BRN Discussion Ongoing

cosors

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The obscure US financier backing the ASX’s hot stocks​

LDA Capital is the go-to financier for a handful of speculative ASX plays, and its model is now being embraced by local funds.

sorry I could read this article for short, then there is a stop and you must pay to read the whole article.
Brainchip was named

Delete your cookies and then reload the page and immediately stop the reload. Then you can read it. By the way, this says a lot about the page...
 
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equanimous

Norse clairvoyant shapeshifter goddess
Found facts:-

BrainChip is the AArdvark of the ARM partnership programme.

https://www.arm.com/partners/catalog/results#f:Industry=[Artificial Intelligence]

I know this has been remarked upon before, but I think it bears repeating. When any single category is selected, BrainChip is at the top of the list in the majority of cases, and this is not an alphabetical phenomenon. This means that any person searching through the ARM Partners for most of those categories will see listed BrainChip first up.

In fact, you have to get down to Edge Gateway to shake BrainChip off.
View attachment 13491

But you want more - scroll down to:

Arm and Google: tinyML Pioneers
tinyML is a fast-growing field of machine learning (ML) technologies and applications implemented by Arm and Google to enable ultra-low power ML at the edge. See how tinyML can help solve computer vision, audio recognition, speech recognition, and natural language processing challenges
.

https://www.arm.com/partners/artificial-intelligence##

Trillions of ML devices!

So using the standard 1% baseline, that's 10's of billions Google/Akida devices.

But are Google contemplating outside partnerships?

View attachment 13497



https://services.google.com/fh/files/misc/ai_adoption_framework_whitepaper.pdf


So if Google were to incorporate Akida IP in their transformational partnerships, we could be in more than 20% of trillions of Google AI/ML devices.

Nice to see ARM and Google are on speaking terms! Wonder if anyone at Google has thought to search ARM's partnership lists for available AI/ML tech?
 
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Quiltman

Regular
Found facts:-

BrainChip is the AArdvark of the ARM partnership programme.

https://www.arm.com/partners/catalog/results#f:Industry=[Artificial Intelligence]

I know this has been remarked upon before, but I think it bears repeating. When any single category is selected, BrainChip is at the top of the list in the majority of cases, and this is not an alphabetical phenomenon. This means that any person searching through the ARM Partners for most of those categories will see listed BrainChip first up.

In fact, you have to get down to Edge Gateway to shake BrainChip off.
View attachment 13491

But you want more - scroll down to:

Arm and Google: tinyML Pioneers
tinyML is a fast-growing field of machine learning (ML) technologies and applications implemented by Arm and Google to enable ultra-low power ML at the edge. See how tinyML can help solve computer vision, audio recognition, speech recognition, and natural language processing challenges
.

https://www.arm.com/partners/artificial-intelligence##

Trillions of ML devices!

So using the standard 1% baseline, that's 10's of billions Google/Akida devices.

But are Google contemplating outside partnerships?

View attachment 13497



https://services.google.com/fh/files/misc/ai_adoption_framework_whitepaper.pdf


So if Google were to incorporate Akida IP in their transformational partnerships, we could be in more than 20% of trillions of Google AI/ML devices.

Nice to see ARM and Google are on speaking terms! Wonder if anyone at Google has thought to search ARM's partnership lists for available AI/ML tech?

It really impresses on me the size of the ecosystem developing around tingML, and now neuromorphic computing.

As an example of this, the tinyML organisation that started in 2018/2019 and has attracted many heavyweights over the last few years, with Microsoft being the latest. Brainchip remains a key sponsor, with other sponsors including Tensorflow, Synaptics, Sony, Samsung, Renesas, Qualcomm, Prophesee, Plumerai, Intel, Infineon, Edge Impulse and ARM.

TinyML and Neuromorphic are becoming closely entwined. In fact tinyML are running an event on the 29th September for all members ( can guarantee many from the above organisations will be attending ) called :

tinyML Neuromorphic Engineering Forum​


tinyML is a fast-growing initiative around low-power machine-learning technologies for edge devices. The scope of tinyML naturally aligns with the field of neuromorphic engineering, whose purpose is to replicate and exploit the way biological systems sense and process information within constrained resources.
In order to build on these synergies, we are excited to announce the first tinyML Forum on Neuromorphic Engineering. During this event, key experts from academia and industry will introduce the main trends in neuromorphic hardware, algorithms, sensors, systems, and applications.


Among the speakers are our own Anil Mankar, Christoph POsch, CTO of Propesee and Yulia Sandamirskaya from Intel Labs.

Combining Neuromorphic Design Principles with Modern Machine Learning Algorithms​

Anil MANKAR, Chief Development Officer, BrainChip
Abstract (English)
Neuromorphic computing takes inspiration from the structure and function of neural systems and seeks to replicate the energy efficiency, tolerance to noise, representational power, and learning plasticity these systems possess. Current machine learning (ML) algorithms, such as convolutional neural networks (CNNs), are capable of state-of-the-art performance in many computer vision applications such as object classification, detection, and segmentation. In this talk, we discuss how our neuromorphic design architecture, Akida, brings these ML algorithms into the neuromorphic computing domain by executing them as spiking neural networks (SNNs). We highlight how hardware design choices such as the event-based computing paradigm, low-bit width precision computation, the co-location of processing and memory, distributed computation, and support for efficient, on-chip learning algorithms enable low-power, high-performance ML execution at the edge. Finally, we discuss how this architecture supports next generation SNN algorithms such as binarized CNNs and algorithms that efficiently utilize temporal information to increase accuracy.


Sensors​

Neuromorphic Event-based Vision​

Christoph POSCH, CTO, PROPHESEE
Abstract (English)
Neuromorphic Event-based (EB) vision is an emerging paradigm of acquisition and processing of visual information that takes inspiration from the functioning of the human vision system, trying to recreate its visual information acquisition and processing operations on VLSI silicon chips. In contrast to conventional image sensors, EB sensors do not use one common sampling rate (=frame rate) for all pixels, but each pixel defines the timing of its own sampling points in response to its visual input by reacting to changes of the amount of incident light. The highly efficient way of acquiring sparse data, the high temporal resolution and the robustness to uncontrolled lighting conditions are characteristics of the event sensing process that make EB vision attractive for numerous applications in industrial, surveillance, IoT, AR/VR, automotive. This short presentation will give an introduction to EB sensing technology and highlight a few exemplary use cases.


So over the last few years I believe we have transformed in the eyes of the tech savvy investing community from " will neuromorphic computing actually even work from a commercial perspective, and even if i does, surely there are limited applications " to a recognition that tinyMl and neuromorphic compute will become "ubiquitous" in our lives with endless applications.
For Brainchip investors, the addressable market is forming beyond our wildest dreams, now it's a matter of our market share.
From what I can see, Brainchip is front and center, and it's going to acquire a fair piece of the action.

It really is very good to be a shareholder !
 
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Deleted member 118

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This would be nice





318EFDCF-ACA1-4EC5-A221-78E4E6DD3AC7.png
 
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Deadpool

hyper-efficient Ai
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Says confidential and private on the 1st page. I wonder if someone forgot to put a password on there google drive. Saved to my google drive now. Good to see they also have many NDAs. Very exciting times if you ask me.


 
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TECH

Regular
Good morning Brainchip supporters,

I can confirm that I have followed up on my effort to get an update on how things have been progressing with the Akida 2000 development, over say, the last 10/12 months.

The matter is highly sensitive, how do I know this ?

Having sought some (new) information from the "most knowledgeable staff member" nothing has been forthcoming, which I totally respect, and that is our answer in a nutshell, all progress is highly confidential.

An update may appear, but not at this point in time, it purely indicates that very important things are at play.

We all know that it's a long runway now, so much more patience is required, and until some other company knocks us off our perch, well, we
remain the NUMBER 1 PLAYER WORLDWIDE IN THIS SPACE...get used to it, it feels great being a shareholder (stolen phrase).

Love Brainchip...Tech x
 
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Proga

Regular
Says confidential and private on the 1st page. I wonder if someone forgot to put a password on there google drive. Saved to my google drive now. Good to see they also have many NDAs. Very exciting times if you ask me.



Page 37. 4 Nviso apps are interoperable. Hopefully 1 or 2 are commercialised soon.
bandicam 2022-08-07 12-57-16-620.jpg
 
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Deleted member 118

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This brainchip video was over 3 years ago and quite a while before most of us had heard of BrN, but I just watched it for the 1st time.

 
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uiux

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Bravo

If ARM was an arm, BRN would be its biceps💪!

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Proga

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Here we go. NVIDIA Jetson Nano (4GB) doesn't use Akida

NVISO AI App model performance
can be accelerated by average by
3.67x using neuromorphic
computing over a single core ARM
Cortex A57 as found in a NVIDIA
Jetson Nano (4GB).
 
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Deleted member 118

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Another old video

 
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equanimous

Norse clairvoyant shapeshifter goddess
Hi @Diogenese

Not sure if this has been posted before. Looks like this combination will be effective for harsh weather conditions with vision applications.


Spiking neural network (SNN) has attracted much attention due to its powerful spatio-temporal information representation ability. Capsule Neural Network (CapsNet) does well in assembling and coupling features of different network layers. Here, we propose Spiking CapsNet by combining spiking neurons and capsule structures. In addition, we propose a more biologically plausible Spike Timing Dependent Plasticity routing mechanism. The coupling ability is further improved by fully considering the spatio-temporal relationship between spiking capsules of the low layer and the high layer. We have verified experiments on the MNIST, FashionMNIST, and CIFAR10 datasets. Our algorithm still shows comparable performance concerning other excellent SNNs with typical structures (convolutional, fully-connected) on these classification tasks. Our Spiking CapsNet combines SNN and CapsNet’s strengths and shows strong robustness to noise and affine transformation. By adding different Salt-Pepper and Gaussian noise to the test dataset, the experimental results demonstrate that our algorithm is more resistant to noise than other approaches. As well, our Spiking CapsNet shows strong generalization to affine transformation on the AffNIST dataset. Our code is available at https://github.com/BrainCog-X/Brain-Cog.

4. Conclusion​

In this paper, the Spiking CapsNet is proposed by introducing the capsule structure into the modelling of the spiking neural network, which fully combines their spatio-temporal processing capabilities. The cortical minicolumns inspire the capsules to work as the coincidence detectors [14]. The information transmission method based on discrete spike trains is more consistent with the work mechanism of the human brain. Meanwhile, we propose a more biologically plausible STDP routing algorithm inspired by the learning mechanisms of synapses in the brain [2]. The routing algorithm fully considers the part and the whole spatial relationship between the low-level capsule and the high-level capsules, as well as the spike firing time order between the pre-synaptic and post-synaptic neurons. The coupling ability between low-level and high-level spiking capsules is further improved. Compared with other excellent BP-based SNNs [37], our model shows great adaptability to noise and the spatial affine transformation. Although LISNN [4] and HMSNN [44] show excellent robustness to the noise, they cannot handle the spatial affine transformation well. Our model shows strong performance and robustness on the MNIST, FashionMNIST, and CIFAR10 datasets.

1659843266851.png
 
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equanimous

Norse clairvoyant shapeshifter goddess
Interesting..


Dilute combustion control using spiking neural networks​

Invention Reference Number​


202104856

Technology Summary​


Technologies directed to dilute combustion control using spiking neural networks is described.

Inventors​

Bryan Maldonado Puente
Buildings & Transportation Science Division

Licensing Contact​


Andrei Zorilescu
zorilescua@ornl.gov

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Bravo

If ARM was an arm, BRN would be its biceps💪!
California’s Department of Motor Vehicles (DMV) has accused Tesla of falsely advertising its Autopilot and Full Self-Driving (FSD) features.


Screen Shot 2022-08-07 at 1.55.15 pm.png
 
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equanimous

Norse clairvoyant shapeshifter goddess

Yahoo Finance had 93.9M monthly unique visitors in November 2020​

 
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