BRN Discussion Ongoing

[supersonic001]

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Another really nice and memorable image, but what has the Akida Edge AI Box got to do with “leading engineering innovation in the automotive sector”?

Cars arranged in BRN logo format, chapeau lads ….. on brand every time, consistently!

 

[Berlinforever]

Data centres and Akida? Here a comprehensive review.

https://arxiv.org/pdf/2402.02521

 

[Berlinforever]

This BrainChip presentation got picked up by others too?

TENN: A highly efficient transformer replacement for edge and event processing
M Anthony Lewis, Yan Ru Pei and Olivier Coenen
April 25, 2024

https://flagship.kip.uni-heidelberg.de/jss/HBPm?m=displayPresentation&mI=257&mEID=9439

 

[Diogenese]

This BrainChip presentation got picked up by others too?

TENN: A highly efficient transformer replacement for edge and event processing
M Anthony Lewis, Yan Ru Pei and Olivier Coenen
April 25, 2024

https://flagship.kip.uni-heidelberg.de/jss/HBPm?m=displayPresentation&mI=257&mEID=9439

Good lord!

Learnt about Chebyshev polynomials 50 years ago - didn't think you could actually use them!

 

[Fullmoonfever]

If not posted....McKinsey Eelectronics liking what we're doing highlighting the recent Microchip boards. Nice that one of their preferred partners is Valeo.

WHAT WE DO | McKinsey Electronics

A wide range of active and passive components. From MOSFETs, resistors, and sensors to FPGAs and microprocessors, we cater to your circuit design needs with our genuine electronic components.

www.mckinsey-electronics.com

McKinsey Electronics on LinkedIn: #brainchip #akida #mckinseyelectronics

BrainChip recently unveiled its Akida neuromorphic processor on Microchip's embedded platform, using SAMv71 Ultra and SAMA7G54-EK boards. These neuromorphic…

www.linkedin.com

McKinsey Electronics
1,555 followers
1w

BrainChip recently unveiled its Akida neuromorphic processor on Microchip's embedded platform, using SAMv71 Ultra and SAMA7G54-EK boards. These neuromorphic computing systems aim to perform parallel and distributed processing, imitating the structure and functionality of the human brain. It functions on an event-based principle, staying inactive until triggered, leading to decreased power consumption. This board highlights Akida's efficiency with a 32-bit microprocessor unit and a TSMC 28nm technology, focusing on always-on machine learning tasks like keyword spotting and visual wake words. This SoC implements 80 NPUs, offering on-chip learning and a complete machine-learning framework. The second-generation Akida platform supports the intelligence chip market, integrating AI accelerators for IoT and high-performance RF applications. It boasts event-based computing, scalability, and customizable AI neural processing capabilities. BrainChip's software development ecosystem supports seamless neural network creation, training and testing. #BrainChip #Akida #McKinseyElectronics

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[cosors]

Apologies if posted already

Geoffrey Eappen on LinkedIn: #ai #machinelearning #neuromorphiccomputing #keywordspotting #brainchip…

🚀 Exciting News from the Intersection of AI and Wireless Communication! 🌐 We are thrilled to announce that a groundbreaking demonstration of keyword spotting…

www.linkedin.com

https://www.linkedin.com/school/snt-lu/

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One of the likes

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And

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I love everything with space. But there should also be a little AKD on Earth.
I've seen pretty much all the documentaries about Mars robots. AKD empowered is more than a revolution I think.
They said that the moon travellers back then got further in one day or a few hours than the Mars rover did in three months. There are many decades of development in between.
I love Brainchip. That much is clear.

 

[DingoBorat]

Cars arranged in BRN logo format, chapeau lads ….. on brand every time, consistently!

Whoever they have doing these marketing images, whether it's an individual, or a small team, they are a gun

Then again, it might just be someone punching keywords, into a Generative A.I. program..

Impressive ideas, none the less.

 

[MDhere]

Puh-leeze!
Why did you have to dig out this misleading comparison once again, although I had already fact-checked it months ago? Well, here we go again:

In the “technical comparison” image you re-posted, Akida gets compared to Dynap-SEL, SynSense’s 2018 neuromorphic chip featuring “1k analog low-power spiking neurons and up to 80k configurable synaptic connections, including 8k synapses with integrated spike-based learning rules.”

Low-Power Neuromorphic Chip DYNAP™-SEL | SynSense

Our next-generation neuromorphic chip, Powering Innovation with 1k Analog Neurons and 80k Configurable Synapses, Including Integrated Spike-Based Learning!

www.synsense.ai

You are evidently aware, though, that BMW are experimenting with SynSense’s fully event-driven neuromorphic vision SoC Speck for their smart cockpit occupant monitoring R&D. So why are you not comparing Akida to Speck instead, as you ought to (although I am not sure whether a direct comparison between AKD1000 and a smart vision processing SoC combining a dynamic vision sensor (DVS) and a neuromorphic processor, actually makes sense?)

https://www.synsense.ai/wp-content/uploads/2023/06/Speck-application-brochures-EN.pdf

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And to be fair, you should also factor in Speck’s competitive price tag at < 7 $ (presumably USD) that could easily tip the scales in SynSense’s favour, when potential customers who don’t mind doing business with a de facto-Chinese company consider Speck’s technical specifications “good enough“ for their envisaged use cases, even though AKD1000 boasts more than three times as many neurons as Speck (but not more than 1000x as many, as your comparison seems to suggest). They may not see the point in paying more for a product that could be described as “over-engineered” for their narrow use cases.

IMO, you are doing BrainChip no favour by cherry-picking a competitor’s far less capable neuromorphic mixed-signal chip for your apples and oranges-comparison. By doing so, you are totally exaggerating the parameter divide between Akida and the competition’s more advanced neuromorphic offerings, eg in SynSense’s case, the fully digital neuromorphic processor Dynap-CNN:

DYNAP™-CNN: Neuromorphic Processor With 1M Spiking eurons

DYNAP™-CNN is world's first fully scalable, event-driven neuromorphic processor with 1M configurable spiking neurons and direct interface with external DVS.

www.synsense.ai

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What’s the point of unfairly disparaging the competition?
While we shareholders may wish for BrainChip to literally make EVERY sensor smart one day, the commercial reality will be that we will never gain 100% of the global market share. Regardless of any technological superiority.

Settle petal. Bravos contributions are well respected and Im surprised that you are so vocal in such a rude way.
Have a calm Sunday, your contribuons a sometimes long but good! Except when you go nuts on someone. Bit bipolar behaviour im guessing? Or drinking when you wrote that? Or none of the above.
If i may with your permission.. that May i remind you that Brainchip is years ahead of competitors.
Take a chill pill and wind back the attacks on good people please.
Kind regards
MD

 

[Frangipani]

For the sake of completeness, usually one of Frangipanni's fortes, my post in full added:

"Hi Bravo,

Apologies for that German chap.

That project looks to me to be more in the "R" phase of R&D.

"The project aims to develop new devices for implementing the ONN architecture and processing the analog sensor data. It is led by researchers from Technical University in Eindhoven.

It is a university project and universities seem to br enthralled by the close analogy between wetware and analog neurons. They still have to come to grips with the inherent variability of ReRAM/MemRistors.

Not commercial whthin 5 years +. "

Just to expand on that,

https://phastrac.eu/

PHASTRAC is a research project funded by Horizon EU’s research and innovation programme with core subject “Phase Transition Materials for Energy Efficient Edge Computing”. The project with duration of 42 months (1 January 2023 – 30 June 2026) brings together leading European research and academic institutions.

PHASTRAC aims to develop a novel analog-to-information neuromorphic computing paradigm based on oscillatory neural networks (ONNs). We offer a first-of-its-kind and novel analog ONN computing architecture to seamlessly interface with sensors and process their analog data without any analog-to-digital conversion. ONNs are a biologically inspired neuromorphic computing architecture, where neuron oscillatory behavior will be developed by innovative phase change VO2 material coupled with synapses developed by bilayer Mo/HfO2 RRAM devices. PHASTRAC will address the most critical issues, namely 1) novel devices for implementing ONN architecture, 2) novel ONN architecture to allow analog sensor data processing, and 3) processing the data efficiently to take appropriate action. This “sensing-to-action” computing approach based on ONN technology will allow energy efficiency improvements.

TU Eindhoven, Netherlands, leads the consortium of four partners. The project partners are Pazmany Peter Catholic University, Hungary, IBM Research Zurich, and Bayerische Motoren Werke AG (BMW), Germany.

With all those exotic materials, it sounds pretty ickky to manufacture. Does this need a new machine?

The ickkyness goes on ... capacitors and varistors ...

IBM has a patent application for ONN:


US2022004876A1 TRAINING OF OSCILLATORY NEURAL NETWORKS 20200702

[0002] Oscillatory neural networks (ONNs) are artificial neural networks which employ a network of coupled oscillators. The oscillators correspond to neurons of a neural network, and the strength of the coupling between pairs of oscillators emulates the network (synaptic) weights. Such networks can be trained to perform correlation tasks, such as image classification and speech recognition, by processing training samples in the network and adapting the matrix of network weights so that the network “learns”, or memorizes, the particular correlations to be detected. Few hardware implementations have been proposed for ONNs, and these networks typically rely on mathematical pre-training of the weights matrix, e.g. via a Hebbian learning algorithm.

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The network comprises at least one network layer in which a plurality of electronic oscillators, interconnected via programmable coupling elements storing respective network weights, generate oscillatory signals at time delays dependent on the input signal to propagate the input signal from an input to an output of that layer. The network is adapted to provide a network output signal dependent substantially linearly on phase of oscillatory signals in the last layer of the network. The method includes calculating a network error dependent on the output signal and a desired output for the training sample, and calculating updates for respective network weights by backpropagation of the error such that weight-updates for a network layer are dependent on a vector of time delays at the input to that layer and the calculated error at the output of that layer.


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Note the IBM inventors are based in Zurich.

This sounds like spike rate with the added complexity of phase matching, which, added to the inconsistency of capacitors and memRistors sounds messy to implement.

What are you talking about?
Your post is right there, and in full length, too, once you click on “click to expand”.
May I suggest you get yourself a (new) pair of reading glasses?

https://thestockexchange.com.au/threads/brn-discussion-ongoing.1/post-420263

 

[MrNick]

Settle petal. Bravos contributions are well respected and Im surprised that you are so vocal in such a rude way.
Have a calm Sunday, your contribuons a sometimes long but good! Except when you go nuts on someone. Bit bipolar behaviour im guessing? Or drinking when you wrote that? Or none of the above.
If i may with your permission.. that May i remind you that Brainchip is years ahead of competitors.
Take a chill pill and wind back the attacks on good people please.
Kind regards
MD

Spot on.

 

[JoMo68]

What are you talking about?
Your post is right there, and in full length, too, once you click on “click to expand”.
May I suggest you get yourself a (new) pair of reading glasses?

https://thestockexchange.com.au/threads/brn-discussion-ongoing.1/post-420263


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Enough of the condescending attitude!

 

[TECH]

Good morning,

Yesterdays graphics on X feed highlighted 4 sectors which indicate in my opinion exactly where AKD will be deployed in
automobiles, the company pointing us in the right direction.

The graphics have improved tenfold in all our marketing material, top stuff management, keep up the great work behind the scenes, some
of us do appreciate the effort to continually improve as we slowly become more and more professional year on year.

Regards...Tech.

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