Is it likely RTX would buy a pig in a poke?
If RTX have agreed to take payment in BRN shares, I see this as a vote of confidence.
Agree:-
BRN Discussion Ongoing
- Thread starter TechGirl
- Start date
If RTX have agreed to take payment in BRN shares, I see this as a vote of confidence.
Agree:-
Bravo
If ARM was an arm, BRN would be its biceps💪!
Tata Elxsi - Tata Elxsi and Infineon Partner to Accelerate EV Innovation in India
Tata Elxsiwww.tataelxsi.com
View attachment 87288
Tata Elxsi, a global leader in design and technology services, and Infineon Technologies, a global leader in semiconductor solutions, have signed a Memorandum of Understanding (MoU) to jointly… | Tata Elxsi
Tata Elxsi, a global leader in design and technology services, and Infineon Technologies, a global leader in semiconductor solutions, have signed a Memorandum of Understanding (MoU) to jointly develop application-ready EV solutions tailored to the Indian market. “At Infineon, we are committed...www.linkedin.com
Hopefully this MoU increases the likelihood of Infineon engaging with BrainChip either directly, or indirectly through an expanded collaboration with Tata Elxsi.
Tata Elxsi and BrainChip first announced a partnership in 2022 to bring Akida-based edge AI solutions to market, with a focus on autonomous vehicles, industrial automation, and health tech.
This new MoU between Tata Elxsi and Infineon centers around the joint development of EV platforms.
Infineon’s hardware portfolio aligns extremely well with BrainChip’s value proposition. As a global leader in automotive microcontrollers (MCUs), radar sensors, and safety semiconductors, Infineon plays a critical role in emerging mobility platforms but typically relies on third-party or internal AI accelerators for signal processing and decision-making.
Infineon are actively involved in applications such as:
- Radar-based gesture recognition
- Driver monitoring systems
- Automotive safety and automation
- Sensor fusion and AI processing at the edge
Infineon already collaborates with the likes of Cerence, whom I am very interested in. For example, Infineon’s sensors enable a number of Cerence’s safety and comfort features, several of which could materially benefit from BrainChip’s TENNs technology as a front-end edge processor. These include:
- Child and occupant presence detection
- Driver drowsiness and vital sign monitoring
- Touchless gesture control
- Proximity and contextual awareness
- Health and emergency event detection
- Tata Elxsi delivers system-level AI and vehicle integration
- Infineon supplies power electronics, safety, and sensing hardware
- BrainChip provides the embedded intelligence and signal processing core
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smoothsailing18
Regular
Can anyone purchase the following now if not is there a time line for release to market ?
Previously there was I believe also talk of a sim card which could contain Akida , I might be wrong thinking this however what a great product to produce if only the sim could be upgraded to existing WiFi hardware.
Previously there was I believe also talk of a sim card which could contain Akida , I might be wrong thinking this however what a great product to produce if only the sim could be upgraded to existing WiFi hardware.
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Pom down under
Top 20
Probably the most ever positive announcement regarding unquoted securities and I guess it will stop us doing another LDA agreement for a few months
Fullmoonfever
Top 20
Well, kinda sucks we didn't get the Ann this morning but you'd like to think once they're awake and "working" in the US we might get it late today or tomoz morn?
Anyway, additional details and drawings have now been added to the initial publication I spotted over the weekend.
@Diogenese given the references to Pleiades and Gen AI would it be fair to conclude this patent is around the aforementioned and LLM inroads for the Edge? I see Rudy also one of the inventors so appears likely to me.
[0004] Recently different methods have been explored to generate AI-based content using the aforementioned NNs. It is critical for some applications for a neural network to be able to generate content. The neural networks that are capable of generating the AI-based content are called generative models or generative networks. One of the commonly known generative networks is RNNs and the other commonly known generative networks are transformers. The RNNs were traditionally used to generate the content before the emergence of the transformers. Transformers and variants have been at the basis of the advances in generative models, particularly in the domain of large language models (LLMs).
[0005] For each of the above-discussed NN models including ANN, CNN, and RNN, the computation process is very often performed in the cloud for generating the content. However, in order to have a better user experience and privacy, and for various commercial reasons, an implementation of the computation process has started moving from the cloud to edge devices. In order to generate AI-based content, there are mainly two solutions available in the state of the art i.e., RNNs and transformers. However, RNNs are difficult to train because of the recurrence they take more time to train. Transformers generate content without having to make use of recurrence, which permits parallelized training. The transformers are capable of being trained efficiently in the cloud by leveraging Graphics Processing Unit (GPU) or Tensor Processing Unit (TPU) for parallel computation.
[0006] Further, with the increasing complexity of the NN models, there is a corresponding increase in the computational requirements required to execute highly complex NN models, for example, the transformer based models. Thus, a huge computational processing and a large memory are required for executing highly complex transformer based models.
[0007] Thus, there lies a need for a method and system to reduce the computational requirements of the above-discussed NN models while still meeting desired accuracy expectations, in order to facilitate more efficient content generation, particularly for the edge devices.
SUMMARY
[0008] This summary is provided to introduce a selection of concepts in a simplified form that is further described below in the Detailed Description section. This summary is not intended to identify or exclude key features or essential features of the claimed subject matter, nor is it intended to be used as an aid in determining the scope of the claimed subject matter.
[0009] For generating content, the neurons of NN models, such as polynomial expansion in adaptive distributed event-based systems (PLEAIDES) models, perform a temporal convolution operation on input signals with the temporal kernels. In some aspects, the temporal kernels are represented as an expansion over a basis function with kernel coefficients Gi . In some aspects, the kernel coefficients are trainable parameters of the neural network during learning. In some aspects, the kernel coefficients remain constant while generating content using convolutions during inference. Even though the recurrent mode of PLEIADES decomposes the convolution with a kernel onto a set of basis functions, the contribution from each may not be used individually, but summed together to provide a scalar value of the convolution. Such a scalar value has more limited power in generating signals than if a contribution, coefficient, from each basis could be used
smoothsailing18
Regular
Food4 thought
Regular
I am having a really dark day and feel pissed about the whole thing.
Usually I can roll with the everyday ups and downs but not today.
Could someone please give me some advice or positive thoughts
Usually I can roll with the everyday ups and downs but not today.
Could someone please give me some advice or positive thoughts
Food4 thought
Regular
Hey
What is the point of being on the ASX.
I thought that a company was supposed to keep its shares holders informed and up to date
Basically report to its shareholders, when are they going to do so
They said they were going to do better at the shame of a meeting 3 months ago but nothing
Nothing worthy of an announcement.
Great job Brain poop
What is the point of being on the ASX.
I thought that a company was supposed to keep its shares holders informed and up to date
Basically report to its shareholders, when are they going to do so
They said they were going to do better at the shame of a meeting 3 months ago but nothing
Nothing worthy of an announcement.
Great job Brain poop
smoothsailing18
Regular
Your alive and not living in the Gaza
My questioning of their earlier collaboration was confirmed as having ended in 2022. FF also confirmed it so it must be true.
Bravo
If ARM was an arm, BRN would be its biceps💪!
You're not alone Food4thought. We've probably all had moments when the weight of this incredibly looooooooooooooooooooong journey just cant help but get the better of us. It’s okay to feel frustrated. This isn’t just numbers on a screen - it's a real personal investment.
Behind the scenes BrainChip is executing on a very deep-tech roadmap, not a gimmick.
Tony’s recent LinkedIn post about applying state-space models (TENNs) directly to raw radar - skipping image conversion and drastically reducing latency and power - has given me renewed hope that BrainChip is leading with novel architecture in what is a very disruptive field. This could have huge implications not just in defense, but also in automotive radar, medical edge AI, and industrial safety systems as has been discussed to day.
Patents, the roadmap, our partner ecosystem and strategic positioning matter and BrainChip appears to be checking all of those boxes steadily, albeit it much more quietly than many of us would like.
I'm sure that our patience will rewarded - hopefully at some point in the not too distant future.
B
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Food4 thought
Regular
It would seem that we don’t actually know who has taken payment of the shares
Do we ???
Food4 thought
Regular
Thanks mateYou're not alone Food4thought. We've probably all had moments when the weight of this incredibly looooooooooooooooooooong journey just cant help but get the better of us. It’s okay to feel frustrated. This isn’t just numbers on a screen - it's a real personal investment.
Behind the scenes BrainChip is executing on a very deep-tech roadmap, not a gimmick.
Tony’s recent LinkedIn post about applying state-space models (TENNs) directly to raw radar - skipping image conversion and drastically reducing latency and power - has given me renewed hope that BrainChip is leading with novel architecture in what is a very disruptive field. This could have huge implications not just in defense, but also in automotive radar, medical edge AI, and industrial safety systems as has been discussed to day.
Patents, the roadmap, our partner ecosystem and strategic positioning matter and BrainChip appears to be checking all of those boxes steadily, albeit it much more quietly than many of us would like.
I'm sure that our patience will rewarded - hopefully at some point in the not too distant future.
B
You are a legend
Well my truancy has caught up with me. I was away the day we did Pleaides.
The trick with patents is that the claims define the invention:
1 . A neural network system, comprising:
an input interface configured to receive sequential data that includes input data sequences;
a memory configured to store a plurality of group of first basis function values, a first plurality of storage buffers corresponding to a current neural network layer, and implement a neural network that includes a first plurality of neurons for the current neural network layer, a corresponding group among the plurality of groups of the first basis function values is associated with each connection of a corresponding neuron of the first plurality of neurons;
a processor, to perform a projection operation, configured to:
allocate the first plurality of storage buffers to a first group of neurons among the first plurality of neurons;
receive, from corresponding input data sequences over a first sequence window, a first input data sequence of the corresponding input data sequences into the first plurality of storage buffers allocated to the first group of neurons;
project the first input data sequence on a corresponding basis function values among the corresponding group of the first basis function values by performing, for each connection of a corresponding neuron of the first group of neurons, a first dot product of the first input data sequence of the corresponding input data sequences within a corresponding storage buffer of first plurality of storage buffers with the corresponding basis function values, wherein the corresponding basis function values is associated with a corresponding connection of the corresponding neuron of the first group of neurons;
determine a corresponding potential value for the corresponding neurons of the first group of neurons based on the performed first dot product; and
generate a plurality of encoded output responses based on the determined corresponding potential values.
This is definitely a case where a picture is worth 1000 words, (I hope you are all paying attention because I shall say this only once):
[0102] FIG. 4 A illustrates an overall method for encoder projection operations according to some embodiments. In some embodiments, the encoder projection operations may be implemented in system 300 . In a non-limiting example, the system 300 may include an encoder network, and a decoder network or a generator network. In some embodiments, the input signal is projected onto each basis function values of a set of basis functions in each neural network layer to generate corresponding projection coefficients. In a non-limiting example, three sets of basis functions are considered for the sake of brevity. However, the number of basis functions may be more than three and depends on the required application. Accordingly, when the input signal is projected onto the three basis function values derived from corresponding basis functions by performing a dot product of the input signal with each of the basis function values, three corresponding projection coefficients are generated, that is to say, Ci (t)={C1 (t), C2 (t), C3 (t), . . . } are generated respectively. The generated projection coefficients are then multiplied by a corresponding multiplying kernel gain factor Gi (t)={G1 (t), G2 (t), G3 (t), . . . }. Further, each of the corresponding resultant outputs is then passed through a corresponding non-linear activation function. Accordingly, for the encoder projection operations in the buffer mode, a plurality of encoded output responses are generated based on the performed dot product. In a non-limiting example, the plurality of output responses is shown as {tilde over (C)}i (t)={{tilde over (C)}1 (t), {tilde over (C)}2 (t), {tilde over (C)}3 (t), . . . }. Accordingly, the activity within the neural network represents the projection coefficients, or a nonlinear transformation of the projection coefficients. Thus, the projection coefficients are not constants but depend on time, even during the inference operations. Further, contrary to the temporal convolution operation that is performed in FIG. 3 A and FIG. 3 B, the convolution operation is replaced by a projection operation, thereby outputting a vector output rather than a scalar output.
The trick with patents is that the claims define the invention:
1 . A neural network system, comprising:
an input interface configured to receive sequential data that includes input data sequences;
a memory configured to store a plurality of group of first basis function values, a first plurality of storage buffers corresponding to a current neural network layer, and implement a neural network that includes a first plurality of neurons for the current neural network layer, a corresponding group among the plurality of groups of the first basis function values is associated with each connection of a corresponding neuron of the first plurality of neurons;
a processor, to perform a projection operation, configured to:
allocate the first plurality of storage buffers to a first group of neurons among the first plurality of neurons;
receive, from corresponding input data sequences over a first sequence window, a first input data sequence of the corresponding input data sequences into the first plurality of storage buffers allocated to the first group of neurons;
project the first input data sequence on a corresponding basis function values among the corresponding group of the first basis function values by performing, for each connection of a corresponding neuron of the first group of neurons, a first dot product of the first input data sequence of the corresponding input data sequences within a corresponding storage buffer of first plurality of storage buffers with the corresponding basis function values, wherein the corresponding basis function values is associated with a corresponding connection of the corresponding neuron of the first group of neurons;
determine a corresponding potential value for the corresponding neurons of the first group of neurons based on the performed first dot product; and
generate a plurality of encoded output responses based on the determined corresponding potential values.
This is definitely a case where a picture is worth 1000 words, (I hope you are all paying attention because I shall say this only once):
[0102] FIG. 4 A illustrates an overall method for encoder projection operations according to some embodiments. In some embodiments, the encoder projection operations may be implemented in system 300 . In a non-limiting example, the system 300 may include an encoder network, and a decoder network or a generator network. In some embodiments, the input signal is projected onto each basis function values of a set of basis functions in each neural network layer to generate corresponding projection coefficients. In a non-limiting example, three sets of basis functions are considered for the sake of brevity. However, the number of basis functions may be more than three and depends on the required application. Accordingly, when the input signal is projected onto the three basis function values derived from corresponding basis functions by performing a dot product of the input signal with each of the basis function values, three corresponding projection coefficients are generated, that is to say, Ci (t)={C1 (t), C2 (t), C3 (t), . . . } are generated respectively. The generated projection coefficients are then multiplied by a corresponding multiplying kernel gain factor Gi (t)={G1 (t), G2 (t), G3 (t), . . . }. Further, each of the corresponding resultant outputs is then passed through a corresponding non-linear activation function. Accordingly, for the encoder projection operations in the buffer mode, a plurality of encoded output responses are generated based on the performed dot product. In a non-limiting example, the plurality of output responses is shown as {tilde over (C)}i (t)={{tilde over (C)}1 (t), {tilde over (C)}2 (t), {tilde over (C)}3 (t), . . . }. Accordingly, the activity within the neural network represents the projection coefficients, or a nonlinear transformation of the projection coefficients. Thus, the projection coefficients are not constants but depend on time, even during the inference operations. Further, contrary to the temporal convolution operation that is performed in FIG. 3 A and FIG. 3 B, the convolution operation is replaced by a projection operation, thereby outputting a vector output rather than a scalar output.
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HopalongPetrovski
I'm Spartacus!
Hey Mate.
I hear you.
It's been a hard period for many (including myself), for some time now.
And it sucks when things don't work out quite how we would have liked.
These market conditions have been tough going for many and it's all somewhat exacerbated by the political tensions and uncertainty which has rapidly become the new norm.
I think in times like these, it's good perhaps, to take a time out, in whatever way makes sense to you, and is conducive to your unique circumstances.
Perhaps find a way to get yourself some greater perspective and try and look at your situation afresh and perhaps reevaluate your strategy.
Keep what's working and prune what's not.
I personally am still a believer in what BrainChip can offer, but the time it is taking to prove out commercially is testing many's patience.
And it may not succeed.
We have to be realistic in this regard and remain accountable to both ourselves and those who rely upon us.
That's part of the risk/reward scenario we each must weigh up for ourselves on an ongoing basis.
There are many who will try and persuade you one way or the other and if you are reading from the crapper I would suggest giving it a miss for a while. Here is a more generally balanced environment and the unrelenting toxicity over there can be wearing and not conducive to good mental health.
I can't, in good conscience, advise you on any particular course of action but would suggest that tomorrow is a new day, a new financial year and that often its darkest just before the dawn.
Mere platitudes I know, but rooted in some tried and tested experience.
Good luck to you and all the other honest holders in good faith, out there.
I think we deserve a win.
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The DoD just dropped its FY26 RDT&E budget—and it’s a $179B North Star for anyone building the future of national defense. | Greg Knutson
The DoD just dropped its FY26 RDT&E budget—and it’s a $179B North Star for anyone building the future of national defense. Here’s what’s hot (and heavily funded): 🤖 Unmanned Systems & Physical AI – The budget is stacked with programs for launched effects, ground robotics, SUAS, TITAN, and...
Is it likely that we may get a few dollars?
yes but USD...
overpup
Regular
I can’t offer advice, but I can say you are not alone… I have been feeling some despair re BRN of late
I am starting to get shit from friends and family now…
(“Hey are you a millionaire yet”)
(“hey got any more expert advice for me moneybags”) etc etc
you get the picture
it’s kind of become a running joke to them and I look like a sucker in their eyes lol maybe they are right too which gets me down even more!
Stay strong overpup,
I know you have been with us for many years, the technology has progressed as has the education process, Peters tech has been proven to be years ahead, and that has been a major time delay hurdle.
Today's 3.6 million share issue to settle a third party contractor is a very positive sign in my opinion, yes it dilutes the share register, but delays another Cap. Raise, maybe some see it as a non event, but looking deeper there's an element of trust being shown by the contractor who is on the "inside" looking out, they must be comfortable in what's transpiring.
On another point, Dr. Tony maybe just hinting that he and the engineering team are awaiting the delivery of chip/s from the foundry, as chips are back on the agenda in my opinion, my crystal ball is known to be accurate at times.
With tomorrow being a brand new financial year it will be interesting to see if the washout of shares will cease, another quarter comes to an end, so within the next 20 trading days we will get to see if any new revenue has entered our bank account.
The tide has definitely been out for way too long, the next 6 months may see a high tide return and refloat our share price a touch !?
Regards....Tech
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