I just did driving back from the shopsAfternoon Pom ,
Well that was rather racey,
CRANK IT .
Regards,
Esq.
@DingoBoratSean has stated a couple of times at least now. how when New Technological frontiers are created, it is a bit of a "Wild West" for a while, until 2 or 3 dominant players, come to the fore.
He wants "us" to be one of those dominant players and so obviously, do we!
I would think this would look like, something of a 20 to 30% market share, of our targeted markets combined.
With our apparent foothold lead, in the Premier, Space, Military and Medical fields, this is well within reach, in my opinion.
But it's obviously the Big breakthrough in "bread and butter" consumer markets, that we eagerly seek.
Despite what our 20 cent share price and lack of any serious revenue shows, "we" have been laying the foundations and groundwork, to become one of those dominant players, for some time now.
Tony Lewis is hinting very strongly at the announcement of a new technological update, coming out imminently, which while strategically important and value adding from an IP perspective, is not the kind of announcement we really need.
I know Sean must have mixed up the Time Zones, as "his" Friday has only recently ended and the necessity to announce a new large IP deal on the ASX, missed even "our" late announcements deadline.
So Monday morning, is still on the cards, for the Big Announcement, I promised last week.
View attachment 87779
Hi Fmf,@DingoBorat
Your crystal ball working well.
Suspect we get an Ann Mon morn as you say with a new Patent published 26 June.
Just came up and doesn't have all the paperwork on the website yet, just the below.
US2025209313A1
METHOD AND SYSTEM FOR IMPLEMENTING ENCODER PROJECTION IN NEURAL NETWORKS
Bibliographic data
Global Dossier
Applicants
BRAINCHIP INC [US]
Inventors
COENEN PHD OLIVIER JEAN-MARIE DOMINIQUE [US]; PEI YAN RU [US]
Classifications
IPC
G06F17/16; G06N3/048;
CPC
G06F17/16 (US); G06N3/048 (US);
Priorities
US202363614220P·2023-12-22; US202418991246A·2024-12-20
Application
US202418991246A·2024-12-20
Publication
US2025209313A1·2025-06-26
Published as
US2025209313A1
en
METHOD AND SYSTEM FOR IMPLEMENTING ENCODER PROJECTION IN NEURAL NETWORKS
Abstract
Disclosed is a neural network system that includes a memory and a processor. The memory is configured to store a plurality of storage buffers corresponding to a current neural network layer, and implement a neural network that includes a plurality of neurons for the current neural network layer and a corresponding group among a plurality of groups of basis function values. The processor is configured to receive an input data sequence into the first plurality of storage buffers over a first time sequence and project the input data sequence on a corresponding basis function values by performing, for each connection of a corresponding neuron, a dot product of the first input data sequence within a corresponding storage buffer with the corresponding basis function values and thereby determine a corresponding potential value for the corresponding neurons. Thus, utilizing the corresponding potential values, the processor generates a plurality of encoded output responses.
Cheers.Hi Fmf,
That was a quick pickup ...
I find Espacenet is more user friendly, but you can get the nitty-gritty on examination from USPTO.Cheers.
I have Espacenet and USPTO open on tabs on my phone with relevant keywords and just refresh every so often when I remember and see if the result # changes.
I noticed tonight it went from 18 to 20 so had a look and obviously one was our own and the other is Collins Aerospace reference to Akida.
It's all gobbledygook to me FMF..@DingoBorat
Your crystal ball working well.
Suspect we get an Ann Mon morn as you say with a new Patent published 26 June.
Just came up and doesn't have all the paperwork on the website yet, just the below.
US2025209313A1
METHOD AND SYSTEM FOR IMPLEMENTING ENCODER PROJECTION IN NEURAL NETWORKS
Bibliographic data
Global Dossier
Applicants
BRAINCHIP INC [US]
Inventors
COENEN PHD OLIVIER JEAN-MARIE DOMINIQUE [US]; PEI YAN RU [US]
Classifications
IPC
G06F17/16; G06N3/048;
CPC
G06F17/16 (US); G06N3/048 (US);
Priorities
US202363614220P·2023-12-22; US202418991246A·2024-12-20
Application
US202418991246A·2024-12-20
Publication
US2025209313A1·2025-06-26
Published as
US2025209313A1
en
METHOD AND SYSTEM FOR IMPLEMENTING ENCODER PROJECTION IN NEURAL NETWORKS
Abstract
Disclosed is a neural network system that includes a memory and a processor. The memory is configured to store a plurality of storage buffers corresponding to a current neural network layer, and implement a neural network that includes a plurality of neurons for the current neural network layer and a corresponding group among a plurality of groups of basis function values. The processor is configured to receive an input data sequence into the first plurality of storage buffers over a first time sequence and project the input data sequence on a corresponding basis function values by performing, for each connection of a corresponding neuron, a dot product of the first input data sequence within a corresponding storage buffer with the corresponding basis function values and thereby determine a corresponding potential value for the corresponding neurons. Thus, utilizing the corresponding potential values, the processor generates a plurality of encoded output responses.
So.....who are Rockwell Collins...now known as Collin Aerospace
They seem to think Akida could be an option to fit their neuromorphic processing component in this Patent also just published.
US2025208915A1
ONLINE SCHEDULING FOR ADAPTIVE EMBEDDED SYSTEMS
Bibliographic data
Global Dossier
Applicants
ROCKWELL COLLINS INC [US]
Inventors
GARCIA GENER ALEJANDRO [IE]; SKALISTIS STEFANOS [IE]; MORA DE SAMBRICIO JAVIER [IE]
Classifications
IPC
G06F9/50;
CPC
G06F9/4881 (EP); G06F9/4893 (EP); G06F9/5027 (US); G06N3/049 (EP); G06N3/063 (EP); G06N3/088 (EP);
Priorities
EP23219808A·2023-12-22
Application
US202418960907A·2024-11-26
Publication
US2025208915A1·2025-06-26
Published as
EP4575780A1;
US2025208915A1
en
ONLINE SCHEDULING FOR ADAPTIVE EMBEDDED SYSTEMS
Abstract
A method of generating schedules for an adaptive embedded system, the method comprising: deriving task sets of all possible tasks to be performed by the embedded system; deriving sets of all possible hardware configurations of the embedded system; creating a multi-model system having a multi-model defining the adaptivity of the system for all possible tasks and all possible hardware and all combinations thereof, the adaptivity defining how the system can change operation responsive to a mode change requirement and/or occurrence of a fault; solving a scheduling problem for the models of the multi-model system in a neuromorphic accelerator implemented by spiked neural networks; and providing schedule instructions to the system, for performance of tasks, based on the solution
Reference except:
0063] The neuromorphic accelerator, using SNNs, can be implemented in various known ways, e.g. on an FPGA as described, for example, in C. Frenkel, M. Lefebvre, J-D Legat and D. Bol, ‘A 0.086-mm212.7-pJ/SOP 64k-Synapse 256 Neuron Online-Learning Digital Spiking Neuromorphic Processor in 28-nm CMOS,’ IEEE Transactions on Biomedical Circuits and Systems, vol. 13, no. 1, pp. 145-156, 2019, or using a specific ASIC peripheral as in e.g. Brainchip Akida Neuromorphoc Core.
Implication:"We literally took a model from a completely different domain (text processing) and applied it to RADAR..."
Implication:"Conventional models take radar signals... convert to a 2D image... then use CNNs..."
Implication:"Huge opportunity for state-space models... real eye-opener..."
Implication:"My team will push out a number of new models for our Defense, Medical and commercial work."
Implication:"...lower latency, fewer MACs (less energy), less chip area (cost), and excellent performance..."
Nice one FMF!
Rockwell Collins (now part of Collins Aerospace) and Lockheed Martin have a history of collaboration on various aerospace and defense projects. They've partnered on avionics for aircraft like the F-22 and F-35, and on training systems, among other things. More recently, Collins Aerospace has supplied subsystems for NASA's Orion spacecraft, which Lockheed Martin is also involved with.
As mentioned previously, Rockwell Collins is a subsidiary of RTX Corporation, formerly known as Raytheon Technologies.
Check this out Brain Fam!
Here's a patent for NEUROMORPHIC SENSORS FOR LOW POWER WEARABLES.
The applicant is Rockwell Collins. Date of filing was 5th April 2024.
The patent doesn't mention BrainChip, but as you can see below, Brainchip worked with Rockwell Collins in 2017 on perimeter surveillance, so you'd think they would have to be aware of us.
Rockwell Collins now operates as part of Collins Aerospace, a subsidiary of ...wait for it.... the RTX Corporation (formerly Raytheon Technologies).
EXTRACT ONLY
View attachment 77447
ENLARGED EXTRACT
View attachment 77448
View attachment 77449
NEUROMORPHIC SENSORS FOR LOW POWER WEARABLES - Patent 4451233
NEUROMORPHIC SENSORS FOR LOW POWER WEARABLES - Patent 4451233data.epo.org
This Philipe Dodge guy sounds like an amateur young teenager with his BRN license plate comment.
Surely it can't be too much longer before something comes our way as a result of this new partnership between Arm and Cerence, where low-power, real-time LLM inference at the edge is the central challenge?
As we all know, Akida excels at:
While Arm and Cerence are working on optimizing LLMs on traditional CPU/GPU pipelines, the bottlenecks of power, latency, and thermal limits in vehicles still remain. Akida, being a neuromorphic processor would be capable of delivering sub-milliwatt operation for AI inference, event-based, real-time processing, on-device learning capabilities and ultra-low latency for audio and language data streams.
- Keyword spotting
- Natural language intent classification
- Time-series pattern recognition
What's not to like about that? These would be ideal traits for in-vehicle voice assistants and LLM use cases, where responsiveness, power efficiency, and privacy really matter.
It says here that "CaLLM Edge operates fully on Arm-based chipsets" and we know Akida is compatible with the Arm product family as has been successfully demonstrated with Cortex M85.
I could easily imagine a Cerence voice assistant enhanced by Akida doing real-time voice analysis and decision-making, entirely offline, with a power budget that’s EV-battery friendly.
Arm should be asking: "How can we future proof this stack for in-cabin AI by 2026-2027 when compute demands will surge but battery and thermal budgets won't".
Cerence AI and Arm push LLM boundaries with on-device AI for smarter cars
Jun 6, 2025 | Stephen Mayhew
Categories Edge Computing News | Hardware
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Cerence AI has partnered with semiconductor manufacturer, Arm to enhance its embedded small language model (SLM), CaLLM Edge, using Arm’s Kleidi software library.
The collaboration aims to optimize CPU and GPU performance for real-time language processing at the edge, improving speed, efficiency, and privacy highlighting the growing importance of edge computing and generative AI in the automotive industry.
Arm’s Kleidi technology accelerates machine learning and neural network operations on Arm-based devices, addressing the challenges of limited compute power in vehicles. CaLLM Edge operates fully on Arm-based chipsets, enabling advanced in-car AI capabilities without relying on cloud connectivity.
“We are excited to partner with Arm to take CaLLM Edge to the next level, setting new standards for performance and efficiency in edge computing in the car,” says Nils Schanz, EVP, Product & Technology, Cerence AI. “By combining our expertise in AI-powered language models with Arm’s innovative library, we are continuing our journey to create a new era of voice-first experiences and next-generation AI applications in the automotive space, empowering consumers with smarter, faster, and more responsive in-car assistants.”
This partnership supports automakers in delivering smarter, faster, and more responsive AI-powered user experiences for drivers and setting new standards for in-car AI applications, enhancing safety and connectivity.
https://www.edgeir.com/cerence-ai-and-arm-push-llm-boundaries-with-on-device-ai-for-smarter-cars-20250606
THE EYES have it- European manufactured cars are required by law -2026
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🚀 The Age of Neuromorphic AI: The Next Big Thing Is Here | Philip Dodge
🚀 The Age of Neuromorphic AI: The Next Big Thing Is Here Imagine your smartwatch predicting what you need before you ask. Imagine self-driving cars reacting with human-like instinct. Imagine medical devices diagnosing illnesses faster than the best specialists — all while sipping barely any...www.linkedin.com
Philip Dodge again, nice work.
Perhaps we should all follow Glenn and comment - Go Brainchip