BRN Discussion Ongoing

[Tony Coles]

Are you referring to Akida?

You got rocks in your head mate… wake up bud. They are talking about 4DS.

 

[Deleted member 118]

I never thought we be getting close to the $1 again, but can’t complain with my self super now available. Should I wait for it to drop into the 90s?

 

[robsmark]

Although today was completely shit - I guess the takeaway is that we should remain optimistic that our technology at least works and has been validated accordingly by several industry leaders. For that I am thankful.

Hearts out to the 4DS holders - it must be a though pill to swallow, not only watching your investment disappear, but for many of the long term holders, watching the hope of any substantial future return disappear with it.

 

[Boab]

BRAINCHIP: THESE CHIPS ARE UNIQUE
Away from operational worries and hardships, the BrainChip stock is being traded very briskly these days - within a month it was up 16%. BrainChip relies on a revolutionary chip architecture that is based on the human brain and is designed to use extremely little energy. The self-learning chips should therefore be used for many future tasks, such as autonomous driving. In the past, BrainChip caused a sensation when the technology was tried out in the voice control of Daimler cars, among other things, and provided outstanding results.

A few weeks ago, the authorities in Australia, where BrainChip has its home stock exchange, published the twenty largest shareholders of the up-and-coming company. Including such illustrious names as Citicorp, HSBC, BNP Paribas, Merrill Lynch and several other asset managers and family offices. Since BrainChip offers convincing solutions for everything to do with AI and the price has fallen significantly in recent months, investors could make a note of the value. Although there will be no dividends in the foreseeable future, the patented architecture could prevail. Earlier this year, the market ran through this scenario once before, sending the stock to levels more than 100% above its current price.

The boom in electric mobility is causing the shares of growth companies and industry giants to rise. The only difference is in intensity. Smaller companies in particular, which are still fighting for market share or who want to bring their products to market first, could have great potential in the medium term. Examples are BYD, which has already jumped in, and the AI chip pioneer BrainChip. The downside is a higher risk. Those who shy away from this should better orientate themselves in the direction of Infineon and Volkswagen.

This is an interesting paper. Does not name BRN or AKIDA but when read you will see why partnering with ARM Cortex M4 is so very exciting:

A Brief Review of Deep Neural Network Implementations for ARM Cortex-M Processor

....System-On-Chip (SoC) devices are an attractive solution that, in addition to high processing capabilities, includes multiple peripheral devices that can be very helpful for the sophisticated requirements of deep-learning applications. Examples of manufacturers that develop AI integrated circuits for edge computing are Samsung, Texas Instruments, Qualcomm, and STM. Some of their recent products are briefly presented below.......

5. Conclusions
Deep learning and deep neural networks are emerging as promising solutions for solving complex problems. Solving complex problems requires high computational capabilities and memory resources, so are traditionally designed to run on a large computer system around specialized hardware. However, recent research shows that simple applications can benefit from the deep learning paradigm and their edge computing implementation as well. Edge computing is the solution to many real-world problems that need to be solved soon. For instance, the automotive industry is using and developing prototypes using state-of-the-art hardware and software solutions for autonomous driving. Once these prototypes prove their ability to solve problems, the systems will have to run on real-world cars. At that stage, cost is necessary to be competitive in the market, and, using high performance computing solutions, the cost is high. The edge computing paradigm must be prepared with efficient and low-cost solutions while meeting specific requirements such as functional safety. In this work, we provide a summary of what edge computing means in the context of low-cost/low-power applications. Here, the ARM Cortex-M processor represents one of the best possible candidates. More specifically, we summarize deep neural network implementations using ARM Cortex-M core-based microcontrollers. From the software perspective, the STM32Cube.AI support package, made available by STMicroelectronics for its 32-bit microcontroller series, represents one of the best freely available tools. Implementing deep neural networks on embedded devices, such as microcontrollers, is a difficult task. This is mainly due to the computation and memory footprint constrains. For this reason, it is observed that developers are forced to customize existing architectures or even develop from scratch innovative models that better suit embedded processors. Optimization techniques such as quantization, pruning, and distillation are constantly evolving to achieve higher performance, and they are enabling developers to introduce state-of-the-art models of increasing complexity to the embedded domain. Ultimately, using an optimized hardware combined with optimized deep neural network architectures leads to maximum energy efficient systems. Electronics 2022, 11, 2545 19 of 21 Future work proposes to extend the study to a wider family of ARM cores, including, for example, deep learning applications running on Cortex-A type processors or even specialized Arm Ethos-N series processors for machine learning

https://www.mdpi.com/2079-9292/11/16/2545/pdf?version=1660467458

My technical knowledge is limited but after scanning this paper there is not one mention of SNN's.
All of the references are pre our announced partnership with ARM and to me in regards to Akida'a capabilities this research is out of date.
Happy to be corrected.
I do however agree with @Sirod69 that our partnership with ARM is even more exciting if my above thinking is correct.

 

[VictorG]

This has most likely been posted before, no apologies though because on days like today I like to read and reread articles like this.

Machine learning platform Edge Impulse to support BrainChip Akida Neuromorphic IP | Edge Industry Review

The goal of the companies cooperation is to boost the use of edge machine learning applications for large-scale enterprise use.

www.edgeir.com

 

[JPIck]

Just driving home and heard on the radio FORD have a new headlight technology , curious if AKIDA is a part of this??
Sorry if this has already been posted

 

[equanimous]

Convolutional Spiking Neural Networks for Detecting Anticipatory Brain Potentials Using Electroencephalogram

[2208.06900] Convolutional Spiking Neural Networks for Detecting Anticipatory Brain Potentials Using Electroencephalogram

Now who do we know who has a Convolutional Spiking Neural Network chip already commercially available:

VI. CONCLUSION AND FUTURE WORK This paper explored the use of a CSNN as a classifier for detecting features in EEG data that predict braking intention, which occurs before the actual physical activity. The EEG data for the classification experiment was collected via an in-house experiment using a pseudo-realistic testbed with the participant operating a remote-controlled vehicle using a live video feed. The CSNN performance was compared to a standard CNN and three GNN models using a 10-fold cross-validation scheme with the CSNN achieving the highest performance and with more consistency. In addition, the effect of converting the floating-point EEG data into spike trains prior to training the CSNN was studied. The best results were obtained using a threshold of 0.5, which were similar to those obtained using floating-point data, suggesting that spike train transformation might be possible with acceptable levels of performance degradation. Future work includes assessing the performance of the CSNN in different environments, particularly when the cognitive functions of the participants are stressed because of, for example, fatigue or distractions. In addition, the ability of the CSNN to decode the participant’s intention in other EEG control signals in BCI applications, such as P300, motor imagery, motor-related cortical potentials and steady-state evoked potentials would be of interest. Implementation of the CSNN on a neuromorphic platform to study energy efficiency is another area of future research.

https://arxiv.org/pdf/2208.06900.pdf

 

[Tony Coles]

I never thought we be getting close to the $1 again, but can’t complain with my self super now available. Should I wait for it to drop into the 90s?

Rocket577, mate take on your gut feeling, i have 3 different top ups myself placed ready to pounce on if it does reach my targets and 2 of them are in the 90s and the other in the high 80s just in case, on the other hand might not even go below $1.00 They are trying to confuse us genuine BRN share holders by the trading tactics that they are playing around with, at the moment looks like that the ters might get what they want ultimately and then ……… BRN can stretch its wings.

 

[Deleted member 118]

Rocket577, mate take on your gut feeling, i have 3 different top ups myself placed ready to pounce on if it does reach my targets and 2 of them are in the 90s and the other in the high 80s just in case, on the other hand might not even go below $1.00 They are trying to confuse us genuine BRN share holders by the trading tactics that they are playing around with, at the moment looks like that the ters might get what they want ultimately and then ……… BRN can stretch its wings.

I’ll see what happens the next few days and maybe buy in on Friday, as fridays tend to be not a good day for BrN shares

 

[marsch85]

I never thought we be getting close to the $1 again, but can’t complain with my self super now available. Should I wait for it to drop into the 90s?

Over the last 6 months we’ve been trading between 0.80-1.30. Without price sensitive news, we’ll probably stay in this range and could also go below $1 again. SP will keep bouncing around at these levels until IP licenses and/or non-lumpy revenue growth appear.

 

[Fullmoonfever]

May have been posted previously as was a blog post by Doug at Megachips in late July in Embedded.

Speaks of NN and I liked the last sentence highlighted...hopefully some out there take heed

MegaChips Corporation

Provide total solution from design and development to manufacturing for State-of-the-art LSIs by full use of our unique analog and digital technologies.

megachips.com

Implementing Edge AI: Look Before You Leap​

This blog post was originally published at Embedded.com on behalf of MegaChips.
As the need for artificial intelligence grows more common and technology needs become more sophisticated, companies looking to adopt edge AI into their products often find it to be a difficult challenge. But what makes it so difficult, and what solutions exist to solve this problem?
Perhaps the single biggest issue that companies face in implementing edge AI is that most companies don’t have the resources in house to develop these sophisticated fast-changing technologies. Lack of trained personnel and little familiarity with design flow often leads to delayed timelines and excess expense to train team members. In addition, there are so many choices, it is impossible for engineers to explore each option. And since every application is different, it may not be appropriate to replicate solutions based on past implementations. However, by asking a few key questions and finding the right partner to take your project from ideation to silicon, any enterprise can develop a roadmap to successfully deploy edge AI in their devices.

Defining Use Cases And Feasibility​

It is important to define use cases before exploring implementation options. The first question any business should ask is: What would the customer find truly useful? After pinpointing the functionality the customer wants, your team needs to set development and production cost goals along with the acceptable time to market.
Now comes the challenging part – making technology-related decisions. Is it possible to implement that functionality within the cost, time, power and space tradeoffs you’re dealing with? Working with an experienced partner/consultant or drawing on internal experience is critical at this stage. You won’t have perfect data on which to base your decision, so actual experience is essential in making these judgements.

Technology Choice​

There are several choices a team can make to implement the customer’s desired functionality in the product. Depending on your available resources and development time, here are some of the choices a team might consider:

• Software only on the existing embedded processor – This may require very carefully coded models in order to achieve the desired performance. Functionality may be limited, but it is generally the lowest cost solution if it works. Because this is a software-only solution, upgrades or bug fixes are more easily addressed.
• Upgrade/replace the existing processor – This can be a great solution if you can make it work and preserve existing code base, and like the solution above, is software-only and can be easily fixed or upgraded. However, this can often start a project down a slippery path that requires extensive power and performance evaluation. Companies may be better off adding a neural network (NN) or similar accelerator.
• Add a fixed neural network accelerator – This is an optimum choice if there is a good match with the needs of the application, as evaluation and design may not be too difficult. It could very well provide excellent power/performance tradeoffs at a very reasonable cost.
• FPGA – This solution is flexible and upgradable, but typically comes with high cost and high power for the final product. Rarely is this a good choice for “edge” products.
• Dedicated SoC – Often this is the optimum choice for high volume, low cost and low power products where use cases are clearly defined.

How To Evaluate The Right Choice​

It can be difficult to evaluate the right choice without expertise from trained professionals in the edge AI chip space. Evaluation of each option can often take a long time and require extensive knowledge. For example, evaluating a fixed accelerator versus an FPGA implementation can require engineers with different skill sets. With so many vendors and solutions making conflicting claims, making basic decisions can be overwhelming for most enterprises.

One of the most important steps one can take is to find the right partner who can help evaluate the technology tradeoffs and take the company from the initial research and evaluation stage to the design and implementation of the solution. Also, don’t get hung up on finding the solution with the “optimum” power/performance. If you can identify a solution that will work and has adequate software and technical support, that is likely your best choice. Don’t get caught chasing specs.

Building The Solution​

Once functionality and technology have been chosen, the next step is implementation. Often the focus is on the implementation of a neural network model, however businesses also have to deal with the implementation of logic (software/hardware) to handle the pipeline from sensor to final output, requiring unique algorithms at each step.

Questions that might come up include:

• What kind of signal conditioning/filtering do I need before passing the data to a NN accelerator?
• Which NN model should I use? Is there an existing model for my technology selection? Which version of which model is best in my application?
• How do I train my model? Where do I get my data and what biases are built into that data? What volume of data do I need?
• What is the cost and availability of the processing power for training models? Do we train in the cloud or on local servers?
• What level of accuracy is adequate? Is it better to have false positives or false negatives?
• What post processing is required and can I handle that workload?

Final Words Of Advice​

With so many vendors voicing conflicting claims, it is important for businesses looking to implement edge AI not to focus on finding the “best TOPS” or the “fastest” solution, as these are elusive goals. The best way to answer many of these questions of functionality, technology choice and implementation is to partner with a person or organization that has “been there, done that.” Someone with the experience to quickly evaluate potential use cases, technical solutions and vendor offerings to help you narrow your choices as quickly and accurately as possible. Focus on vendors that have the most complete solution, with both the engine to implement, but also models, algorithms, and even existing data to help you in your unique use case and create a solid proof of concept.

Douglas Fairbairn
Director of Business Development, MegaChips

 

[Sirod69]

oh how nice to read from him again, laugh, the sentence brain chip is not one of them, but I missed it

Why is the BrainChip share price sinking today?

The BrainChip Holdings Ltd (ASX: BRN) share price is falling today despite the S&P/ASX 200 Index (ASX: XJO) travelling higher. Here's why.

www.fool.com.au

 

[equanimous]

oh how nice to read from him again, laugh, the sentence brain chip is not one of them, but I missed it

View attachment 14270

Why is the BrainChip share price sinking today?

The BrainChip Holdings Ltd (ASX: BRN) share price is falling today despite the S&P/ASX 200 Index (ASX: XJO) travelling higher. Here's why.

www.fool.com.au

No offence but this is Motley Fool asking why its sinking (wanca)

 

[VictorG]

Interesting project for the US DOD, calling for new edge architecture. It would be interesting to see which OEM's are involved and if they use akida anywhere in their design submissions.

SAM.gov

sam.gov

 

[JoMo68]

It's complicated but essentially, the chip doesn't work.

At the moment…

 

[GStocks123]

Is Mr. Hehir alive?

 

[SharesForBrekky]

You'd think Brainchip would be all over this:

U.S. DOD wants edge computing, fog computing tech to improve decision making | Edge Industry Review

The DOD is eyeing transformational edge computing and fog computing technologies that could revolutionize how the agency processes and shares data.

www.edgeir.com

Akida to the rescue!

 

[Stable Genius]

I have been a be pre-occupied lately so I may have missed some posts lately but Rob Telson liked this.

Obviously it’s a long bow to draw as there’s no guarantees every company Rob likes is a customer however Drones are an obvious target for Brainchip. Fingers crossed!

 

[Slade]

I have been a be pre-occupied lately so I may have missed some posts lately but Rob Telson liked this.

View attachment 14280

Obviously it’s a long bow to draw as there’s no guarantees every company Rob likes is a customer however Drones are an obvious target for Brainchip. Fingers crossed!

This company shoots down drones. You did say "Drones are an obvious target for BrainChip". Very Clever!

 

[DingoBorat]

I have been a be pre-occupied lately so I may have missed some posts lately but Rob Telson liked this.

View attachment 14280

Obviously it’s a long bow to draw as there’s no guarantees every company Rob likes is a customer however Drones are an obvious target for Brainchip. Fingers crossed!

A company like Dronesheild, would definitely be dealing with BrainChip, or at least seeing how they could disrupt a drone using our IP.

They have no choice.

Their technology, is dependent on being able to disable drones.

Fully autonomous drones, powered by something like AKIDA, would probably be practically invulnerable, to their current tech.

They are in danger, of being made obsolete, to all but "public" drones (for airport control/safety etc).
And their big contracts are military (my impression).

It's probably Rob, giving them the heads up of..
"Better check out what we're enabling Sunshine "..