BRN Discussion Ongoing

[Fact Finder]

Hi Taproot
That interview was one I missed but read an early version of the transcript. As a consequence I missed the slides which is something I will not do again. LOL Very greatful to you for sharing. Many thanks. FF

In return I found this article. It does not relate to AKIDA directly but when thinking about MegaChips and Nintendo it gives some guidance as to where AKIDA technology might go beyond the simple hand controller:

MSN

My opinion only DYOR
FF

AKIDA BALLISTA

Hi Taproot
Given the recent presentation by the CEO Sean Hehir I think it is very useful for everyone invested in or thinking of investing in Brainchip to read again (and watch the slides) or for the first time the full interview with Peter van der Made which you have generously put up but the following extracted quotes very much resonate:

Q: Have any of the EAPs decided not to continue with Akida?

A: All our EAP customers continue to work with us to integrate either the Akida IP or the AKD1000 chip.

(The question of FORD's logo not appearing on Sean Hehir's presentation under the new "Early Adopters" category is clearly answered here.)

Q: What is the timeline for development of Akida 2000.

A: The BrainChip Research Institute are in the process of handing over the prototype of the AKD2000 design to our engineering team, who will then design the silicon. Our engineering resources are currently occupied supporting clients to integrate AKD1000 or the Akida IP. The commercial success of AKD1000 is our focus at present.

(The commitment of the CEO Sean Hehir to the next iteration coming out later this year fits with the above statement and the fact that the recent LDA Capital call included sufficient extra funds as quantified by Ken Scarince in his German Investor presentation to make it happen.)


Q: Can you talk a little bit about the product roadmap and addressing different markets, e.g. with high-margin use cases?

A: In terms of the Akida family of products, we have already released our first generation Akida1000 product commercially, and we are well advanced in the development of the second generation, Akida2000.

Beyond that, we have our third generation Akida3000 currently in development at the BrainChip Research Institute in Perth, and we are looking at several options, including an Akida500, to address the needs of a range of different customer requirements and commercial applications. Some of these products are intended to be commercialised for specific markets (such as radiation hardened chips for use by NASA in space exploration), while others have a multitude of commercial applications across a global market.

The upshot is we believe that this pipeline of new products will keep us at the forefront of the neuromorphic AI sector for years to come, and that’s an exciting opportunity for investors to consider. Given the huge growth already happening in the Edge-AI sector, and the pressure that’s building on all fronts for industry, government, and households to find low-power solutions to help reduce carbon emissions, we believe we are the right company with the right product in the market at the right time.

(The CEO's commitment to ensure the Peter van der Made and Anil Mankar have the financial and human resources to make these things happen is very exciting. The confirmation of the NASA radiation hardened chip also makes clear why NASA and Vorago are listed as Early Adopters with Mercedes and of course VALEO.)

My opinion only DYOR
FF

AKIDA BALLISTA

 

[M_C]

 

[Boab]

I spotted this on Twitter too @Suri3

Everyone here knows it already but worth reiterating:

The commercialisation of our IP is where our easy money will be made. We (hopefully soon) get a very healthy revenue stream via per unit royalties (as per below from Reuters)

And Brainchip does not need to lift a finger. Everybody wins, but especially us

View attachment 1328

For a relative newbie this is gold. Very excited. Cheers

 

[thelittleshort]

Thought I’d found a new discovery here, but upon a google search for more info it turns out our old mate @Fact Finder beat me to it and made the link back on 19/07/21 on HC. Damn he’s good

Anyway that predates my BRN days (unfortunately!) and I’d done the research already so thought I’d continue my post - for two reasons:

1. Share the knowledge with more recent holders who may not have seen on HC
2. Reignite the discussion, as I personally don’t know if it had/has legs. Hopefully FF has something more to add


It’s not a silver bullet but yet another interesting dot joined on our path to Brainchip glory

Milind Joshi is the Intellectual Property Officer at Brainchip - since May 2021

Prior to this Milind spent almost 7 years at Samsung in India - at their R&D Institute

9 months ago he asked on LinkedIn for:

recommendations for patent watch tool that sends email notification for each new patent publication or grant (USPTO and EPO at least) of a competing firm(s)?

More recently he liked this post by Mercedes about the Vision EQXX

Cheers
TLS

 

[Taproot]

Hi Taproot
That interview was one I missed but read an early version of the transcript. As a consequence I missed the slides which is something I will not do again. LOL Very greatful to you for sharing. Many thanks. FF

In return I found this article. It does not relate to AKIDA directly but when thinking about MegaChips and Nintendo it gives some guidance as to where AKIDA technology might go beyond the simple hand controller:

MSN

My opinion only DYOR
FF

AKIDA BALLISTA

Amazing stuff, going to be quite the journey over the next few years.
"Researchers have managed to trigger specific actions in virtual reality environments with facial expressions as inputs"

 

[M_C]

Thought I’d found a new discovery here, but upon a google search for more info it turns out our old mate @Fact Finder beat me to it and made the link back on 19/07/21 on HC. Damn he’s good

Anyway that predates my BRN days (unfortunately!) and I’d done the research so thought I’d continue my post anyway - for two reasons:

1. Share the knowledge with more recent holders who may not have seen on HC
2. Reignite the discussion, as I personally don’t know if it had/has legs. Hopefully FF has something more to add


It’s not a silver bullet but yet another interesting dot joined on our path to Brainchip glory
View attachment 1338

Milind Joshi is the Intellectual Property Officer at Brainchip - since May 2021

Prior to this Milind spent almost 7 years at Samsung in India - at their R&D Institute


View attachment 1340



View attachment 1341
View attachment 1343


9 months ago he asked on LinkedIn for:

recommendations for patent watch tool that sends email notification for each new patent publication or grant (USPTO and EPO at least) of a competing firm(s)?

View attachment 1344


More recently he liked this post about by Mercedes about the Vision EQXX


View attachment 1345

Cheers
TLS

Personally (with my limited understanding of the patent world) I wouldn't be surprised if he was planted by Samsung to assist BRN (and possibly to protect Samsungs interests) so they would be confident that BRNs IP is protected if Samsung are implementing into future products.........Pure speculation of course

 

[Taproot]

Hi Taproot
Given the recent presentation by the CEO Sean Hehir I think it is very useful for everyone invested in or thinking of investing in Brainchip to read again (and watch the slides) or for the first time the full interview with Peter van der Made which you have generously put up but the following extracted quotes very much resonate:

Q: Have any of the EAPs decided not to continue with Akida?

A: All our EAP customers continue to work with us to integrate either the Akida IP or the AKD1000 chip.

(The question of FORD's logo not appearing on Sean Hehir's presentation under the new "Early Adopters" category is clearly answered here.)

Q: What is the timeline for development of Akida 2000.

A: The BrainChip Research Institute are in the process of handing over the prototype of the AKD2000 design to our engineering team, who will then design the silicon. Our engineering resources are currently occupied supporting clients to integrate AKD1000 or the Akida IP. The commercial success of AKD1000 is our focus at present.

(The commitment of the CEO Sean Hehir to the next iteration coming out later this year fits with the above statement and the fact that the recent LDA Capital call included sufficient extra funds as quantified by Ken Scarince in his German Investor presentation to make it happen.)


Q: Can you talk a little bit about the product roadmap and addressing different markets, e.g. with high-margin use cases?

A: In terms of the Akida family of products, we have already released our first generation Akida1000 product commercially, and we are well advanced in the development of the second generation, Akida2000.

Beyond that, we have our third generation Akida3000 currently in development at the BrainChip Research Institute in Perth, and we are looking at several options, including an Akida500, to address the needs of a range of different customer requirements and commercial applications. Some of these products are intended to be commercialised for specific markets (such as radiation hardened chips for use by NASA in space exploration), while others have a multitude of commercial applications across a global market.

The upshot is we believe that this pipeline of new products will keep us at the forefront of the neuromorphic AI sector for years to come, and that’s an exciting opportunity for investors to consider. Given the huge growth already happening in the Edge-AI sector, and the pressure that’s building on all fronts for industry, government, and households to find low-power solutions to help reduce carbon emissions, we believe we are the right company with the right product in the market at the right time.

(The CEO's commitment to ensure the Peter van der Made and Anil Mankar have the financial and human resources to make these things happen is very exciting. The confirmation of the NASA radiation hardened chip also makes clear why NASA and Vorago are listed as Early Adopters with Mercedes and of course VALEO.)

My opinion only DYOR
FF

AKIDA BALLISTA

The upcoming challenge, will be trying to identify products that have been developed by Brainchip's current and future licencees.
Undoubtedly some stunning new products to hit the market.

 

[M_C]

lol maybe someone should tell them they're wasting their time

https://www.quantamagazine.org/ai-overcomes-stumbling-block-on-brain-inspired-hardware-20220217/

 

[Diogenese]

View attachment 1346

 

[M_C]

View attachment 1360

 

[rgupta]

Brain-Inspired Chips Are Good for More than AI,

Brain-inspired "neuromorphic" microchips from IBM and Intel might be good for more than just artificial intelligence; they may also prove ideal for a class of computations useful in a broad range of applications including analysis of medical X-rays and financial economics, a new study finds.

spectrum.ieee.org

Another article on SNN. I don't understand while living in 21st century how people are so I'll informed. They are still talking about true north and loihi while Akira is available right now with much better ease and configurations.

 

[IndepthDiver]

Why did Mercedes announce they were working with us in January this year? I'm sure others on here have thought about this. They had no pressure to announce this and by not announcing this (at least until it was already inside commercial vehicles, if ever) they would have had potential to greatly extend any market lead. They could have simply said they were using advanced AI technologies to reduce power draw without mentioning Brainchip.

Here's what I think:
-The CEO Sean started in November. He has two main focusses: extending the market lead and getting the word out about Brainchip.
-Before January there hadn't been new customer announcements for a while. No customers wanted to say they were working with Brainchip as the tech is a key differentiator. The suddenly there were two, namely Mercedes and ISL. I don't think this is a coincidence.
-Sean is very limited about what he can talk about in his NDAs. This makes it difficult to promote Brainchip
-In Sean's first few public appearances he would likely have wanted to project a vibe of a highly successful, top tier technology. As mentioned above the NDAs keep getting in the way so he needed a way around this.
-Maintaining strong investor interest is crucial as a higher share price means higher valuation, greater international a credibility, and an increased ability to do things like strategic acquisitions. Again NDAs have been limiting this and affecting some investor decisions.
-In Sean's most recent presentation he said he would be negotiating with other companies to have the ability to display their logos. This indicates they won't necessarily say what they're working on with the customer, even just the logo would have a big impact. ISL was probably negotiated with NASA as a non-lethal defense application (non-lethal being critical for Brainchip's image). Using Mercedes as an example, they've probably only negotiated the ability to talk about the voice recognition system (a fairly basic / common use case). We probably won't hear about other things they are working on, especially if it's something big like Level 4 or 5 autonomous driving.
-Negotiating with a company means that whatever the outcome, it should work in both companies favour. For Mercedes to agree to announce Brainchip, they likely would have got something in return. This could be something like more engineering support, a discount on licensing fees, lower royalty fees, or the earliest access to AKD2000 etc.
-This desire to negotiate the ability to display EAP logos is probably being strongly driven by Sean. Brainchip are at a stage where they need to carefully balance increased investor interest with the revenue stream and the cash drain from increased hires.
-I think Sean said something in the Q&A about how he will only announce material contracts if the customer allows it. I think he'll try to push for a few more public announcements this year as it aligns strongly with his strategy. Valeo is a strong contender for this with their Lidar IMO. Early announcements will help project his image as talented and successful CEO. Though he may try space these announcements out to ensure regular news flow to investors and to maintain interest but without impacting on negotiations too heavily.

Also of interest is how in the recent interview Sean confirmed Megachips / Renesas are being used to hide which companies are using Brainchip IP (my words). This confirms what FF had been suggesting for quite some time.

Also worth mentioning is how the CEO is less forthcoming about NDAs than others in the company. I think this will become more of the norm in future Brainchip presentations, including those by other presenters going forward as Sean will push for this. He needs customers to trust he runs a tight ship and seems to be setting a high bar.

Pure speculation, DYOR

 

[Diogenese]

Interesting article from Democritus University which I'm not sure if it's been posted previously. We know they have worked with both Brainchip and NASA. Now they are looking into nanophotonic neural networks. They researched what has been done for multiple neural network types including CNN's and SNN's, This article was written by the same authors many on this forum will remember:
View attachment 1334

A Comprehensive Survey on Nanophotonic Neural Networks: Architectures, Training Methods, Optimization, and Activations Functions

In the last years, materializations of neuromorphic circuits based on nanophotonic arrangements have been proposed, which contain complete optical circuits, laser, photodetectors, photonic crystals, optical fibers, flat waveguides and other passive optical elements of nanostructured materials...

www.mdpi.com

Published: 18 January 2022

Here are some relevant / interesting parts:

4.5. Spiking Neural Networks​

The spiking neural networks (SNNs) [76,77,78] are networks that imitate more than any other the biological ΝΝs. Apart from the neural and synaptic condition, the SNNs incorporate the concept of time in their operating model. The idea behind this is that the neurons in a SNN should not trigger and be triggered in every propagation circle, as in standard networks of multiple layers with perceptron’s. As it happens with the biological neurons, when the dynamics of their cell membrane reaches a particular value, which is called action potential, then the neuron triggers and produces a signal that travels to other neurons, which, in turn, increase or decrease the dynamics of their cell membrane according to this particular signal. The SNNs use peak sequences as mechanisms of internal information presentation, in contrast to the usual continuous variables, while at the same time having equal, if not better, performance in computational cost to the traditional NNs [79,80,81].
In the field of optical SNNs, many studies have been conducted in the past years [82,83], initially taking advantage of the fast optical elements used in the construction of big systems with optical fibers. Despite the significant advances to build active optical artificial neurons using for example phase-change materials, lasers, photodetectors and modulators, miniaturized integrated sources and detectors suited for few-photon spike-based operations and of interest for neuromorphic optical computing are still lacking. The successful applications finally led to the completion of arrangements, aiming for greater scalability, increase of energy efficiency, reduction of cost and flexibility in the environmental fluctuations.
In a survey, the use of a graphene laser is recommended as an artificial neuron, which is the fundamental element for the processing of information in the form of spikes. Moreover, the integrated layer of graphene is used as an optical absorber for the materialization of the non-linear activation function. The following Figure 10 presents the application with the use of circuits of free optics for the creation of a series of current peaks with adjustable characteristics of width and breadth [49,82,84,85].

Figure 10. (a) The circuit for the creation of repeated current peak. (b) The waveforms of the implementation. One pulse of the output is led to the input via single-mode fiber (SMF), which acts as a delay element [82].
In another survey, the fundamental neuron is based on distributed feedback (DFB) laser of semi-conductors of indium phosphide [86]. The use of this type of laser devices is very common in the construction of SNNs. The laser possesses two photodetectors (PD), which allow for inhibitory as well as excitatory stimuli. The recommended device is very fast, reaching 1012 MACs/sec (MAC—Multiply Accumulate Operations) [87,88].

7. Conclusions​

In this research paper, we present an overview of the development and materialization methods of neuromorphic circuits of nanophotonic [61] arrangements for every respective contemporary architecture of conventional neural networks, and the advantages and restrictions that arise during the transition from the electronic to the optical materializations are displayed. The aforementioned networks are energy efficient, when compared to the corresponding electronic ones, and much faster due to photons. The reduction of simultaneous processing time radically increases the potentials of modern computational systems, which use optical arrangements, offering a promising alternative approach to micro-electronic and optical-electronic applications.
All these lead to the conclusion that there are potentials for a full transition to optical materializations as these display the following advantages:
(1)
Most of the systems do not require energy for the processing of optical signals. As soon as the neural network is trained, the computations on the optical signals are conducted without any additional energy consumption, rendering this particular architecture completely passive.
(2)
The optical systems, in contrast to the conventional electronic ones, do not produce heat during their operation and, as a result, they can be enclosed in three-dimensional constructions.

Hi IDD,

Haven't read the whole article, but commercialization of nano-optical NNs seems to be some way off:

To summarize, nanophotonics are more expensive and harder to fix, and waveguides and fibers are harder to use than wires and are characterized by spurious reflections that are more troublesome.
Although there are potentials concerning the materialization of PNNs, there are still some areas that require further research, such as some specific architectures of deep neural nets, specifically Long Short-Term Memory Neural Networks, Generative Adversarial Nets, Geometric Deep Neural Networks, Deep Belief Networks and Deep Boltzmann Machines. Due to the significance of DNNs and the role they play in mechanical learning techniques, the research studies should focus on the question whether every type of conventional DNN can be converted in PNN, performing better and, thus, offering more advantages when compared to electronic arrangements. The ultimate goal in this is to replace the huge energy-consuming NNs, with thousands of knots and multiple interconnections among hidden layers, with very fast optical arrangements.
There are also fields where the research on PNNs should focus on, such as the hyper dimensional learning (HL) [118,119], a modern and very promising approach to NNs, which is still in the development stage. Here, the problem of a photonic materialization lies in the very big size of the internal representation of objects that are used in HL.
A further point that needs to be studied is the application of non-linear functions, which in most of the suggestions are materialized through software outside the optical arrangement. This results in the decline of performance, sometimes of a high rate, given that in multilayer NNs it is necessary to insert non-linearity many times successively.
Many more challenges need to be overcome, such as the many different hardware platforms that have been recommended, which are still under investigation with no clear winner yet. Moreover, we have to improve the already developed hardware as, in many cases, basic elements are still simulated, or classic electronic ones are used. Furthermore, a critical element in a recommended NN architecture is its expandability in various applications, something that must be confirmed with further research studies. Finally, the field of NNs, which is still in early stage, is the massive integration of optical arrangements and, of course, their mass production, which is the last and most fundamental fortress of conventional NN arrangements against the transition to fully optical circuits.

 

[IndepthDiver]

Hi IDD,

Haven't read the whole article, but commercialization of nano-optical NNs seems to be some way off:

To summarize, nanophotonics are more expensive and harder to fix, and waveguides and fibers are harder to use than wires and are characterized by spurious reflections that are more troublesome.
Although there are potentials concerning the materialization of PNNs, there are still some areas that require further research, such as some specific architectures of deep neural nets, specifically Long Short-Term Memory Neural Networks, Generative Adversarial Nets, Geometric Deep Neural Networks, Deep Belief Networks and Deep Boltzmann Machines. Due to the significance of DNNs and the role they play in mechanical learning techniques, the research studies should focus on the question whether every type of conventional DNN can be converted in PNN, performing better and, thus, offering more advantages when compared to electronic arrangements. The ultimate goal in this is to replace the huge energy-consuming NNs, with thousands of knots and multiple interconnections among hidden layers, with very fast optical arrangements.
There are also fields where the research on PNNs should focus on, such as the hyper dimensional learning (HL) [118,119], a modern and very promising approach to NNs, which is still in the development stage. Here, the problem of a photonic materialization lies in the very big size of the internal representation of objects that are used in HL.
A further point that needs to be studied is the application of non-linear functions, which in most of the suggestions are materialized through software outside the optical arrangement. This results in the decline of performance, sometimes of a high rate, given that in multilayer NNs it is necessary to insert non-linearity many times successively.
Many more challenges need to be overcome, such as the many different hardware platforms that have been recommended, which are still under investigation with no clear winner yet. Moreover, we have to improve the already developed hardware as, in many cases, basic elements are still simulated, or classic electronic ones are used. Furthermore, a critical element in a recommended NN architecture is its expandability in various applications, something that must be confirmed with further research studies. Finally, the field of NNs, which is still in early stage, is the massive integration of optical arrangements and, of course, their mass production, which is the last and most fundamental fortress of conventional NN arrangements against the transition to fully optical circuits.

Hi D,

Fully agree it's a while away. I like to think that the researchers from DUTH are frequently looking at the cutting edge of neuromorphic technologies (eg. SNN cybersecurity algorithm, SNN for robotic fish or remote environmental monitoring).
I think it's interesting that they were the researchers looking into this given their Brainchip and NASA connections.

 

[JK200SX]

I spotted this on Twitter too @Suri3

Everyone here knows it already but worth reiterating:

The commercialisation of our IP is where our easy money will be made. We (hopefully soon) get a very healthy revenue stream via per unit royalties (as per below from Reuters)

And Brainchip does not need to lift a finger. Everybody wins, but especially us

View attachment 1328

The use cases in the devices/markets shown above are going to be in the multi millions.
As a comparison, i remember when I worked at continental we used chips from Renesas on all the pcb's we manufactured that went into products for the Australian car market. At the peak we were making components for about 130,000-150,000 cars each for Toyota Camry, Ford Falcon and Holden Commodore. That was 450,000 pieces of one IC alone for one year (ie one particular use case only). Continental at the time had ~70 manufacturing facilities around the world that had some sort of manufacturing capability. So you do the maths and you can quickly see how many AKIDA IC's or IP licenses can be used just alone in one industry, year on year. Multiply that figure by~$15 a pop, and that would put a very large smile on your face.

 

[Fact Finder]

The following paper from 2019 is interesting because of the authors' connections to Valeo and the fact that it is exploring the advantages of SNN for autonomous driving. It seems a little familiar and so it may have been posted or I may have found it previously. Given that our research led to the belief that Valeo and Brainchip were working together from as early as 2018 nothing is lost by it being double posted. I cannot access the full article unfortunately.

My opinion only DYOR
FF

AKIDA BALLISTA:

Paper​

Exploring Deep Spiking Neural Networks for Automated Driving Applications

Topics: Deep Learning for Visual Understanding ; Machine Learning Technologies for Vision

In Proceedings of the 14th International Joint Conference on Computer Vision, Imaging and Computer Graphics Theory and Applications - Volume 5: VISAPP, 548-555, 2019 , Prague, Czech Republic

PrevNext

Authors: Sambit Mohapatra 1 ; Heinrich Gotzig 1 ; Senthil Yogamani 2 ; Stefan Milz 3 and Raoul Zöllner 4
Affiliations: 1 Valeo Bietigheim and Germany ; 2 Valeo Vision Systems and Ireland ; 3 Valeo Kronach and Germany ; 4 Heilbronn University and Germany

Keyword(s): Visual Perception, Efficient Networks, Automated Driving.

Abstract: Neural networks have become the standard model for various computer vision tasks in automated driving including semantic segmentation, moving object detection, depth estimation, visual odometry, etc. The main flavors of neural networks which are used commonly are convolutional (CNN) and recurrent (RNN). In spite of rapid progress in embedded processors, power consumption and cost is still a bottleneck. Spiking Neural Networks (SNNs) are gradually progressing to achieve low-power event-driven hardware architecture which has a potential for high efficiency. In this paper, we explore the role of deep spiking neural networks (SNN) for automated driving applications. We provide an overview of progress on SNN and argue how it can be a good fit for automated driving applications.

 

[Vojnovic]

The following paper from 2019 is interesting because of the authors' connections to Valeo and the fact that it is exploring the advantages of SNN for autonomous driving. It seems a little familiar and so it may have been posted or I may have found it previously. Given that our research led to the belief that Valeo and Brainchip were working together from as early as 2018 nothing is lost by it being double posted. I cannot access the full article unfortunately.

My opinion only DYOR
FF

AKIDA BALLISTA:

Paper​

Exploring Deep Spiking Neural Networks for Automated Driving Applications

Topics: Deep Learning for Visual Understanding ; Machine Learning Technologies for Vision

In Proceedings of the 14th International Joint Conference on Computer Vision, Imaging and Computer Graphics Theory and Applications - Volume 5: VISAPP, 548-555, 2019 , Prague, Czech Republic

PrevNext

Authors: Sambit Mohapatra 1 ; Heinrich Gotzig 1 ; Senthil Yogamani 2 ; Stefan Milz 3 and Raoul Zöllner 4
Affiliations: 1 Valeo Bietigheim and Germany ; 2 Valeo Vision Systems and Ireland ; 3 Valeo Kronach and Germany ; 4 Heilbronn University and Germany

Keyword(s): Visual Perception, Efficient Networks, Automated Driving.

Abstract: Neural networks have become the standard model for various computer vision tasks in automated driving including semantic segmentation, moving object detection, depth estimation, visual odometry, etc. The main flavors of neural networks which are used commonly are convolutional (CNN) and recurrent (RNN). In spite of rapid progress in embedded processors, power consumption and cost is still a bottleneck. Spiking Neural Networks (SNNs) are gradually progressing to achieve low-power event-driven hardware architecture which has a potential for high efficiency. In this paper, we explore the role of deep spiking neural networks (SNN) for automated driving applications. We provide an overview of progress on SNN and argue how it can be a good fit for automated driving applications.

 

[Fact Finder]

Many thanks Vojnovic greatly appreciated. It is a useful paper but what stood out for me was the following paragraphs which fits very nicely with the hypothesis that Valeo's new Lidar uses the AKIDA SNN technology:

"Point Cloud: Light Detection and Ranging (LiDAR)
sensors have recently gained prominence as state of
the art sensors in sensing the environment. They
produce a 3D representation of the objects in the field of
view as distances of points from the source. This collection
of points over a 3D space is called a 3D Point Cloud.
Though cameras have been used for a long time and
they provide a more direct representation of the surrounding,
LiDARs have gained ground because of some critical advantages
such as long range, robustness to ambient light conditions and
accurate localization of objects in 3D space. They produce sparse
data and hence suitable for SNNs....

We summarize the key benefits of SNN for automated driving:

• Event driven mechanism which brings adaptation for different scenarios.

• Low power consumption when realized as neuromorphic hardware.

• Simpler learning algorithm which leads to possibility of on-chip learning for longer term adaptation.

• Ability to integrate directly to analog signals leading to tightly integrated system.

• Lower latency in algorithm pipeline which is important for high speed braking and maneuvering.

4 Conclusion
Spiking Neural Networks (SNN) are biologically inspired
where the neuronal activity is sparse and event driven in order to
optimize power consumption. In this paper, we provide an overview of
SNN and compare it with CNN and argue how it can be useful in
automated driving systems. Overall power consumption over the driving
cycle is a critical constraint which has to be efficiently used especially for -(Remember what Mercedes said 6 to 10 times more efficient)
electric vehicles. Event driven architectures for various scenarios in
automated driving can also have accuracy advantages."

My opinion only DYOR
FF

AKIDA BALLISTA

 

[butcherano]

Why did Mercedes announce they were working with us in January this year? I'm sure others on here have thought about this. They had no pressure to announce this and by not announcing this (at least until it was already inside commercial vehicles, if ever) they would have had potential to greatly extend any market lead. They could have simply said they were using advanced AI technologies to reduce power draw without mentioning Brainchip.

Here's what I think:
-The CEO Sean started in November. He has two main focusses: extending the market lead and getting the word out about Brainchip.
-Before January there hadn't been new customer announcements for a while. No customers wanted to say they were working with Brainchip as the tech is a key differentiator. The suddenly there were two, namely Mercedes and ISL. I don't think this is a coincidence.
-Sean is very limited about what he can talk about in his NDAs. This makes it difficult to promote Brainchip
-In Sean's first few public appearances he would likely have wanted to project a vibe of a highly successful, top tier technology. As mentioned above the NDAs keep getting in the way so he needed a way around this.
-Maintaining strong investor interest is crucial as a higher share price means higher valuation, greater international a credibility, and an increased ability to do things like strategic acquisitions. Again NDAs have been limiting this and affecting some investor decisions.
-In Sean's most recent presentation he said he would be negotiating with other companies to have the ability to display their logos. This indicates they won't necessarily say what they're working on with the customer, even just the logo would have a big impact. ISL was probably negotiated with NASA as a non-lethal defense application (non-lethal being critical for Brainchip's image). Using Mercedes as an example, they've probably only negotiated the ability to talk about the voice recognition system (a fairly basic / common use case). We probably won't hear about other things they are working on, especially if it's something big like Level 4 or 5 autonomous driving.
-Negotiating with a company means that whatever the outcome, it should work in both companies favour. For Mercedes to agree to announce Brainchip, they likely would have got something in return. This could be something like more engineering support, a discount on licensing fees, lower royalty fees, or the earliest access to AKD2000 etc.
-This desire to negotiate the ability to display EAP logos is probably being strongly driven by Sean. Brainchip are at a stage where they need to carefully balance increased investor interest with the revenue stream and the cash drain from increased hires.
-I think Sean said something in the Q&A about how he will only announce material contracts if the customer allows it. I think he'll try to push for a few more public announcements this year as it aligns strongly with his strategy. Valeo is a strong contender for this with their Lidar IMO. Early announcements will help project his image as talented and successful CEO. Though he may try space these announcements out to ensure regular news flow to investors and to maintain interest but without impacting on negotiations too heavily.

Also of interest is how in the recent interview Sean confirmed Megachips / Renesas are being used to hide which companies are using Brainchip IP (my words). This confirms what FF had been suggesting for quite some time.

Also worth mentioning is how the CEO is less forthcoming about NDAs than others in the company. I think this will become more of the norm in future Brainchip presentations, including those by other presenters going forward as Sean will push for this. He needs customers to trust he runs a tight ship and seems to be setting a high bar.

Pure speculation, DYOR

G’day @IndepthDiver . Great post. But I actually landed on only 2 real drivers behind Mercedes revealing themselves.

The first is based on the assumption that it’s well known that we’re working with a number of other automotive companies. And Mercedes simply wanted to ensure that they become renowned as the innovators and the rest as just followers.

And the second hinges on the competitive advantage and they’re trying to throw other competitors off the scent (that aren’t woking with Brainchip) to give the impression that Akida is only being used for voice recognition or “Hey Mercedes“…and not the plethora of other applications that we know Akida can and most likely is doing in the EQXX.

 

[butcherano]

G’day @IndepthDiver . Great post. But I actually landed on only 2 real drivers behind Mercedes revealing themselves.

The first is based on the assumption that it’s well known that we’re working with a number of other automotive companies. And Mercedes simply wanted to ensure that they become renowned as the innovators and the rest as just followers.

And the second hinges on the competitive advantage and they’re trying to throw other competitors off the scent (that aren’t woking with Brainchip) to give the impression that Akida is only being used for voice recognition or “Hey Mercedes“…and not the plethora of other applications that we know Akida can and most likely is doing in the EQXX.

It was actually this reply on Twitter that got me thinking that there are probably a lot more people that haven’t quite grasped what we’re capable of….. So if it worked on him then maybe our competitors are in the same boat.