BRN Discussion Ongoing

Slade

Top 20
IP Licenses are Brainchips play first and foremost, as without them being signed there is no royalty stream..end of story.

We are all aware or should be that the royalty stream is where the real revenue is long term, I also only mentioned our EAP customers, as in
at least 8 Tier 1 companies, not small fry, why won't they commit do you think?

"This year will be about hopefully some more licenses and some engineering fees", that's exactly what I am referring to, I appreciate
your view, but you appear to have got caught up on my word "play"

"Brainchips play are royalties, not IP licenses." We are following the ARM business model as a supplier of IP, that's our "initial play"

I do agree with your view on the other matters that you raised, cheers.

Tech (y)
Hi Tech,
I’m only guessing and I figure the answer to your question is probably very complicated. I think part of the answer comes from when the early EAPs were established. During this time Brainchip was in a very early phase of its development and still trying to figure out the right path forward. In some ways I think the early EAPs might still be experiencing privledges that new customers are not. Since then, we have seen a lot of expert staff hired and a much clearer strategy put in place which I think will put a lot more focus on closing sales. I get the gut feeling that our original EAPs like Ford and Valeo are in the enviable position of being able to wait for Akida 2.0 before they step out from their NDA.
There is also our growing ecosystem of parters that I find interesting. I wonder whether some of our early EAPs have now been passed onto our partners to sign commercial contracts. I have no idea about how these kind of arrangements work but I hope we can get some clarity at the next AGM.
 
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Was having a look around on Prophesee info and came across the CVPR from 2021.

Understand this is prior to the release of our partnership news though I believe we were in discussions with them back in 2021 from memory.

Anyway, link to the program which includes presenters slides and video recording links. May or may not have been covered previously.

Haven't had chance to review / watch as yet but posting fwiw to kill some time while we wait for the qtrly and hopefully an add on Ann.

Couple that could be interesting is the one by Luca at Prophesee and also Greg Cohen at WSU where he discusses their space camera but also the Foosball which we all saw in an article a while ago which mentioned Akida & Intel neuromorphic.

The slides from Luca, I did look at, and was curious on the last several applications listed. The ones prior was "public" and generally had the partner listed whereas the last ones (where I believe we could assist with) were listed as undisclosed at that time. Couple example snips below & hoping we're well advanced with testing / integration with Prophesee.




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Iseki

Regular
Would this be similar to all the Partnerships being announced on social media but not the asx ?
Totally. Unless there is a known level of revenue attributable, it's not material...
 
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Iseki

Regular
BRN IP royalties should only be paid at the point of chip production by BRN partners and Licensees.
BRN clients are not consumers.
I think you will find that the license fee only applies to what the BRN client can sell.
I guess the bottom line is that we all hope it becomes so compelling to have akida ip in you average CPU, that it get's put in their just in case it's needed.
 
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equanimous

Norse clairvoyant shapeshifter goddess
Was having a look around on Prophesee info and came across the CVPR from 2021.

Understand this is prior to the release of our partnership news though I believe we were in discussions with them back in 2021 from memory.

Anyway, link to the program which includes presenters slides and video recording links. May or may not have been covered previously.

Haven't had chance to review / watch as yet but posting fwiw to kill some time while we wait for the qtrly and hopefully an add on Ann.

Couple that could be interesting is the one by Luca at Prophesee and also Greg Cohen at WSU where he discusses their space camera but also the Foosball which we all saw in an article a while ago which mentioned Akida & Intel neuromorphic.

The slides from Luca, I did look at, and was curious on the last several applications listed. The ones prior was "public" and generally had the partner listed whereas the last ones (where I believe we could assist with) were listed as undisclosed at that time. Couple example snips below & hoping we're well advanced with testing / integration with Prophesee.




View attachment 27761

View attachment 27762

View attachment 27763

View attachment 27764
With the vibration monitoring you will be able to attach that sensor to a cricket bat, baseball bat, tennis racket and get instant notification when you hit the sweet spot for training. It will eventually be able to detect a persons emotions and irregular heart beats via a phone.
I wander how professor Barry Marshall is doing these days??
 
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FJ-215

Regular
With the vibration monitoring you will be able to attach that sensor to a cricket bat, baseball bat, tennis racket and get instant notification when you hit the sweet spot for training. It will eventually be able to detect a persons emotions and irregular heart beats via a phone.
I wander how professor Barry Marshall is doing these days??
Smart cricket ball
 
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Couldn't recall if posted prev and tbh didn't search.

Wonder how the update mid Dec re Transformers fits?

Is it to do with interface of third party models or to with ours or combination or...??

Might need @Diogenese thoughts if not already provided b4.


Upgrade to akida/cnn2snn 2.2.6 and akida_models 1.1.8​

Latest

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@ktsiknos-brainchip
ktsiknos-brainchip released this Dec 14, 2022
2.2.6-doc-1
d334eea

Update akida and cnn2snn to version 2.2.6

New features​

  • [akida] Upgrade to quantizeml 0.0.13
  • [akida] Attention layer
  • [akida] Identify AKD500 devices
  • [engine] Move mesh scan to host library

API changes​

  • [engine] toggle_learn must be called instead of program(p,learn_enabled)
  • [engine] set_batch_size allows to preallocate inputs

Bug fixes​

  • [engine] Memory can grow indefinitely if queueing is faster than processing

Update akida_models to 1.1.8

  • updated CNN2SNN minimal required version to 2.2.6 and QuantizeML to 0.0.13
  • VWW model and training pipeline refactored and aligned with TinyML
  • Layer names in almost all models have been updated in preparation for quantization with QuantizeML
  • Tabular data models and tools have been removed from the package
  • Transformers pretrained models updated to 4-bits
  • Introduced calibration utils in training toolset
  • KWS and ImageNet training scripts now offer a "calibrate" CLI action
  • ImageNet training script will now automatically restore the best weights after training
 
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Another item maybe or maybe not posted but I haven't seen it myself.

There was a conference Nov last year in Syd and I see a team from BRN did a presso as below.

Haven't tried to search the paper yet.

Appears based on Akidanet models for agri crop / weed ID at the edge.



Screenshot_2023-01-23-12-44-22-87_4641ebc0df1485bf6b47ebd018b5ee76.jpg


Screenshot_2023-01-23-12-44-52-49_4641ebc0df1485bf6b47ebd018b5ee76.jpg
 
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Another item maybe or maybe not posted but I haven't seen it myself.

There was a conference Nov last year in Syd and I see a team from BRN did a presso as below.

Haven't tried to search the paper yet.

Appears based on Akidanet models for agri crop / weed ID at the edge.



View attachment 27769

View attachment 27770
Just found the connection.

From memory I think the award that Vi got was discussed previously which relates back to that presso.



Screenshot_2023-01-23-12-55-31-22_4641ebc0df1485bf6b47ebd018b5ee76.jpg
 
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Vladsblood

Regular
Over one month now and no updates so hopefully they are saving some sweet surprises for the 4C.
Not too much expected though until agm presso.
For what we’re holding in our collective hands we do have an extremely small MC atm,but…when our Board releases our true numbers/positions/partnerships $$$$$ one way is only up many multiples of our minnow MC that we are at currently.
Nice when we are 10x20x40x 60x plus and only maybe 2/3 more years. Worth the waiting for sure. Vlad
 
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Bravo

If ARM was an arm, BRN would be its biceps💪!
Brainchip??? Joining the dots!



Re Snapdragon 8 Gen 2, I find it really interesting that Qualcomm says theres a bunch of architectural changes giving it extra performance boost and per watt improvement and INT 4 will be used a lot for camera functions and the Sensing Hub was given an extra AI processor.

Screen Shot 2023-01-23 at 4.52.45 pm.png


Screen Shot 2023-01-23 at 4.32.57 pm.png

This Qualcomm video on the Sensing Hub 11 Nov 2022 says it can "feel" your footsteps.


 
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VictorG

Member
I don't know but wouldn't be surprised if there is something for Brainchip in this, but if you feel like a little digging, I think I'll leave this here👇
Flex Logix and Brainchip have a few partners in common and their technology would work well together.

InferX™ Inference Accelerator IP​

High Throughput, Low Cost, Low Power​

The InferX AI accelerator IP is in development.
More information coming in Q1 2023.
contact us at: info at flex-logix.com
 
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Nanose was my original reason for buying into Brainchip so it’s great to see this still in the pipeline!





1674458373085.png

 
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wilzy123

Founding Member
Couldn't recall if posted prev and tbh didn't search.

Wonder how the update mid Dec re Transformers fits?

Is it to do with interface of third party models or to with ours or combination or...??

Might need @Diogenese thoughts if not already provided b4.


Upgrade to akida/cnn2snn 2.2.6 and akida_models 1.1.8​

Latest

Compare
@ktsiknos-brainchip
ktsiknos-brainchip released this Dec 14, 2022
2.2.6-doc-1
d334eea

Update akida and cnn2snn to version 2.2.6

New features​

  • [akida] Upgrade to quantizeml 0.0.13
  • [akida] Attention layer
  • [akida] Identify AKD500 devices
  • [engine] Move mesh scan to host library

API changes​

  • [engine] toggle_learn must be called instead of program(p,learn_enabled)
  • [engine] set_batch_size allows to preallocate inputs

Bug fixes​

  • [engine] Memory can grow indefinitely if queueing is faster than processing

Update akida_models to 1.1.8

  • updated CNN2SNN minimal required version to 2.2.6 and QuantizeML to 0.0.13
  • VWW model and training pipeline refactored and aligned with TinyML
  • Layer names in almost all models have been updated in preparation for quantization with QuantizeML
  • Tabular data models and tools have been removed from the package
  • Transformers pretrained models updated to 4-bits
  • Introduced calibration utils in training toolset
  • KWS and ImageNet training scripts now offer a "calibrate" CLI action
  • ImageNet training script will now automatically restore the best weights after training

These github commits are definitely worth watching. They are public and need to genuinely reflect changes in source code, which legitimately hold clues into the ongoings at BRN for those that know what it is they are reading.
 
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cassip

Regular
SP in Germany is up 7,28% at Tradegate (0,4596 € / 0,7163 AUS $)
Volume low, 64k
 
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equanimous

Norse clairvoyant shapeshifter goddess
Food for thought

Multi-compartment Neuron and Population Encoding improved Spiking Neural Network for Deep Distributional Reinforcement Learning​

01/18/2023

by Yinqian Sun, et al.

12

share
Inspired by the information processing with binary spikes in the brain, the spiking neural networks (SNNs) exhibit significant low energy consumption and are more suitable for incorporating multi-scale biological characteristics. Spiking Neurons, as the basic information processing unit of SNNs, are often simplified in most SNNs which only consider LIF point neuron and do not take into account the multi-compartmental structural properties of biological neurons. This limits the computational and learning capabilities of SNNs. In this paper, we proposed a brain-inspired SNN-based deep distributional reinforcement learning algorithm with combination of bio-inspired multi-compartment neuron (MCN) model and population coding method. The proposed multi-compartment neuron built the structure and function of apical dendritic, basal dendritic, and somatic computing compartments to achieve the computational power close to that of biological neurons. Besides, we present an implicit fractional embedding method based on spiking neuron population encoding. We tested our model on Atari games, and the experiment results show that the performance of our model surpasses the vanilla ANN-based FQF model and ANN-SNN conversion method based Spiking-FQF models. The ablation experiments show that the proposed multi-compartment neural model and quantile fraction implicit population spike representation play an important role in realizing SNN-based deep distributional reinforcement learning.

 
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Slade

Top 20
We have partnered with Renesas, Prophesee, MegaChips, Edge Impulse, Intel and ARM. As a shareholder I am more than happy with that. I have a lot of patience when it comes to Intel and ARM. I'm not stressing over the lack of a licensing fee. In fact it would not surprise me if the fee was waived for Intel and ARM. Why pay a license fee for the privilege of helping make Brainchip a huge success. Surely the royalties that roll in for years are the crux of Brainchips' business model. Watching and holding, holding and watching. I will be at the 2024 AGM and buying plenty of rounds after it.
 
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GStocks123

Regular
Check this out- worth a dig through!


I notice Prophesee Gen 4 at the top of vid screen.

 
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GStocks123

Regular
Prophesee web page updated?

Anyone have access to the white paper?
 

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