BRN Discussion Ongoing

[Iseki]

He who laughs last, laughs best

Just remember that quote, as we are about to enter the most exciting 18 month chapter in our short history !

Proven technology, Technology that continues to advance, Proven beyond any possible doubt in silicon, If our
competitors had the secret sauce with their access to huge capital, well, products would have already been in the marketplace.

Brainchip is right on track to succeed, yes it feels so, so slow, but trust the Texta we will set the tech world on fire, and maybe
a bunch of shorts at the same time.

God Bless our leaders !

Tech back in New Zealand

Gesture Control

teksun.com

Astra Edge Camera

teksun.com

Time will tell

 

[Bravo]

I'm also willing to trade for a reasonable number of shares. Units are x 1000 but the placard was too small to fit the extra zeros. So 72,000 BRN shares and he's all yours!

PM me if interested.

 

[tjcov87]

 

[Damo4]

View attachment 38622

So Mercedes could stick to a "Hey Mercedes" command in the future and that would mean it would be 5-10 times more efficient running on Akida than their current voice control platform.

Motortrend: Mercedes-Benz Vision EQXX Concept: Did You See This Coming, Elon?
Mercedes engineers worked with California-based artificial-intelligence developer BrainChip to create systems based on the company’s Akida hardware and software. Among other things, the technology makes the “Hey, Mercedes” voice control system in the EQXX five to ten times more efficient than conventional voice control.

 

[Diogenese]

Could someone please ring Rob Telson and ask him to give Renee Haas (CEO Arm) a call, to see if Rene would mind giving Masayoshi Son (CEO SoftBank) a call and to remind Son when he is speaking to Sam Altman (CEO Open AI) tomorrow, if he could please quickly mention Shyamal Anadkat from OpenAI's (ChatGPT) recent Linkedin post below, which mentions us?

TIA




View attachment 38620

https://lnkd.in/gEN3NdXt


View attachment 38619

There's more of Svengali than Trilby in you.

 

[buena suerte :-)]

He who laughs last, laughs best

Just remember that quote, as we are about to enter the most exciting 18 month chapter in our short history !

Proven technology, Technology that continues to advance, Proven beyond any possible doubt in silicon, If our
competitors had the secret sauce with their access to huge capital, well, products would have already been in the marketplace.

Brainchip is right on track to succeed, yes it feels so, so slow, but trust the Texta we will set the tech world on fire, and maybe
a bunch of shorts at the same time.

God Bless our leaders !

Tech back in New Zealand

With ya mate .... Enjoy your trip

 

[Zedjack33]

He who laughs last, laughs best

Just remember that quote, as we are about to enter the most exciting 18 month chapter in our short history !

Proven technology, Technology that continues to advance, Proven beyond any possible doubt in silicon, If our
competitors had the secret sauce with their access to huge capital, well, products would have already been in the marketplace.

Brainchip is right on track to succeed, yes it feels so, so slow, but trust the Texta we will set the tech world on fire, and maybe
a bunch of shorts at the same time.

God Bless our leaders !

Tech back in New Zealand

Bloody well hope that is the case Tech.

 

[cosors]

View attachment 38622

I can not judge but are these two things related?

"Shyamal Hitesh Anadkat​

Applied AI at OpenAI​

...
To counter this, the focus will shift toward specialized hardware, optimization techniques, and neural processor architectures. Being able to run optimized versions of foundational models at the edge/on-device will open up endless possibilities. Recognizing these emerging requirements, BrainChip has stepped forward with innovative solutions like the Akida processor, an advanced neural processing system for edge AI. It’s important to understand and assess edge AI technology to overcome deployment challenges and explore new potentials."
https://www.linkedin.com/pulse/frontiers-startups-2023-shyamal-hitesh-anadkat

 

[Deleted member 118]

What a concept car. Sorry just a car and nothing else incase you were wondering

 

[IloveLamp]

@Iseki what is your deal today?
Before you jump on ILoveLamp, do your own research.

BrainChip and Socionext Provide a New Low-Power Artificial Intelligence Platform for AI Edge Applications​

ALISO VIEJO, Calif.–(BUSINESS WIRE)– BrainChip Holdings Ltd (ASX: BRN), a leading provider of ultra-low power high performance AI technology, today announced that Socionext Inc., a leader in advanced SoC solutions for video and imaging systems, will offer customers an Artificial Intelligence Platform that includes the Akida SoC, an ultra-low power high performance AI technology.

BrainChip has developed an advanced neural networking processor that brings artificial intelligence to the edge in a way that existing technologies are not capable. This innovative, event-based, neural network processor is inspired by the event-based nature of the human brain. The resulting technology is high performance, small, ultra-low power and enables a wide array of edge capabilities that include local inference and incremental learning.

Socionext has played an important role in the implementation of BrainChip’s Akida IC, which required the engineering teams from both companies to work in concert. BrainChip’s AI technology provides a complete ultra-low power AI Edge Network for vision, audio, and smart transducers without the need for a host processor or external memory. The need for AI in edge computing is growing, and Socionext and BrainChip plan to work together in expanding this business in the global market.

Complementing the Akida SoC, BrainChip will provide training and technical customer support, including network simulation on the Akida Development Environment (ADE), emulation on a Field Programmable Gate Array (FPGA) and engineering support for Akida applications.

Socionext also offers a high-efficiency, parallel multi-core processor SynQuacerTM SC2A11 as a server solution for various applications. Socionext’s processor is available now and the two companies expect the Akida SoC engineering samples to be available in the third quarter of 2020.

In addition to integrating BrainChip’s AI technology in an SoC, system developers and OEMs may combine BrainChip’s proprietary Akida device and Socionext’s processor to create high-speed, high-density, low-power systems to perform image and video analysis, recognition and segmentation in surveillance systems, live-streaming and other video applications.

“Our neural network technology enables ultra-low power AI technology to be implemented effectively in edge applications”, said Louis DiNardo, CEO of BrainChip. “Edge devices have size and power consumption constraints that require a high degree of integration in IC solutions. The combination of BrainChip’s technology and Socionext’s ASIC expertise fulfills the requirements of edge applications. We look forward to working with the Socionext in commercial engagements.”

“As a leading provider of ASICs worldwide, we are pleased to offer our customers advanced technologies driving new innovations,” said Noriaki Kubo, Corporate Executive Vice President of Socionext Inc. “The Akida family of products allows us to stay at the forefront of the burgeoning AI market. BrainChip and Socionext have successfully collaborated on the Akida IC development and together, we aim to commercialize this product family and support our increasingly diverse customer base.”

 

[Fullmoonfever]

Hadn't personally seen this project over at Edge Impulse before.

Google search link said late May 23 but who knows.

Have taken all the code sections etc out but full read at the link. Pretty cool running with FOMO.

https://docs.edgeimpulse.com/experts/featured-machine-learning-projects/brainchip-akida-traffic-monitoring

Traffic Monitoring using the Brainchip Akida Neuromorphic Processor​

A computer vision project that monitors vehicle traffic in real-time using video inferencing performed on the Brainchip Akida Development Kit.
Created By: Naveen Kumar
Public Project Link: https://studio.edgeimpulse.com/public/222419/latest

Overview​

A highly efficient computer-vision system that can detect moving vehicles with great accuracy and relative motion, all while consuming minimal power.

By capturing moving vehicle images, aerial cameras can provide information about traffic conditions, which is beneficial for governments and planners to manage traffic and enhance urban mobility. Detecting moving vehicles with low-powered devices is still a challenging task. We are going to tackle this problem using a Brainchip Akida neural network accelerator.

Hardware Selection​

In this project, we'll utilize BrainChip’s Akida Development Kit. BrainChip's neuromorphic processor IP uses event-based technology for increased energy efficiency. It allows incremental learning and high-speed inference for various applications, including convolutional neural networks, with exceptional performance and low power consumption.

The kit consists of an Akida PCie board, a Raspberry Pi Compute Module 4 with Wi-Fi and 8 GB RAM, and a Raspberry Pi Compute Module 4 I/O Board. The disassembled kit is shown below.

The Akida PCIe board can be connected to the Raspberry Pi Compute Module 4 IO Board through the PCIe Gen 2 x1 socket available onboard.

The FOMO model uses an architecture similar to a standard image classification model which splits the input image into a grid and runs the equivalent of image classification across all cells in the grid independently in parallel. By default the grid size is 8x8 pixels, which means for a 224x224 image, the output will be 28x28 as shown in the image below.

For localization, it cuts off the last layers of the classification model and replaces this layer with a per-region class probability map, and subsequently applies a custom loss function that forces the network to fully preserve the locality in the final layer. This essentially gives us a heat map of vehicle locations. FOMO works on the constraining assumption that all of the bounding boxes are square, have a fixed size, and the objects are spread over the output grid. In the aerial view images, vehicles look similar in size hence FOMO works quite well.

Confusion Matrix​

Once the training is completed we can see the confusion matrices as shown below. By using the post-training quantization, the Convolutional Neural Networks (CNN) are converted to a low-latency and low-power Spiking Neural Network (SNN) for use with the Akida runtime. We can see in the below image, the F1 score of 94% of the Quantized (Akida) model is better than that of the Quantized (int8) model.

Demo​

The video used for the demonstration runs at a framerate of 24 fps, and the inferencing takes approximately 40ms per frame, resulting in real-time inferencing.

Conclusion​

This project highlights the impressive abilities of the Akida PCIe board. Boasting low power consumption, it could be used as a highly effective device for real-time object detection in various industries for numerous use cases.

 

[rgupta]

Intel Unveils Aurora genAI: A Trillion-Parameter AI Model to Revolutionize Scientific Breakthroughs and Predict the Unseen

Intel Unveils Aurora genAI: A Trillion-Parameter AI Model to Revolutionize Scientific Breakthroughs and Predict the Unseen

www.marktechpost.com

 

[rgupta]

Speed up neural networks 3.3x with Sparse Weight Activation Training [Breakdowns] | Eric Feuilleaubois (Ph.D)

Speed up neural networks 3.3x with Sparse Weight Activation Training [Breakdowns] https://buff.ly/3qHx31N

www.linkedin.com

 

[MadMayHam | 合氣道]

Just saw one of our our employees that I follow on instagram liking Kim Kadarshian's post.
Now I am not saying she must be involved with Akida. But I am not saying she is NOT invovled at all.
To me this is exciting news regardless.
Anyway no need to keep your eyes on her insta page or our SP.
DYOR YMMV

Ahhh... so that's what all the silicon is for!

 

[CrabmansFriend]

Nothing specific about Brainchip, nonetheless an interesting article in my opinion:

https://www.eetimes.com/can-any-novel-architecture-topple-the-mighty-gpu/

Favorite Quotes:

Ian Cutress:
“The number that I always get quoted is something like 90% of the training market is currently hosted by Nvidia. But when I speak training, there’s obviously the whole world of inference that sometimes we forget about.”
...
However, there are many more real-world needs for inference, and Nvidia has no plans to meet them, he said.
...
“The devices that we hold in our hands, the devices on the edge and even going in to solve the data center market, there’s a lot more malleability there for these new AI hardware vendors to play in, to take advantage of, to find cost-effective solutions—and optimize solutions with customers,” he said. “That’s where I see the biggest opportunity to kind of battle the Nvidia juggernaut.”


Bill Jenkins:
“I’ll go back to one of the biggest problems,” he said. “Not many people really know what they want to do. There are just so many ways and so many things that they could implement. You know, I look at the GPU, the CPU and even the FPGA as that flexible architecture that can handle everything. And then the question is: Does it need to do something really well, and is there an alternative dedicated piece of hardware for that something?”


Nitin Dahad:
Dahad pointed out that there is a lot of expertise required on the customer side in using the hardware and software for AI. He asked the panelists what they are asking for from the industry.

“I would say the No. 1 thing is, ‘I’ve got a model. How do I implement that on your architecture?’” Jenkins said. “And then they’re going to compare that performance against where they are today. So if somebody can provide [a product that is] going to be lower-latency, lower-power, higher-performance and turnkey … they’ll take it and tweak it over time.”

 

[HopalongPetrovski]

Ahhh... so that's what all the silicon is for!

 

[Iseki]

View attachment 38631

BrainChip and Socionext Provide a New Low-Power Artificial Intelligence Platform for AI Edge Applications.... Just not together, in the one package​

hence Socionext also offers...

Look, I've tried to be positive with the following:

Astra Edge Camera

teksun.com

and

Gesture Control

teksun.com

I do this because Teksun says they are a partner.

40c was my sell line, but WTF...

 

[Terroni2105]

Hadn't personally seen this project over at Edge Impulse before.

Google search link said late May 23 but who knows.

Have taken all the code sections etc out but full read at the link. Pretty cool running with FOMO.

https://docs.edgeimpulse.com/experts/featured-machine-learning-projects/brainchip-akida-traffic-monitoring

Traffic Monitoring using the Brainchip Akida Neuromorphic Processor​

A computer vision project that monitors vehicle traffic in real-time using video inferencing performed on the Brainchip Akida Development Kit.
Created By: Naveen Kumar
Public Project Link: https://studio.edgeimpulse.com/public/222419/latest

Overview​

A highly efficient computer-vision system that can detect moving vehicles with great accuracy and relative motion, all while consuming minimal power.

By capturing moving vehicle images, aerial cameras can provide information about traffic conditions, which is beneficial for governments and planners to manage traffic and enhance urban mobility. Detecting moving vehicles with low-powered devices is still a challenging task. We are going to tackle this problem using a Brainchip Akida neural network accelerator.

Hardware Selection​

In this project, we'll utilize BrainChip’s Akida Development Kit. BrainChip's neuromorphic processor IP uses event-based technology for increased energy efficiency. It allows incremental learning and high-speed inference for various applications, including convolutional neural networks, with exceptional performance and low power consumption.

The kit consists of an Akida PCie board, a Raspberry Pi Compute Module 4 with Wi-Fi and 8 GB RAM, and a Raspberry Pi Compute Module 4 I/O Board. The disassembled kit is shown below.


View attachment 38629


The Akida PCIe board can be connected to the Raspberry Pi Compute Module 4 IO Board through the PCIe Gen 2 x1 socket available onboard.


View attachment 38628

The FOMO model uses an architecture similar to a standard image classification model which splits the input image into a grid and runs the equivalent of image classification across all cells in the grid independently in parallel. By default the grid size is 8x8 pixels, which means for a 224x224 image, the output will be 28x28 as shown in the image below.

View attachment 38627

For localization, it cuts off the last layers of the classification model and replaces this layer with a per-region class probability map, and subsequently applies a custom loss function that forces the network to fully preserve the locality in the final layer. This essentially gives us a heat map of vehicle locations. FOMO works on the constraining assumption that all of the bounding boxes are square, have a fixed size, and the objects are spread over the output grid. In the aerial view images, vehicles look similar in size hence FOMO works quite well.

Confusion Matrix​

Once the training is completed we can see the confusion matrices as shown below. By using the post-training quantization, the Convolutional Neural Networks (CNN) are converted to a low-latency and low-power Spiking Neural Network (SNN) for use with the Akida runtime. We can see in the below image, the F1 score of 94% of the Quantized (Akida) model is better than that of the Quantized (int8) model.

Demo​

The video used for the demonstration runs at a framerate of 24 fps, and the inferencing takes approximately 40ms per frame, resulting in real-time inferencing.

Conclusion​

This project highlights the impressive abilities of the Akida PCIe board. Boasting low power consumption, it could be used as a highly effective device for real-time object detection in various industries for numerous use cases.

that’s great, I haven’t seen it before either, thanks for posting it

 

[Diogenese]

Nothing specific about Brainchip, nonetheless an interesting article in my opinion:

https://www.eetimes.com/can-any-novel-architecture-topple-the-mighty-gpu/

Favorite Quotes:

Ian Cutress:
“The number that I always get quoted is something like 90% of the training market is currently hosted by Nvidia. But when I speak training, there’s obviously the whole world of inference that sometimes we forget about.”
...
However, there are many more real-world needs for inference, and Nvidia has no plans to meet them, he said.
...
“The devices that we hold in our hands, the devices on the edge and even going in to solve the data center market, there’s a lot more malleability there for these new AI hardware vendors to play in, to take advantage of, to find cost-effective solutions—and optimize solutions with customers,” he said. “That’s where I see the biggest opportunity to kind of battle the Nvidia juggernaut.”


Bill Jenkins:
“I’ll go back to one of the biggest problems,” he said. “Not many people really know what they want to do. There are just so many ways and so many things that they could implement. You know, I look at the GPU, the CPU and even the FPGA as that flexible architecture that can handle everything. And then the question is: Does it need to do something really well, and is there an alternative dedicated piece of hardware for that something?”


Nitin Dahad:
Dahad pointed out that there is a lot of expertise required on the customer side in using the hardware and software for AI. He asked the panelists what they are asking for from the industry.

“I would say the No. 1 thing is, ‘I’ve got a model. How do I implement that on your architecture?’” Jenkins said. “And then they’re going to compare that performance against where they are today. So if somebody can provide [a product that is] going to be lower-latency, lower-power, higher-performance and turnkey … they’ll take it and tweak it over time.”

I think that encapsulates the quandary - back in the last millennium, marketing used to talk about customers' wants and customers' needs.

Because the capabilities of Akida are far beyond those of known technology, customers do not know what they want it to do, let alone what they need it to do.

 

[Zedjack33]

Full blown conniption on its way.