED	1,446.91
6	FLT / Flight Centre Travel Group Ltd	3,969.01
7	BLD / Boral Ltd	3,938.03
8	MP1 / MEGAPORT LIMITED	641.62
9	REH / Reece Ltd	10,548.92
10	SYA / SAYONA MINING LIMITED COM	1,641.89

Most Shorted Stocks Australia (ASX)

Most Shorted Stocks Australia (ASX)

fintel.io

I don't think LTR is still on that list. They received a takeover offer today, was up 65% last I looked.
A perfect example of how obtuse shorters can be. One announcement by Brainchip and it will also vacate the list leaving behind chard carbon and the smell of sulphur where shorters once gathered.

 

[alwaysgreen]

I don't think LTR is still on that list. They received a takeover offer today, was up 65% last I looked.
A perfect example of how obtuse shorters can be. One announcement by Brainchip and it will also vacate the list leaving behind chard carbon and the smell of sulphur where shorters once gathered.

Not sure if a licence agreement will cause it to rocket like a takeover offer 65% higher than the current share price (as LTR received) but there would definitely be a few shorters starting to sell off.

Three or 4 deals though, that would really put a rocket up them. Hopefully Akida 1500 is the catalyst for more sales.