BRN Discussion Ongoing
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HopalongPetrovski
I'm Spartacus!
Somedays BRN makes my head hurt.
Today is one of those days.
Jacob Kiraz
Emerged
Just remember Tech if no deals in 24/25 Sean Heir will not be in Sydney AGM TimeDon't forget....that's the pointy, point toasty, you appreciate NDA's and why they are in place in the first place, and as such the curtains
will remain closed until such time as either party concerned agrees to remove them, so the waiting, dot joining and impatience remains
the number one annoyance for many shareholders.
You still whinging about management ? 85% + didn't think it warranted a spill just yet, maybe this time next year the story will be
completely reversed, but not from where I stand.
Anything worth contributing to the forum toasty ? just asking for a friend.
Tech
jtardif999
Regular
Why would Loihi scale better than Akida? Akida being available as IP puts it in a scaling league of its own don’t you think? The node offerings mean it can be embedded as small as two nodes or as large as 256 nodes. I don’t think BrainChip have ever left Akida in the Data Centre off the table either. Versatility is definitely a thing being able to offer up both hardware and software versions of both TENNs and Akida - something that I’m pretty sure Loihi is not capable of. TENNs makes our product line extremely agile imo.That's indeed the answer of my question... couple posts ago.
BTW: <!DOCTYPE html><!-- Last Published: Fri May 24 2024 17:40:12 GMT+0000 (Coordinated Universal Time) --><html data-wf-domain="www.quantumventura.tech" .....
Very nice find... and we all know that Loihi is not the best choice
While Loihi 2 offers more scalability and programmability, Akida's key advantage is its on-chip learning capability and extreme power efficiency for edge AI applications. This allows Akida to continue learning and adjusting to new data at the edge, without relying on external processors or data transfer. ... necessary for network attack detection... Akida bring also low power consumption as well as lower compute cost to the tabke... Loihi 2 does not have this on-chip learning capability and need a separate CPU. (For my understanding)
While Loihi 2 provides advantages like faster processing, better scalability across chips, and more programmability. But ... Loihi and Loihi 2 chips are currently only available for research and evaluation purposes through Intel's Neuromorphic Research Community (INRC).
.
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Cirat
Regular
Well that was over 1M shares boughtSomedays BRN makes my head hurt.
Today is one of those days.
Bravo
If ARM was an arm, BRN would be its biceps💪!
Not sure if this has anything to do with us but is evidence of further interest in utilising facial recognition for vehicle access.
CATEGORIES Access Control | Biometrics News | Facial Recognition
A patent filing from the Ford Motor Company for a facial recognition vehicle entry system has been published by the U.S. Patent and Trademark Organization. This technology utilizes both biometric and a non-biometric fallback authentication method to allow access to a vehicle.
The system described in the patent application is designed to integrate dual authentication modes, ensuring that only authorized individuals can gain access to the vehicle. The system includes various components, including image sensors, lockout devices, and a controller for the authentication process.
During primary authentication based on face biometrics, the system captures a real-time image of the person attempting to access the vehicle. The captured image is then analyzed to determine if it matches any stored facial patterns associated with authorized users. If a match is found, the controller grants access to the vehicle by commanding the lockout device to unlock.
If the primary biometric authentication fails and the captured image doesn’t match any stored facial patterns, the system prompts for secondary authentication. It then captures a second image and analyzes it for a non-biometric code. The system offers adjustable security levels based on the complexity of the secondary code. For instance, the code can be alphanumeric, gestures, or graphical depictions. The document depicts the use of gesture recognition to recognize a sequence of hand movements as an authentication method.
For activity logging, whenever a secondary user gains access using a secondary code, the system captures and stores an image of this event. This stored image enables the primary user to review and verify the activities and identities of those who accessed the vehicle.
“The invention may be practiced in any vehicle with exterior cameras that are tied to a vehicle controller with image processing capability. Facial recognition may be retained as the primary device-free authentication modality, while a secondary authentication option can reinstate some benefits associated with the use of keypad code entry,” the patent application explains.
Ford Global Technologies was awarded a patent for a facial recognition system to identify drivers and unlock car doors in 2022. The system could start the vehicle, monitor the health conditions of occupants, and even identify and assess the threat level of animals outside the vehicle.
Ford Motor patent filing for facial recognition vehicle entry system published | Biometric Update
The innovative technology utilizes both biometric and a non-biometric fallback authentication method to allow access to a vehicle.
www.biometricupdate.com
Ford Motor patent filing for facial recognition vehicle entry system published
May 29, 2024, 5:52 pm EDT | Abhishek JadhavCATEGORIES Access Control | Biometrics News | Facial Recognition
A patent filing from the Ford Motor Company for a facial recognition vehicle entry system has been published by the U.S. Patent and Trademark Organization. This technology utilizes both biometric and a non-biometric fallback authentication method to allow access to a vehicle.
The system described in the patent application is designed to integrate dual authentication modes, ensuring that only authorized individuals can gain access to the vehicle. The system includes various components, including image sensors, lockout devices, and a controller for the authentication process.
During primary authentication based on face biometrics, the system captures a real-time image of the person attempting to access the vehicle. The captured image is then analyzed to determine if it matches any stored facial patterns associated with authorized users. If a match is found, the controller grants access to the vehicle by commanding the lockout device to unlock.
If the primary biometric authentication fails and the captured image doesn’t match any stored facial patterns, the system prompts for secondary authentication. It then captures a second image and analyzes it for a non-biometric code. The system offers adjustable security levels based on the complexity of the secondary code. For instance, the code can be alphanumeric, gestures, or graphical depictions. The document depicts the use of gesture recognition to recognize a sequence of hand movements as an authentication method.
For activity logging, whenever a secondary user gains access using a secondary code, the system captures and stores an image of this event. This stored image enables the primary user to review and verify the activities and identities of those who accessed the vehicle.
“The invention may be practiced in any vehicle with exterior cameras that are tied to a vehicle controller with image processing capability. Facial recognition may be retained as the primary device-free authentication modality, while a secondary authentication option can reinstate some benefits associated with the use of keypad code entry,” the patent application explains.
Ford Global Technologies was awarded a patent for a facial recognition system to identify drivers and unlock car doors in 2022. The system could start the vehicle, monitor the health conditions of occupants, and even identify and assess the threat level of animals outside the vehicle.
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Newk R
Regular
I've been off line for a while. Just got out of hospital after major kidney surgery. The pain has been quite staggering to be honest.
Anyway, for a bit of therapy, I thought I'd take a quick look here, and it really has helped. The pain of the operation has paled into insignificance and is now a distant memory.
Anyway, for a bit of therapy, I thought I'd take a quick look here, and it really has helped. The pain of the operation has paled into insignificance and is now a distant memory.
Cigarless once again.Not sure if this has anything to do with us but is evidence of further interest in utilising facial recognition for vehicle access.
Ford Motor patent filing for facial recognition vehicle entry system published | Biometric Update
The innovative technology utilizes both biometric and a non-biometric fallback authentication method to allow access to a vehicle.www.biometricupdate.com
Ford Motor patent filing for facial recognition vehicle entry system published
May 29, 2024, 5:52 pm EDT | Abhishek Jadhav
CATEGORIES Access Control | Biometrics News | Facial Recognition
A patent filing from the Ford Motor Company for a facial recognition vehicle entry system has been published by the U.S. Patent and Trademark Organization. This technology utilizes both biometric and a non-biometric fallback authentication method to allow access to a vehicle.
The system described in the patent application is designed to integrate dual authentication modes, ensuring that only authorized individuals can gain access to the vehicle. The system includes various components, including image sensors, lockout devices, and a controller for the authentication process.
During primary authentication based on face biometrics, the system captures a real-time image of the person attempting to access the vehicle. The captured image is then analyzed to determine if it matches any stored facial patterns associated with authorized users. If a match is found, the controller grants access to the vehicle by commanding the lockout device to unlock.
If the primary biometric authentication fails and the captured image doesn’t match any stored facial patterns, the system prompts for secondary authentication. It then captures a second image and analyzes it for a non-biometric code. The system offers adjustable security levels based on the complexity of the secondary code. For instance, the code can be alphanumeric, gestures, or graphical depictions. The document depicts the use of gesture recognition to recognize a sequence of hand movements as an authentication method.
For activity logging, whenever a secondary user gains access using a secondary code, the system captures and stores an image of this event. This stored image enables the primary user to review and verify the activities and identities of those who accessed the vehicle.
“The invention may be practiced in any vehicle with exterior cameras that are tied to a vehicle controller with image processing capability. Facial recognition may be retained as the primary device-free authentication modality, while a secondary authentication option can reinstate some benefits associated with the use of keypad code entry,” the patent application explains.
Ford Global Technologies was awarded a patent for a facial recognition system to identify drivers and unlock car doors in 2022. The system could start the vehicle, monitor the health conditions of occupants, and even identify and assess the threat level of animals outside the vehicle.
US2024166165A1 FACIAL RECOGNITION ENTRY SYSTEM WITH SECONDARY AUTHENTICATION 20221121 Publication: US2024166165A1·2024-05-23
[0023] FIG. 2 shows authentication controller 16 in greater detail. A main processor 30 includes logic 31 which directs operation according to the processes described herein. A program block 32 performs facial recognition and/or gesture recognition by comparing captured images to prestored templates (biometric and nonbiometric).
It looks like Ford are using software to compare stored images with the camera output (processor 30; program block 32). There is no mention of NNs or AI.
Still that does not conclusively rule out using Akida simulation software, but it does not closely describe a NN application. But doing an old fashioned image comparison would use a lot of power. Wouldn't it be funny if the car recognizes the driver, but then has a flat battery from the effort.
TeNNs?
After all there is the hypothesis that Valeo and Mercedes could be using Akida simulation software ...
MadMayHam | 合氣道
Regular
Fullmoonfever
Top 20
Further to my prev QV post...it appears they also set up a new company in Japan mid last year.Could be the CyberNeuro-RT now live and available
You'd like to think if selling it, that they will need to be sourcing the 2 "neuromorphic offerings" via direct eg BRN & Intel, via their own licence or via an existing licensee eg Megachips or Renesas?
View attachment 63995
Quantum Ventura
www.quantumventura.tech
CyberNeuro-RT
An AI/ML-driven, highly-scalable, real-time network defense & threat intelligence tool with GPU or low-power neuromorphic chip deployment
A Quantum Ventura, Lockheed Martin, and Penn State Innovation
Quantum Ventura’s CyberNeuro-RT (CNRT) technology offering has been developed in partnership with Lockheed Martin Co.’s MFC Division and Pennsylvania State University under partial funding from the U.S. Department of Energy.
Cutting-Edge Unsupervised ML
Scalable Unsupervised Outlier Detection (SUOD)
Variational Autoencoder (VAE)
- Large-scale heterogeneous outlier detection
- 6 ML Algo EnsembleModel Approximation for Complex Models
- Execution Efficiency Improvement for Task Load Balancing in Distributed System
- Variational Autoencoder (VAE)
75x Dataset Growth in Under 2 Months
- Encoder-Decoder Architecture
- Variational => Highly Regularized Encoder
- ETrained to Minimize Reconstruction Error of initial input and reconstructed output
- Variational Autoencoder (VAE)
Proprietary Pipeline Adapts to Any Dataset
- Existing Dataset Ingestion: Proprietary system enables ingestion of any existing network capture dataset with flexible support for any labelling system
- From-the-wild Zero Day Sampling: System enables capturing and simulation of novel threats for additional data sampling
- Data Generation via Simulation: ThreatATI database and proprietary ingestion system enable sampling and augmentation for cataloged threats from proprietary and public threat databases
View attachment 63991
Follow Threats Home with Dark Web Tracking
View attachment 63992
At-the-edge Neuromorphic Processing
◯ Two offerings from the leading neuromorphic developers: Intel and Brainchip
◯ Small form factor, magnitudes less power consumption than GPU
◯ On-chip learning for deployment network specific attack detection
Intel Loihi
View attachment 63993
Brainchip Akida
Dashboards Minimizes Operator Fatigue
Robust, Multi-Faceted, User-Friendly Cyber Analyst Dashboard Operator Fatigue Allows Cyber Attacks To Happen
- Large numbers of false alarms cause real threats to be missed
- False alarms fatigue the cyber analyst further increasing risk of missed threats
The Cyber Neuro-RT Dashboard Is Designed To Minimize All Sources Of Analyst Fatigue While Presenting Timely And Meaningful Data Insights
- Al based false alarms are minimized (trained for minimal false positive rate)n for cataloged threats from proprietary and public threat databases
- Possible threats are ranked by importance and confidence
- Only the most relevant and likely alarms are actioned upon
ABOUT US
What we do
Team
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CyberNeuro RT
E-RECOV
Diamond Droid
CONNECT
© 2023 Quantum Ventura Inc. All Rights Reserved.
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This site goes into more explanation on the product and architecture and where we sit in the system integration flow.
Quantum Software International, Japan
Quantum Ventura Inc specializes in AI/Machine Learning, Cyber & Mobile Security, Big Data Analytics. Quantum Research Labs undertakes advanced system research.
www.quantumsoftware.jp
Pom down under
Top 20
Bravo
If ARM was an arm, BRN would be its biceps💪!
Arm's Next Chips Should Bring Faster On-Device AI to Future Phones
The chip is built with efficiency in mind to handle the power drain spikes of using generative AI.
David Lumb
May 29, 2024 8:00 a.m. PT
2 min read
Chip designer Arm on Wednesday introduced its new set of processors that will boost efficiency and speed of phones to come -- and they're built especially to empower on-device generative AI.
Chipmakers like Qualcomm include Arm processors into their full chipsets -- for instance, there are eight Arm Cortex CPU cores in the latest Snapdragon 8 Gen 3, which powers premium phones like the Samsung Galaxy S24 series. Arm's next generation of CPUs and GPUs promise speed and efficiency advancements and will better empower on-device AI, like Samsung's Galaxy AI and tools that add or remove elements of images.
The trio of new chips run on the company's latest Armv9.2 architecture. The top-end Cortex X925, made on a 3-nanometer size die, scored 36% higher on Geekbench metrics than an anonymous premium Android phone from 2023. The mid-end Cortex A725 has 35% better efficiency than its predecessor (Cortex A720) and the lower-end Cortex A525 is 15% more efficient than the Cortex A520.
Arm says these chips are made for AI, and the Cortex X925 has 41% faster time to first token -- a metric for AI speed measuring the first output after inputting a prompt -- using the tiny-Llama language model than the Cortex X4. While Arm acknowledged that any AI processing drains more power on devices than less computationally intensive use (like browsing the internet), the company said its processor efficiency gains balance out higher power drain from AI, which would be beneficial for phone- and device-makers keen on riding the AI wave with more and more implementations.
The company is also announcing its new Kleidi software libraries focusing on AI and computer vision for chipset manufacturers to pick and choose from when implementing its processors. But potentially more exciting for other devices is Arm's commitment to adding more Windows apps to run natively on its processors -- this time, adding Spotify, Chrome, Audacity and a couple others.
Arm also introduced its newest high-end GPU to pair with its CPU chips, the 14-core Immortalis G925, which is the third generation of chips to include ray tracing to render realistic light and reflections, and now includes Unreal's Lumen Raytracing on the chip. Arm says this GPU has 52% better ray tracing performance than its predecessor, the 12-core Immortalis G720.
Arm noted how these graphical improvements lead to better video efficiency on Android platforms, like 10% saved power when watching YouTube videos.
Arm's Next Chips Should Bring Faster On-Device AI to Future Phones
The chip is built with efficiency in mind to handle the power drain spikes of using generative AI.
www.cnet.com
Why is it that when using google search for “Akida” and I restrict the search for the last 24 hours only, the third site listed is www.qualcomm.com/products/mobile/snapdragon/smartphones/mobile-ai 
Fullmoonfever
Top 20
This came up while looking further into QV stuff and is from last year but they hiring for work in image processing and still using Akida.Further to my prev QV post...it appears they also set up a new company in Japan mid last year.
This site goes into more explanation on the product and architecture and where we sit in the system integration flow.
Quantum Software International, Japan
Quantum Ventura Inc specializes in AI/Machine Learning, Cyber & Mobile Security, Big Data Analytics. Quantum Research Labs undertakes advanced system research.www.quantumsoftware.jp
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View attachment 64020
Interesting was I picked up it up off a repost by Aswin Jose as below. Nice to see GF around this as well. Liking the web of connections.
Aswin Jose
GlobalFoundries Anna University
Singapore Contact Info
536 followers 500+ connections
Aswin Jose - GlobalFoundries | LinkedIn
Experienced Full Stack Architect and Machine Learning Engineer with a demonstrated… · Experience: GlobalFoundries · Education: Anna University · Location: Singapore · 500+ connections on LinkedIn. View Aswin Jose’s profile on LinkedIn, a professional community of 1 billion members.
sg.linkedin.com
Aswin Jose reposted this
Quantum Ventura Inc.
247 followers
1y
We're hiring for a part-time or full-time position in deep learning AND/OR metamaterial optical (meta-optic) image processing and classification technology! Open for remote or those near San Jose, CA
Bravo
If ARM was an arm, BRN would be its biceps💪!
View attachment 62407
Here's a patent from 2015*.
Oleg Sinyavski ( currently Principal Applied Scientist at Wayve) was one of the inventors of this patent. The other inventor was ...Olivier Coenen, currently Senior Research Scientist at ....BRAINCHIP!!!!
Could someone please call Tony ASAP and ask him to call Olivier to see if he can get on the blower to his old mate Oleg! Best to strike while the iron isI say!
View attachment 62399
View attachment 62398
*Systems and apparatus for implementing task-specific learning using spiking neuronsSystems and apparatus for implementing task-specific learning using spiking neurons
US9146546B2 · Issued Sep 29, 2015
US9146546B2 - Systems and apparatus for implementing task-specific learning using spiking neurons - Google Patents
Generalized learning rules may be implemented. A framework may be used to enable adaptive spiking neuron signal processing system to flexibly combine different learning rules (supervised, unsupervised, reinforcement learning) with different methods (online or batch learning). The generalized...patents.google.com
Can someone please ask Tony if Olivier Coenen has had a chance to talk to Oleg Sinyavsk from Wayve yet?
Riding the Wayve of AV 2.0, Driven by Generative AI
Startup Wayve develops autonomous driving technologies capable of decision-making in dynamic, real-world environments.May 29, 2024 by Norm Marks
Share
Generative AI is propelling AV 2.0, a new era in autonomous vehicle technology characterized by large, unified, end-to-end AI models capable of managing various aspects of the vehicle stack, including perception, planning and control.
London-based startup Wayve is pioneering this new era, developing autonomous driving technologies that can be built on NVIDIA DRIVE Orin and its successor NVIDIA DRIVE Thor, which uses the NVIDIA Blackwell GPU architecture designed for transformer, large language model (LLM) and generative AI workloads.
In contrast to AV 1.0’s focus on refining a vehicle’s perception capabilities using multiple deep neural networks, AV 2.0 calls for comprehensive in-vehicle intelligence to drive decision-making in dynamic, real-world environments.
Wayve, a member of the NVIDIA Inception program for cutting-edge startups, specializes in developing AI foundation models for autonomous driving, equipping vehicles with a “robot brain” that can learn from and interact with their surroundings.
“NVIDIA has been the oxygen of everything that allows us to train AI,” said Alex Kendall, cofounder and CEO of Wayve. “We train on NVIDIA GPUs, and the software ecosystem NVIDIA provides allows us to iterate quickly — this is what enables us to build billion-parameter models trained on petabytes of data.”
Generative AI also plays a key role in Wayve’s development process, enabling synthetic data generation so AV makers can use a model’s previous experiences to create and simulate novel driving scenarios.
The company is building embodied AI, a set of technologies that integrate advanced AI into vehicles and robots to transform how they respond to and learn from human behavior, enhancing safety.
Wayve recently announced its Series C investment round — with participation from NVIDIA — that will support the development and launch of the first embodied AI products for production vehicles. As Wayve’s core AI model advances, these products will enable manufacturers to efficiently upgrade cars to higher levels of driving automation, from L2+ assisted driving to L4 automated driving.
As part of its embodied AI development, Wayve launched GAIA-1, a generative AI model for autonomy that creates realistic driving videos using video, text and action inputs. It also launched LINGO-2, a driving model that links vision, language and action inputs to explain and determine driving behavior.
“One of the neat things about generative AI is that it allows you to combine different modes of data seamlessly,” Kendall said. “You can bring in the knowledge of all the texts, the general purpose reasoning and capabilities that we get from LLMs and apply that reasoning to driving — this is one of the more promising approaches that we know of to be able to get to true generalized autonomy and eventually L5 capabilities on the road.”
Riding the Wayve of AV 2.0, Driven by Generative AI
London-based startup Wayve is pioneering AV.20, a new era in autonomous vehicle technology, developing autonomous driving technologies built on NVIDIA DRIVE.
blogs.nvidia.com
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FJ-215
Regular
Hi Dio,
Quick question,
What is the possibility of TENNs becoming a separate product in its own right?
As we haven't produced a reference chip for Akida 2.0, we are running simulations for customers on legacy hardware. Could the algorithm be tuned to run on say, FPGA, CPU/GPU & alternative neuromorphic h/ware? (Loihi?)
OK, that is two questions.
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That'ud be all @Bravo 's postsWhy is it that when using google search for “Akida” and I restrict the search for the last 24 hours only, the third site listed is www.qualcomm.com/products/mobile/snapdragon/smartphones/mobile-ai
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buena suerte :-)
BOB Bank of Brainchip
buena suerte :-)
BOB Bank of Brainchip
Take care buddy
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