BRN Discussion Ongoing

[DK6161]

Carefull what you say @Cyw wouldnt like you to get roasted, lol
@DK6161 may buy more shares based on that.

Lol typical bullsh1t from the same CEO that predicted explosion in sales in 2022 2023 2024.
The only thing that is imminent is this bloke leaving us in 6 months. Bye.

 

[Gemmax]

Bravo.
Don’t run away!!
Maybe around the block though!

 

[HopalongPetrovski]

Hi Chips,

It‘s getting annoying for me too. I really shouldn’t have to feel the need to defend myself over and over again. There are more important things to focus on in life. So perhaps it’s time for me to sign out for a little while.

Good luck to all genuine holders. We’ve stuck together through thick and thin and I’m very hopeful that our luck will turn shortly.

B x

Hi Bravo.
Sure, take a break if you wish/need to.
It's healthy to step away from social media every once and a while.
Smell the kitten mitts, ride the rainbow, have a run and a spa. Reset.
We'll miss you and what you bring here along with your gorgeous personality.
Frangipanni seems to be an excellent researcher, as are you, but I rarely read what she puts here in such excruciating and voluminous detail.
Frankly I don't have the time or inclination.
But, I'm happy to skim over 99% of it in the expectation that one day it may be important to me.
If he/she/they/it are bothering you with their attacks I suggest you treat them as I do the pests over on the crapper.
Don't engage with them. They exist, but that doesn't mean they can, or have to, define your existence, unless you allow it.
They may say all sorts of horrid things or spin myriad distortions to justify existence, but just don't play their game.
Anyhow, I hope you will return to us soon, as I, and I am sure, many others will miss you. xo

 

[Quiltman]

For all those who mistakenly use the SP to value Brainchip.

 

[Esq.111]

Chippers ,

CRANK IT.



Esq.

 

[Hammertime]

First it was fact finder now it’s Bravo , well dun pongy ,absolute disgrace.

Agree, Bravo's posts are about Brainchip, Frangipani's posts look like they are about Brainchip but in reality they are just about Frangipani.

 

[Diogenese]

View attachment 72206

Brainchip’s MetaTF Software Tools ease the Transition of ML Models to the Edge​

Kurt Manninen, BrainChip Senior Solutions Architect​

Empowering Companies: Seamless AI Migration to the Edge​

In their latest survey report, Edge Impulse highlights that the estimated market value of edge AI will be $143.6B by 2032. Companies that are early adopters of AI on the edge, particularly those that are willing to invest in the latest neural network hardware to give themselves an advantage over their competitors, will be looking to their vendors to help make the transition as painless as possible. This is why BrainChip developed our MetaTF framework: as a way for companies to effortlessly transition their existing machine learning models into a format that takes advantage of Akida’s event-based computing platform.

MetaTF is a suite of tools that can be used to convert, quantize and run existing machine learning models on an Akida event-based processor, which reduces computations by only computing on event data, not the raw sparse data as conventional NPUs must do. It was built for the purpose of providing an easy path for current and prospective BrainChip customers to validate their existing models on our advanced processor. As an experienced software and machine learning engineer who is new to event-based computing, BrainChip’s MetaTF allowed me to use tools that I already know (i.e. Python and Jupyter Notebooks) to experiment with ML on the edge. This is a valuable offering that will help engineers at developer organizations quickly migrate their processes to edge-based learning.

Embedded Devices ≠ Servers​

When it comes to deploying machine learning solutions, embedded devices present unique challenges compared to traditional server environments. Unlike servers with standardized architectures and operating systems, the world of IoT devices is incredibly diverse. From smartphones to smart sensors, each device may have a different architecture, run on a different operating system, or in some cases, operate on bare metal without any OS at all. This design diversity makes software packaging and installation a complex task. Moreover, when scaling to thousands or even millions of devices, manually managing each installation becomes impractical. This is where software build tools become invaluable, offering a way to manage complexity, ensure repeatability, and create consistent device images across a wide range of hardware configurations.

Edge Impulse+MetaTF = The Easy Button for Buildroot and Yocto​

Training a model, and making it small enough for low size, weight and power devices is one thing, but packaging and deploying the application to the edge device is another. BrainChip has extensive experience with major embedded build systems such as Buildroot and Yocto. Our goal is to ensure that our Akida System on Chip design can be seamlessly integrated into our customers unique embedded environments. Furthermore, our partnership with Edge Impulse is helping to revolutionize the ML-to-Edge training and deployment process, making it easier than ever to deploy advanced event-based machine learning solutions on embedded devices.

A prime example of our expertise in action is the Akida™ Edge Box, developed in collaboration with our partners VVDNand Edge Impulse. This innovative product embodies the principles discussed in this blog post, showcasing our ability to tackle complex edge AI challenges. The Edge Box runs on an NXP i.MX 8M application processor, featuring dual Akida 1000 chips on reference boards connected to a single M.2 PCI card. Our demonstration models were trained using Edge Impulse Studio and we bundled our MetaTF libraries into the target image using NXP’s i.MX Yocto Project Board Support Package. This real-world application demonstrates our commitment to integrating cutting-edge event-based computing with established embedded system build tools.

To learn more about how BrainChip’s solutions architecture team can integrate cutting-edge event-based machine learning into your embedded device toolchain, or to explore how the BrainChip Edge AI Box could benefit your AI initiatives, contact us for a demonstration. We’re here to help you take your specific ML processing needs and AI products to the edge.

"A prime example of our expertise in action is the Akida™ Edge Box, developed in collaboration with our partners VVDNand Edge Impulse."

The mention of Edge impulse indictes that we have been working with EI to develop RAG small language models adapted (quantized) for Akida.

 

[Mea culpa]

Hi Bravo.
Sure, take a break if you wish/need to.
It's healthy to step away from social media every once and a while.
Smell the kitten mitts, ride the rainbow, have a run and a spa. Reset.
We'll miss you and what you bring here along with your gorgeous personality.
Frangipanni seems to be an excellent researcher, as are you, but I rarely read what she puts here in such excruciating and voluminous detail.
Frankly I don't have the time or inclination.
But, I'm happy to skim over 99% of it in the expectation that one day it may be important to me.
If he/she/they/it are bothering you with their attacks I suggest you treat them as I do the pests over on the crapper.
Don't engage with them. They exist, but that doesn't mean they can, or have to, define your existence, unless you allow it.
They may say all sorts of horrid things or spin myriad distortions to justify existence, but just don't play their game.
Anyhow, I hope you will return to us soon, as I, and I am sure, many others will miss you. xo

Again Hoppy you have conveyed in words what I have, and it appears from the responses that others have felt also. I concur with your comment on Frangipani’s research; similarly to yourself, I have limited time. @Bravo is a contributor of long-standing. Her posts are informative, respectful and often her humour, her mischievous humour, is delightfully wicked. She has shared much of her personal life with us. I have not met her, yet I feel she is a friend. I too, will miss her, and cherish her return.

 

[7für7]

Hi Chips,

It‘s getting annoying for me too. I really shouldn’t have to feel the need to defend myself over and over again. There are more important things to focus on in life. So perhaps it’s time for me to sign out for a little while.

Good luck to all genuine holders. We’ve stuck together through thick and thin and I’m very hopeful that our luck will turn shortly.

B x

Are you kiddin’ me ehhhh?! This stronzo tries tellin’ you how to make your sauce ohhhuu? Ma che cazzo! You’ve been doin’ this since forever, and he thinks he’s got somethin’ to say? Get the hell outta here ohh! Mamma mia, these idiots gonna give me a freakin’ stroke! Mannaggiaaaaa …ma Che cazzo fai?????!!! Oooooooooohhuuuu

 

[Labsy]

I'm miss FF don't you now leave @Bravo.... Far out.

 

[buena suerte :-)]

Holding up extremely well with Less than expected 4C!! and very negative markets overnight and today!!

 

[Draed]

I hate the motley crew as much as anyone. But have you all noticed a change in sentiment. They no longer refer to cafe incomes and meme stock etc.

Why the BrainChip share price is in the spotlight this week

The BrainChip share price has been on quite the ride these past 2 days after the company's Q3 cash flow update. Let's see.

www.fool.com.au

 

[7für7]

I hate the motley crew as much as anyone. But have you all noticed a change in sentiment. They no longer refer to cafe incomes and meme stock etc.

Why the BrainChip share price is in the spotlight this week

The BrainChip share price has been on quite the ride these past 2 days after the company's Q3 cash flow update. Let's see.

www.fool.com.au

They obviously change their sentiment depending on if they are shorting a stock or buying

 

[Guzzi62]

They obviously change their sentiment depending on if they are shorting a stock or buying

Agreed.

There is no honor among thieves!

MF is the worst crap so-called stock advisor tool you can spend money on.

They always end their talk about a company with: Whoever these 5 stocks have a higher score, bla bla.

 

[TheDrooben]

Big line wipe at 24.5c just now..........

 

[Esq.111]

 

[buena suerte :-)]

Big line wipe at 24.5c just now..........

Looking good

 

[charles2]

Noted of late in the thinly traded NASDAQ that there have been unusually large bids for BRCHF in the range of 100-500k. And these are seemingly serious bids as they frequently are raised intra-session.

Someone or some entity wants large quantities of stock and are being professionally prudent.

 

[JoMo68]

They obviously change their sentiment depending on if they are shorting a stock or buying

It’s not written by our mate Micklepenis. That’s probably the reason for the more objective sentiment.

 

[Tothemoon24]

"A prime example of our expertise in action is the Akida™ Edge Box, developed in collaboration with our partners VVDNand Edge Impulse."

The mention of Edge impulse indictes that we have been working with EI to develop RAG small language models adapted (quantized) for Akida.

Agree Dio , plenty going on with EI

Not sure if you’ve seen this ;
More in the link ;

Industrial Inspection Line - Brainchip Akida | Expert Projects

An industrial inspection application that uses the Brainchip Akida Neuromorphic processor for fast and efficient quality control inferencing.

docs.edgeimpulse.com

Industrial Inspection Line - Brainchip Akida​

An industrial inspection application that uses the Brainchip Akida Neuromorphic processor for fast and efficient quality control inferencing.

Created By: Peter Ing
Public Project Links:
Object Detection - https://studio.edgeimpulse.com/studio/349843
Classification - https://studio.edgeimpulse.com/studio/349858
GitHub Repo: https://github.com/peteing/brainchip_edgeimpulse_inspectionsystem.git

Introduction​

In the ever-evolving landscape of modern manufacturing, the efficiency and accuracy of production lines are paramount. The meticulous inspection of products at various stages ensures not only the adherence to quality standards but also the optimization of resources. In this dynamic scenario, the integration of cutting-edge technologies such as computer vision and artificial intelligence has emerged as a game-changer.
Initially, machine vision systems relied on basic image processing techniques and rule-based algorithms. These early systems were capable of performing relatively simple tasks, such as inspecting products for basic defects or checking for the presence of specific features. These systems required cameras with high-cost Industrial PC's to perform CPU-based processing that was expensive and power hungry, while offering limited performance.
Today the trend has shifted towards using Deep Learning, specifically Convolutional Neural Networks on Graphics Processing Units and specialized CNN hardware accelerators. However, the solutions on the market are still relatively costly and power hungry. Cameras and IPC's are available with integrated acceleration built-in for industrial use cases, but are very expensive.
Neuromorphic processing, inspired by the human brain, diverges from traditional computing with its parallel, adaptive features like Spiking Neural Networks, parallel processing, event-driven computation, and synaptic plasticity. This disruptive technology holds promise for energy-efficient, brain-like information processing, particularly in tasks like pattern recognition and sensory processing. This makes Neuromorphic computing ideal for use in Industrial inspection systems where it can provide real-time insights into product quality. The benefits include reduced costs, improved performance, and being able to adapt the system at the edge to new use-cases.
Brainchip Akida represents the state of the art in production-ready Neuromorphic computing, ideally suited to edge use-cases. We will be demonstrating the power of the Brainchip Akida in an industrial setting in this guide as part of a standalone inspection system that can be setup along a production line.
The Akida processor is available on a PCIe card form-factor for integration into your own hardware, or ships as either an Intel or Arm-based developer kit. For the purpose of this project our focus is on the Arm-based developer kit, which consists of a Raspberry Pi Compute Module 4 mounted on a Raspberry PI Compute Module 4 IO board, enclosed in a metal housing.
Many users coming from an Industrial environment have limited experience when it comes to AI and Deep Learning, which can seem daunting. There are very expensive platforms and solutions that help simplify the process, but none can match the ease of use and rapid performance of using Edge Impulse for the AI component of your project.

Industrial Inspection Use Case​

A typical scenario in an industrial manufacturing plant is defect detection. This can be applied to a range of different product types, but essentially the requirement is always to determine which products to reject, out of a set of products that are often moving along some kind of materials handling equipment such as a conveyor.
To achieve this, classic machine vision techniques using old camera systems running CPU algorithms often included detecting a Region of Interest (ROI) and then focusing on that area, while using tools such as edge and blob detection to find anomalies.

Deep learning solves this approach by making use of learning algorithms to simply teach the system what is correct and what is not correct. This results in a 2-stage pipeline that first does Object Detection, then cascades the results to a Classifier.