Vladsblood
Regular
Long time I couldn’t get my head around them not worrying about the SP….Lightbulb moment it’s gotta be the Patent safety net !!!
Thank dear God for our Founder PVDM.
Vlad
Thank dear God for our Founder PVDM.
Vlad
BRN Discussion Ongoing
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HopalongPetrovski
I'm Spartacus!
As I recall Peter had an unpleasant learning curve experience in an earlier part of his career relating to the beneficial ownership of some of his ideas.You maybe onto something there Vladsblood.
I can't remember the exact words but I do remember PVDM in one of his interviews saying something along the lines that having these patents means absolutely everything to the survival and success of the business and it's technology.
Definitely looking forward to the approvals of the other pending patents, great signs
Which is part of the why he eschewed the well worn path of working for another established concern or the Venture (Vulture) capital raising private Company route and instead opted for the public ownership company structure which grew into the Brainchip we know today.
Much clumsier, but he gets to retain control and (I think) spread the potential benefit wider than would otherwise be possible.
Personally controlling the patents is a major part of that.
Fullmoonfever
Top 20
From Dell Tech presso this month.
Wonder if they just watching the general horizon or they talking about their own horizon
HERE
We also know from a previous post that Dell aware of us from at least early 2022.
Was from one of the Directors at Dell.
thestockexchange.com.au
Wonder if they just watching the general horizon or they talking about their own horizon
HERE
We also know from a previous post that Dell aware of us from at least early 2022.
Was from one of the Directors at Dell.
Breaking News- new BRN articles/research
Some may remember reading a post I put up regarding LinkedIn followers liking brainchip. One of them was high up in the Telstra Purple divison. I just came across this. https://www.itnews.com.au/news/telstra-to-drive-ai-into-every-part-of-its-business-576771
thestockexchange.com.au
- From a hardware perspective, the accelerators of Domain Specific Architectures (DSA) [12] enable the third-wave AI algorithms to operate in a hybrid ecosystem consisting of Edge, Core, and Cloud. run anywhere in the system . Specifically, accelerators for specific domain architectures include the following examples: Nvidia's GPU, Xilinx's FPGA, Google's TPU, and artificial intelligence acceleration chips such as BrainChip's Akida Neural Processer, GraphCore's Intelligent Processing Unit (IPU), Cambrian's Machine Learning Unit (MLU) and more. These types of domain-specific architecture accelerators will be integrated into more information devices, architectures, and ecosystems by requiring less training data and being able to operate at lower power when needed. In response to this trend, the area where we need to focus on development is to develop a unified heterogeneous architecture approach that enables information systems to easily integrate and configure various different types of domain-specific architecture hardware accelerators. For Dell Technologies, we can leverage Dell's vast global supply chain and sales network to attract domain-specific architecture accelerator suppliers to adhere to the standard interfaces defined by Dell to achieve a unified heterogeneous architecture .
Gies
Regular
Oké I was in the understanding that some of the patents were on his personal name. I could be wrong.
Fullmoonfever
Top 20
At least this author takes the time to contact someone like Nandan to discuss our solution unlike some out there.
Under low power edge processing.
www.techbriefs.com
Wireless Sensor Networking for the IIoT
Factories of all sizes are incorporating automation at ever increasing rates. Among the reasons for that are reshoring, the idea that automating factories is a way of lowering labor costs for U.S. manufacturing so that domestic manufacturing becomes more cost-effective when you compare it to the costs of offshoring. You can take advantage of the much lower labor rates in many countries, but you have to add in the costs of more complex management and logistics, as well as the costs of shipping.
And then there’s the U.S. labor shortage, the difficulty of finding enough people who are willing to work in factories, while at the same time, a significant portion of the existing workforce is aging toward retirement.
And of course, the increasing productivity gains due to automation are good for the bottom line. Automation not only reduces the costs of production in the long run, but it also helps maintain reliable high-quality results and consistently predictable time frames.
The downside of automating an existing factory is the initial investment, not just in dollars, but also in the necessary down time for making such basic changes. However, most factories already have some automated processes using PLCs and other industrial controllers, generally running independently of each other, so that’s a head start. The next step is to integrate all of that into a single network — the Industrial Internet of Things (IIoT).
Ideally the factory network should also connect with the office network, to enable management to make more informed decisions. And it should enable connecting to the cloud for complicated analytics and large data storage — as well to the internet for connectivity beyond the factory.
One strategy for keeping sensor power low is to reduce the amount of data transmitted from each sensor because streaming data uses a relatively large amount of power. The trouble is that once an IIoT network has been installed, the maximum benefit comes from obtaining as much data as possible and sending it to a local server or to a cloud data center for analysis. But in general, only a small percentage of the data is relevant. External analytic data-crunching can sort the wheat from the chaff, but the size of the data stream and the amount of computing can overwhelm systems. A solution is to do preprocessing at the “edge” — right at the sensor — to determine what data is significant and only send that.
Figure 2. (Image: BrainChip)
According to Nayampally, the series is focused on three general configurations (See Figure 2). The Akida-E is the most basic of the solutions, dealing with sensor inputs like vibration detection, anomaly detection, keyword spotting, and sensor fusion. Akida-S is more mid-range. It can do microcontroller (MCU)-level machine learning for more complex tasks such as presence detection, object classification, and biometric recognition. Finally, on the right-hand side of the figure, the Akida-P can perform higher-level tasks using a microprocessor (MPU) for tasks like advanced object detection or sequence prediction.
Figure 3. InnoPhase IoT Talaria TWO™ Low Power Wi-Fi plus BLE5.0 Module with associated ML and MCU. (Image: InnoPhase IoT)
While the Brainchip solutions save power by doing advanced AI processing at the edge, InnoPhase IoT, Inc. focuses on the network architecture. Their Talaria Two Wi-Fi and BLE System on Chip (SoC) and module enables sensors to be networked via Wi-Fi. According to Deepal Mehta, InnoPhase IoT Senior Director of Business Development, since Wi-Fi enabled IoT end points use TCP/IP connectivity, they can communicate directly to the cloud without any need for intervening gateways. The chip also includes a Bluetooth Low Energy (BLE) gateway that can be used in two different ways. It enables legacy BLE connected devices to connect to the Wi-Fi network and facilitates provisioning the Wi-Fi enabled end points using an app on a cell phone.
In addition to saving power by eliminating gateways, they use a low-power radio to transmit the Wi-Fi signal. Key to the low power radio is their method of digitally encoding and decoding the RF waveform. This method, they call PolaRFusion™, is distinguished from other techniques that use higher power-consuming analog processing.
Figure 4. Typical sensor application. (Image: InnoPhase IoT)
A typical simple use case is temperature sensing. If a sensor mounted on a machine shows a trending rise in temperature, that could be an indication that there is a problem. However, instead of sending all the temperature data all the time, a local neural processor like BrainChip’s Akida can actively learn the standard temperature envelope for a particular installation and set an alarm level based on that. Then only the alarm needs to be transmitted, possibly via InnoPhase Wi-Fi, to the local server or to the cloud. Alternatively, once the alarm level has been reached, the continuous stream of temperature data can then be transmitted.
If we now consider a series of temperature sensors mounted on different assets. Each of those pieces of equipment might have different standard operating temperatures. And even identical assets located in different places in the building may have variations based on draft air, sunlight, or other factors. So, being able to use AI analytics separately at each sensor and setting different alarm levels will be extremely efficient. The value added to the manufacturing process will be multiplied if you use the same approach for other data such as vibration, pressure, levels, and flows. And even more, if in addition to all the sensor data, you can track products and processes using cameras and then analyze that information using the video analytics that can be performed with the Akida platform.
This article was written by Ed Brown, editor of Sensor Technology. For more information, go to www.brainchip.com and www.innophaseiot.com .
Under low power edge processing.
Wireless Sensor Networking for the IIoT
The Industrial Internet of Things — sharing, collecting, and analyzing information across a complete manufacturing enterprise — can significantly enhance the bo
www.techbriefs.com
OCTOBER 1, 2023 | INTERNET OF THINGS | SENSORS/DATA ACQUISITION | CONNECTIVITY
Wireless Sensor Networking for the IIoT
Figure 1. Analyzing maintenance data to forecast machine maintenance. (Image: Kristian/Adobe Stock)
Factories of all sizes are incorporating automation at ever increasing rates. Among the reasons for that are reshoring, the idea that automating factories is a way of lowering labor costs for U.S. manufacturing so that domestic manufacturing becomes more cost-effective when you compare it to the costs of offshoring. You can take advantage of the much lower labor rates in many countries, but you have to add in the costs of more complex management and logistics, as well as the costs of shipping.
And then there’s the U.S. labor shortage, the difficulty of finding enough people who are willing to work in factories, while at the same time, a significant portion of the existing workforce is aging toward retirement.
And of course, the increasing productivity gains due to automation are good for the bottom line. Automation not only reduces the costs of production in the long run, but it also helps maintain reliable high-quality results and consistently predictable time frames.
The downside of automating an existing factory is the initial investment, not just in dollars, but also in the necessary down time for making such basic changes. However, most factories already have some automated processes using PLCs and other industrial controllers, generally running independently of each other, so that’s a head start. The next step is to integrate all of that into a single network — the Industrial Internet of Things (IIoT).
Ideally the factory network should also connect with the office network, to enable management to make more informed decisions. And it should enable connecting to the cloud for complicated analytics and large data storage — as well to the internet for connectivity beyond the factory.
WHY WIRELESS?
To begin with, installing a wireless network is much less expensive. The costs in labor, materials, and downtime, for wiring a factory are far greater than for setting up a wireless system. And once in place, a wireless network is much more flexible. As processes change or new equipment is added, it is relatively simple to add or reprogram sensor nodes. Also, wireless sensors can be installed in locations that would be difficult to reach with cabling, for example on rotating machinery.DESIGNING THE SYSTEM
One of the main challenges with setting up wireless sensors, is powering them. Even if you use some sort of power harvesting, you still need power storage, usually with batteries. If the batteries have to be changed often, wireless is a non-starter, so keeping power low is of primary importance.One strategy for keeping sensor power low is to reduce the amount of data transmitted from each sensor because streaming data uses a relatively large amount of power. The trouble is that once an IIoT network has been installed, the maximum benefit comes from obtaining as much data as possible and sending it to a local server or to a cloud data center for analysis. But in general, only a small percentage of the data is relevant. External analytic data-crunching can sort the wheat from the chaff, but the size of the data stream and the amount of computing can overwhelm systems. A solution is to do preprocessing at the “edge” — right at the sensor — to determine what data is significant and only send that.
LOW POWER EDGE PROCESSING
But for edge processing to provide a net improvement in power reduction, the processing itself has to be done at low power. On that subject, I had a discussion with Nandan Nayampally, Chief Marketing Officer of BrainChip, makers of the Akida IP platform, a neural processor designed to provide ultra-low-power edge AI sensor network preprocessing. It starts with fully digital neuromorphic event-based AI and can learn on the device to trigger outputs only when there is significant information. It has its own memory that it uses to analyze the data, thus avoiding the energy-intensive transfer of data back and forth to utilize remote data storage. It also significantly reduces the amount of bandwidth required for the processing.
According to Nayampally, the series is focused on three general configurations (See Figure 2). The Akida-E is the most basic of the solutions, dealing with sensor inputs like vibration detection, anomaly detection, keyword spotting, and sensor fusion. Akida-S is more mid-range. It can do microcontroller (MCU)-level machine learning for more complex tasks such as presence detection, object classification, and biometric recognition. Finally, on the right-hand side of the figure, the Akida-P can perform higher-level tasks using a microprocessor (MPU) for tasks like advanced object detection or sequence prediction.
WI-FI NETWORKING
While the Brainchip solutions save power by doing advanced AI processing at the edge, InnoPhase IoT, Inc. focuses on the network architecture. Their Talaria Two Wi-Fi and BLE System on Chip (SoC) and module enables sensors to be networked via Wi-Fi. According to Deepal Mehta, InnoPhase IoT Senior Director of Business Development, since Wi-Fi enabled IoT end points use TCP/IP connectivity, they can communicate directly to the cloud without any need for intervening gateways. The chip also includes a Bluetooth Low Energy (BLE) gateway that can be used in two different ways. It enables legacy BLE connected devices to connect to the Wi-Fi network and facilitates provisioning the Wi-Fi enabled end points using an app on a cell phone.
In addition to saving power by eliminating gateways, they use a low-power radio to transmit the Wi-Fi signal. Key to the low power radio is their method of digitally encoding and decoding the RF waveform. This method, they call PolaRFusion™, is distinguished from other techniques that use higher power-consuming analog processing.
USE CASE
A typical simple use case is temperature sensing. If a sensor mounted on a machine shows a trending rise in temperature, that could be an indication that there is a problem. However, instead of sending all the temperature data all the time, a local neural processor like BrainChip’s Akida can actively learn the standard temperature envelope for a particular installation and set an alarm level based on that. Then only the alarm needs to be transmitted, possibly via InnoPhase Wi-Fi, to the local server or to the cloud. Alternatively, once the alarm level has been reached, the continuous stream of temperature data can then be transmitted.
If we now consider a series of temperature sensors mounted on different assets. Each of those pieces of equipment might have different standard operating temperatures. And even identical assets located in different places in the building may have variations based on draft air, sunlight, or other factors. So, being able to use AI analytics separately at each sensor and setting different alarm levels will be extremely efficient. The value added to the manufacturing process will be multiplied if you use the same approach for other data such as vibration, pressure, levels, and flows. And even more, if in addition to all the sensor data, you can track products and processes using cameras and then analyze that information using the video analytics that can be performed with the Akida platform.
THE BOTTOM LINE
The industrial internet of things — sharing, collecting, and analyzing information across a complete manufacturing enterprise — can significantly enhance the bottom line. Not only in monetary terms but also in the quality and reliability of the products and the ability to deliver them on time.This article was written by Ed Brown, editor of Sensor Technology. For more information, go to www.brainchip.com and www.innophaseiot.com .
In the US, the Constitution only permits the actual inventor to apply for a patent. Where the inventor works for a company and the invention relates to the company's business, the patent will normally be assigned to the company.
Fullmoonfever
Top 20
Just an article on the latest partnership.
I like the rollout availability.
konrad Posted on2023-10-11
BrainChip Holdings Ltd, the world’s first producer of ultra-low power, fully digital, event-based, neuromorphic AI IP, has partnered with Circle8 Clean Technologies and AVID Group to develop “Smart Bins” that use AI-powered sensors and robotics to automatically sort and recycle different types of waste. The collaboration utilizes BrainChip’s neuromorphic processor technology and AVID Group’s engineering expertise to create intelligent waste receptacles capable of sorting materials like plastic, metal, and glass. The objective is to reduce the environmental impact of waste disposal, particularly landfill usage and ocean pollution, while also increasing recycling rates and decreasing waste management costs.
The advanced smart bins will not only effectively sort and recycle waste but will also provide valuable data and insights into waste generation and consumption patterns. This data can be used to improve waste reduction strategies and identify ways to mitigate environmental impact. The partnership between BrainChip, Circle8 Clean Technologies, and AVID Group is part of a larger initiative to create smart cities that utilize AI and IoT technologies to enhance the quality of life, improve efficiency, and promote sustainability.
Initially, the smart bins will be available to municipalities in Australia before expanding to other locations worldwide. The goal is to create innovative solutions that significantly improve recycling and waste management processes, making them more convenient, efficient, and environmentally friendly. BrainChip’s neuromorphic processor technology, Akida™, is well-suited for edge AI applications that require cost-effective, ultra-low power, intelligent, real-time processing. The technology enables the deployment of effective edge compute across various applications, such as connected cars, consumer electronics, and industrial IoT, bringing AI closer to the sensor and reducing latency while maintaining privacy and data security.
The development of smart bins represents an important step towards sustainable waste management and recycling. By leveraging AI, robotics, and data insights, these bins have the potential to revolutionize waste management practices and contribute to a cleaner, greener future.
I like the rollout availability.
Smart Bins Powered by AI and Robotics Aim to Revolutionize Waste Management
BrainChip Holdings Ltd, the world’s first producer of ultra-low power, fully digital, event-based, neuromorphic AI IP, has partnered with Circle8 Clean Technologies and AVID Group to develop “Smart Bins” that use AI-powered sensors and robotics to automatically sort and recycle different types of waste. The collaboration utilizes BrainChip’s neuromorphic processor technology and AVID Group’s engineering expertise to create intelligent waste receptacles capable of sorting materials like plastic, metal, and glass. The objective is to reduce the environmental impact of waste disposal, particularly landfill usage and ocean pollution, while also increasing recycling rates and decreasing waste management costs.
The advanced smart bins will not only effectively sort and recycle waste but will also provide valuable data and insights into waste generation and consumption patterns. This data can be used to improve waste reduction strategies and identify ways to mitigate environmental impact. The partnership between BrainChip, Circle8 Clean Technologies, and AVID Group is part of a larger initiative to create smart cities that utilize AI and IoT technologies to enhance the quality of life, improve efficiency, and promote sustainability.
Initially, the smart bins will be available to municipalities in Australia before expanding to other locations worldwide. The goal is to create innovative solutions that significantly improve recycling and waste management processes, making them more convenient, efficient, and environmentally friendly. BrainChip’s neuromorphic processor technology, Akida™, is well-suited for edge AI applications that require cost-effective, ultra-low power, intelligent, real-time processing. The technology enables the deployment of effective edge compute across various applications, such as connected cars, consumer electronics, and industrial IoT, bringing AI closer to the sensor and reducing latency while maintaining privacy and data security.
The development of smart bins represents an important step towards sustainable waste management and recycling. By leveraging AI, robotics, and data insights, these bins have the potential to revolutionize waste management practices and contribute to a cleaner, greener future.
Exactly and the Patents are our value safety net. Even if one of the 'big boys' comes up with their own version of an event based all on one chip (no journey to the cloud) there will be an almighty rush to take us out for the patents and ecosystem.
Bravo
If ARM was an arm, BRN would be its biceps💪!
Interesting...
Green Hills Software is excited to be expanding its close working relationship with SiFive by adding optimized support for the P870 and X390.” said Dan Mender, VP of Business Development at Green Hills Software. “Together, Green Hills and SiFive will help companies realize the maximum performance, power, and area benefit possible for these new SiFive offerings.”
Green Hills Partner: TATA Elxsi
Afternoon Chippers ,
Looking at the chart volume indicator over last two and a half days , on one min logging of transactions, getting that feeling again .......surge in volume incoming within next hour.
Purely a Guess ...5,000,000 share volume.
Also of note... in todays trading log / String of all trades executed there are several periods of no transactions.
Should kick to the upside.
Not Financial Advice.
Regards,
Esq.
Looking at the chart volume indicator over last two and a half days , on one min logging of transactions, getting that feeling again .......surge in volume incoming within next hour.
Purely a Guess ...5,000,000 share volume.
Also of note... in todays trading log / String of all trades executed there are several periods of no transactions.
Should kick to the upside.
Not Financial Advice.
Regards,
Esq.
Last edited:
Colorado23
Regular
GazDix
Regular
Oh please lets not have the 2nd quarter report again TechGood morning,
Within the next 14 business days Brainchip will be required to deliver it's 2nd quarter results and short-term guidance, this upcoming
4C will determine our share price movement in the immediate short-term, my gut feeling like a number of posters is once again, very little
income as nothing has been announced that would suggest otherwise, apart from "watch the financials" which I still personally think is
some way off yet.
All the news that's come out of the company has been positive, there's absolutely no denying that fact and for a small start-up we are
100% making great progress establishing our company's name and superb technology. the learning continues for all.
Our accountant has a very sharp pencil at the ready to write down the 7 digit figures we are all awaiting, so what I'm trying to say in a
roundabout way is, try to contain your excitement when the share price does move north, it can't be sustained if the back of house
haven't resharpened their pencil because of lack of use.
Have a good day...Texta
buena suerte :-)
BOB Bank of Brainchip
Qualcomm and Meta are expanding your reality: Here’s how
New Snapdragon XR processors power Meta Quest 3 and the Ray-Ban Meta smart glasses collection to revolutionize your reality.www.qualcomm.com
Another area our teams worked on was the ability to make Meta Quest 3 more capable of understanding the environment around a user, so that virtual experiences can interact with physical spaces. We packed the Snapdragon XR2 Gen 2 with cutting edge on-device AI that is eight times more performant* and ultra-low latency passthrough for smoother and more natural interactions, all in a single chip architecture. This enabled Meta to build a slimmer and more comfortable headset that doesn’t require an external battery pack and enables the freedom for users to interact with their virtual and physical space seamlessly.
View attachment 46856
Zebra Technologies Partners with Qualcomm to Demonstrate on-device GenAI Capabilities | Entrepreneur
Zebra's mobile computers and tablets powered by Qualcomm Technologies will enable data insight, analysis and recommendations, problem-solving, planning and creativity even in rural, built-up and underground working environments.
www.entrepreneur.com
buena suerte :-)
BOB Bank of Brainchip
Yep agree Esqy.... many fake 'sell' orders (normal manipulation)
"Nothing is guaranteed in life"..... Apart from getting rich holding on to my precious BRN shares
Cheers mate
We might have to visit the bar again tomorrow with some weekend sounds
MDhere
Top 20
Hi fellow brners, things are starting to ramp up imo
been reading on here from time to time while dealing with some personal issues but all good now.
Aparn Pal ❤came across my path today and seemed to have pointed my also into the direction of Mr Shyam Prabhakar (realityai acquired by RENESAS) appearing in workshop Sustainable AI 4 Edge 28th Oct.
I couldn't paste what i was reading on here as it was pdf form so couldn't snap it. but i did learn a new word called Zetta
been reading on here from time to time while dealing with some personal issues but all good now.
Aparn Pal ❤came across my path today and seemed to have pointed my also into the direction of Mr Shyam Prabhakar (realityai acquired by RENESAS) appearing in workshop Sustainable AI 4 Edge 28th Oct.
I couldn't paste what i was reading on here as it was pdf form so couldn't snap it. but i did learn a new word called Zetta
Last edited:
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