BRN Discussion Ongoing
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Tony Coles
Regular
Hang on I’ll put a bid in for $900, see if it gets triggered.
xdragon
Member
I've noticed these unrealistic bid/offer prices on many stocks on the ASX, but this is the biggest offer I've seen so far, more than 1000 times the current trading price. Obviously, the person who placed the offer isn't a retail trader. But what I cannot understand is why? What's the logic behind these unrealistic bid/offer prices?
To Retail watching on simple Bank Broker data the idea that the TOTAL SELLS are greater than the TOTAL BUYERS. ie: loading the SELL side.
Market Manipulation.
With a half decent trading program on a COMPUTOR you would see all this for what it is. Trickery.
Phone APPS have no detail worth talking about and most is 20 mins behind anyway.
Yak52
Techinvestor17
Regular
Given the large share float it would not be hard. I in the same mind as you, my greatest concern with BRN is that we will be purchased before I can get any substantial value out of my investment
Fact Finder
Top 20
FFCompletely unrealistic I'm afraid.
Twitter is a mature product. It doesn't need the founders any more. It's value is what Musk wants to pay.
Brainchip is new, very different, exciting and is reliant on keeping the founder on-board. It's value is bound to keeping the status-quo.
xdragon
Member
Thanks Yak, if it's for a big volume I can understand but just 1400 shares? it doesn't make any difference to the sell side.
I agree it's manipulation but what are they trying to achieve with such little volume? 20 shares at 0.1 on the bid side and 1400 shares at 916.28 on the offer side .... I guess it's something to do with the market makers' bot trading algos but just couldn't figure out why.
Justchilln
Regular
I have to disagree
Big tech would love to get there hands on our IP, I’m sure we will have to fight off take over offers in the coming years, my question is how hard will that fight be.
Worker122
Regular
Decimal Point in wrong position?
BaconLover
Founding Member
yes trying to figure out the BoTs behaviour is headache at best! It gets more complex when you have multiples of BoTs from all large iNsto's working a stock.
TODAY was a DUMP 'N'PUMP . [ASX] Opposite to the other usual activity namely pumpndump.
Theres was enough negative news material floating around to make a few Retail knees start shaking so it was easy to load up SELL side making it look bad and have Retail jump out , triggering stop losses etc. Insto's waited for the bottom then slowly started buying back for the rebound.
For the poor RETAIL they come home tonight see their stop losses triggered and the SP is now higher probably than their sell.
So far we have rebounded 3/4% (0.75%) off ASX low. Interesting to see how far we come back to previous close levels.
Unfortunately TIME is on the side of the young,(famous old saying) which I am not any longer! Each day/month dragged out means closer to the day something serious medically catches up with me as happens to most.
Retiring with a very nice nest egg requires it happening BEFORE you get planted 6 feet under, where they don't let you out even for weekends or to go to the pub! This BS with the BRN - SP has just got to stop.
Yak52
**EDIT**
@MC🐠 mentioned this in an earlier post
Just my two cents. Obviously just a guessing game at the moment until we see revenue and a production vehicle with Akida inside but as far as Valeo lidar in Mercedes goes... Mercedes/Daimler have bought a stake in a North American company, Luminar, and it appears this will be their lidar technology in the foreseeable EQ roadmap. Meaning it would seem unlikely that Valeo lidar will be featured in the next generation Mercedes. That's not to say BRN tech won't be a part of future models or that it won't be used in other areas in their vehicles ie. as a part of ADAS or interior MBUX system (EQXX prototype).
Mercedes-Benz partners with Luminar | Mercedes-Benz Group
Today, Mercedes-Benz and Luminar Technologies, Inc., a leading global automotive technology company, announced a partnership to accelerate development of future automated driving technologies for passenger cars. A central component of this is the optimal access to new technologies and global...group.mercedes-benz.com
Mercedes To Use Luminar's LiDAR's With New Deal
Currently, both companies haven't revealed when the first Mercedes car will be revealed which will have a Luminar LiDARwww.carandbike.com
Happy to be corrected.
Hi Rick, Slade,Hi Rick. It is interesting. The sensors supplied to Mercedes currently come from Valeo, and it would seem as you suggest that Luminar are going to be involved in supplying lidar, this despite recent claims by Valeo that they will supply their lidar to Mercedes. Further complicating it all is that Luminar works with mobile eye and Nvidia. It's really hard to put this puzzle together. Think I will just wait to see it unfold, unless someone can present a reasonable scenario.
Mercedes teams with Luminar on lidar sensor technology
Mercedes-Benz and lidar expert Luminar Technologies, Inc. have entered into a partnership to develop future technologies for highly automated driving.www.eenewsautomotive.com
CORRECTION to my rambling above:
My 2 cents is that Mercedes are forming multiple partnerships with various tech companies so that they can draw from many to solve problems, both foreseeable and unforeseeable. Given all the Valeo research pointing to their Lidar being in Mercedes I would put them as the main player. So much for me waiting to see it all unfold. It has taken me all of 2 minutes to come up with a new theory and convince myself that I am right.
it is indeed a tangled web.
From a brief review of Luminar's patents, it seems they have a strong grip on using Lidar to determine relative velocity of objects in the field of view. This is a problem for DVS cameras because, when a vehicle with DVS is moving, all the pixels light up because everything in the field of view changes.
In one solution, Luminar combine a Lidar image with a normal camera image.
US10491885B1 Post-processing by lidar system guided by camera information
Post-processing in a lidar system may be guided by camera information as described herein. In one embodiment, a camera system has a camera to capture images of the scene. An image processor is configured to classify an object in the images from the camera. A lidar system generates a point cloud of the scene and a modeling processor is configured to correlate the classified object to a plurality of points of the point cloud and to model the plurality of points as the classified object over time in a 3D model of the scene.
US2022107414A1 VELOCITY DETERMINATION WITH A SCANNED LIDAR SYSTEM
A scanning imaging sensor is configured to sense an environment through which a vehicle is moving. A method for determining one or velocities associated with objects in the environment includes generating features from the first set of scan lines and the second set of scan lines, the two sets corresponding to two instances in time. The method further includes generating a collection of candidate velocities based on feature locations and time differences, the features selected pairwise with one from the first set and another from the second set. Furthermore, the method includes analyzing the distribution of candidate velocities, for example, by identifying one or more modes from the collection of the candidate velocities.
While some of Luminar's patents refer to neural networks, those I've seen do not describe the individual neurons of a NN. So it is possible that Luminar are using third party NNs.
Fox151
Regular
Wouldn't an early access program be redundant considering any spastic like me who can barely spell SNN let alone write a program to utilise one can buy an akida board from their website?
If there's no early in your early access program, you're just an access program.
If you're not early, you're late.
Just chillin.................2nd post about negativity today? Whats up dude? Hmm?
Ok unless Peter & Anil and the other 3 major management holders have sold ANY of their shares since last August then they hold a BLOCKING vote for any Take Over. PERIOD.
ADD to that number all the LT holders here and elsewhere completes that picture! Anyone invested here should/would know that. Its been recounted late last year and still stands UNLESS management & co have sold any shares since last August 2021.
So that is the only question left unanswered until end of this week with the 4C.
Yak52.
Fullmoonfever
Top 20
Anyone dug into Silicon Labs previously?
Trying to figure out their "in house" AI/ML accelerator to see if any connections.
They not saying much about it apparently.
Some article info below and I know they also connected to Edge Impulse like BRN
Also is inference on the chip...no cloud requirement apparently
Article below and their latest data sheet attached.
www.electronicproducts.com
Silicon Labs’s 2.4-GHz wireless SoCs with integrated AI/ML accelerators improve AI/ML performance with lower energy consumption.
Matter-ready and built for artificial intelligence inferencing at the edge, Silicon Labs has unveiled the BG24 and MG24 families of 2.4-GHz wireless SoCs with integrated artificial intelligence/machine learning (AI/ML) accelerators and support for multiple protocols. The company also released a new software toolkit to help developers build AI and ML algorithms using popular tool suites like TensorFlow.
The hardware and software platform was developed to help bring AI/ML applications and wireless high performance to battery-powered IoT edge devices. The new hardware also supports Matter, ZigBee, OpenThread, Bluetooth Low Energy, Bluetooth mesh, proprietary and multi-protocol operation.
Silicon Labs claims the 2.4-GHz wireless SoCs are the first with integrated AI/ML accelerators. As the first ultra-low powered devices with dedicated AI/ML accelerators built-in, the BG24 and MG24 are said to ease performance and energy penalties when deploying AI or ML at the edge.
This is “going to start to be pretty compelling for our customers. You can do inference on end-node devices without having to push data to the cloud,” said Ross Sabolcik, VP/GM of Industrial and Commercial IoT, at Silicon Labs.
The specialized hardware is designed to handle complex calculations. Silicon Labs’s internal testing showing up to a 4× improvement in AI/ML performance along with up to a 6× improvement in energy efficiency. “Because the ML calculations are happening on the local device rather than in the cloud, network latency is eliminated for faster decision-making and actions,” said the company.
“If you’re going to do [AI] inference and you’re at the edge, power consumption becomes really important,” said Sabolcik. “When we added the accelerator a big focus was ‘okay, you can do inference at the edge, but if it kills your battery life then there’s no advantage to it.’ You might as well wake up the radio and push the data to the cloud if you have the bandwidth.”
The challenge was to make it usable within the constraints of a battery-powered edge node, he said. “We can run 4× faster doing the inferences with the engine versus the MCU core with a 6× to 10× improvement in energy efficiency.”
Not only can AI/ML improve existing applications it also opens up new applications. “There’s a huge appetite in the industry to find ways to use AI/ML at the edge. Some of those use cases are there and maturing and others are emerging.”
A few examples cited include wake-word detection for audio, simple video analysis, security sensing such as glass-break sensors, occupancy sensing, and predictive maintenance.
More features
The 2.4-GHz wireless SoCs support proprietary and multi-protocol operation (including including Zigbee, OpenThread, Bluetooth Low Energy, Bluetooth mesh), depending on the SoC, and include larger memory devices and upgraded peripherals, together with a high-performance radio.
“The fact that we have all those wireless protocols in one house makes Silicon Labs attractive to customers in that we can be a one-stop shop for applications that need to mix and match those protocols. I think that’s a real strength of ours,” said Sabolcik.
“We see ourselves as fairly agnostic,” he added. “We want customers to pick the best solution based on their needs.”
This combination of supporting all of the protocols – Matter, Thread, Zigbee, Bluetooth Low Energy, Bluetooth mesh, the ability to do the multi-protocol support, the inclusion of the AI/ML accelerator, the larger memories to allow for field upgradeability and futureproofing, and adding the rich peripheral set, and doing all this on battery power in edge-node devices “is industry leading and pretty unique in what we’re bringing to the table, said Sabolcik.
Part of the bold claim is also based on the SoCs’ radio, which is the company’s highest performance radio in the 2.4-GHz space, offering a high output power of up to 20 dBm, along with “extremely” good sensitivity on the receive side and very low power consumption, targeting line-powered or battery-powered end nodes.
“There are a couple reasons why output power could be important,” said Sabolcik. “The first is if you’re trying to achieve a longer range. If you can broadcast at a higher output power that can better result in a longer range.”
Another reason is a constrained form factor, he added. “If your form factor is constrained and you don’t have the luxury of having a super-efficient antenna that you can put either on your board or in your case, output power can overcome some of those antenna limitations.”
A third reason is to improve your link budget, Sabolcik said. “Even if you need a shorter range, you could see better throughput and less data error.”
Silicon Labs also didn’t skimp on the compute side. In addition to the high-performance 2.4-GHz radio, the single-die BG24 and MG24 SoCs combine a 78-MHz ARM Cortex-M33 processor, an optimized combination of Flash (up to 1536 kB), and up to 256 kB of RAM. The AI/ML accelerator processes the ML algorithms while offloading the ARM Cortex-M33.
The devices deliver the company’s largest memory and flash capacity in its portfolio. “This means that the device can evolve for multi-protocol support, Matter, and trained ML algorithms for large datasets, said Sabolcik.
The company also beefed up the peripherals, in particular with a 20-bit ADC. “In addition, we have a lot of peripherals that we’ve added to this device including a very high-performance ADC to interface and sense with the world,” he said. “We’ve always had ADCs but getting it to a 20-bit ADC is new for us and opens a lot of new applications.”
Building on its security roadmap, the SoCs come with PSA Level 3 security. With PSA Level 3 Secure Vault protection, the devices provide the security needed for devices in smart homes, medical equipment, and industrial applications.
On the tool side, in addition to natively supporting TensorFlow, Silicon Labs has partnered with leading AI and ML tool providers like SensiML and Edge Impulse to simplify the development of machine-learning models for embedded wireless applications. These tools, together with Silicon Labs’s Simplicity Studio and the BG24 and MG24 SoCs, enable developers to create applications that use data from various connected devices, all communicating with each other using Matter to make intelligent machine learning-driven decisions, said the company.
Development and testing the new platform is underway in a closed Alpha program. The EFR32BG24 and EFR32MG24 SoCs in 5 × 5 mm QFN40 and 6 × 6 mm QFN48 packages will be available for mass deployment in April 2022. Multiple evaluation boards are available now. Modules based on the BG24 and MG24 SoCs will be available in the second half of 2022. Resources for more information about the new SoCs include a Tech Talk, “Unboxing the new BG24 and MG24 SoCs.”
www.electronicsweekly.com
Trying to figure out their "in house" AI/ML accelerator to see if any connections.
They not saying much about it apparently.
Some article info below and I know they also connected to Edge Impulse like BRN
Also is inference on the chip...no cloud requirement apparently
Article below and their latest data sheet attached.
Silicon Labs unveils Matter-ready 2.4-GHz wireless SoCs
Silicon Labs's 2.4-GHz wireless SoCs with integrated AI/ML accelerators improve AI/ML performance with lower energy consumption.
Silicon Labs unveils Matter-ready 2.4-GHz wireless SoCs
Posted on February 8, 2022 by Gina RoosSilicon Labs’s 2.4-GHz wireless SoCs with integrated AI/ML accelerators improve AI/ML performance with lower energy consumption.
Matter-ready and built for artificial intelligence inferencing at the edge, Silicon Labs has unveiled the BG24 and MG24 families of 2.4-GHz wireless SoCs with integrated artificial intelligence/machine learning (AI/ML) accelerators and support for multiple protocols. The company also released a new software toolkit to help developers build AI and ML algorithms using popular tool suites like TensorFlow.
The hardware and software platform was developed to help bring AI/ML applications and wireless high performance to battery-powered IoT edge devices. The new hardware also supports Matter, ZigBee, OpenThread, Bluetooth Low Energy, Bluetooth mesh, proprietary and multi-protocol operation.
Silicon Labs claims the 2.4-GHz wireless SoCs are the first with integrated AI/ML accelerators. As the first ultra-low powered devices with dedicated AI/ML accelerators built-in, the BG24 and MG24 are said to ease performance and energy penalties when deploying AI or ML at the edge.
This is “going to start to be pretty compelling for our customers. You can do inference on end-node devices without having to push data to the cloud,” said Ross Sabolcik, VP/GM of Industrial and Commercial IoT, at Silicon Labs.
The specialized hardware is designed to handle complex calculations. Silicon Labs’s internal testing showing up to a 4× improvement in AI/ML performance along with up to a 6× improvement in energy efficiency. “Because the ML calculations are happening on the local device rather than in the cloud, network latency is eliminated for faster decision-making and actions,” said the company.
“If you’re going to do [AI] inference and you’re at the edge, power consumption becomes really important,” said Sabolcik. “When we added the accelerator a big focus was ‘okay, you can do inference at the edge, but if it kills your battery life then there’s no advantage to it.’ You might as well wake up the radio and push the data to the cloud if you have the bandwidth.”
The challenge was to make it usable within the constraints of a battery-powered edge node, he said. “We can run 4× faster doing the inferences with the engine versus the MCU core with a 6× to 10× improvement in energy efficiency.”
Not only can AI/ML improve existing applications it also opens up new applications. “There’s a huge appetite in the industry to find ways to use AI/ML at the edge. Some of those use cases are there and maturing and others are emerging.”
A few examples cited include wake-word detection for audio, simple video analysis, security sensing such as glass-break sensors, occupancy sensing, and predictive maintenance.
More features
The 2.4-GHz wireless SoCs support proprietary and multi-protocol operation (including including Zigbee, OpenThread, Bluetooth Low Energy, Bluetooth mesh), depending on the SoC, and include larger memory devices and upgraded peripherals, together with a high-performance radio.
“The fact that we have all those wireless protocols in one house makes Silicon Labs attractive to customers in that we can be a one-stop shop for applications that need to mix and match those protocols. I think that’s a real strength of ours,” said Sabolcik.
“We see ourselves as fairly agnostic,” he added. “We want customers to pick the best solution based on their needs.”
This combination of supporting all of the protocols – Matter, Thread, Zigbee, Bluetooth Low Energy, Bluetooth mesh, the ability to do the multi-protocol support, the inclusion of the AI/ML accelerator, the larger memories to allow for field upgradeability and futureproofing, and adding the rich peripheral set, and doing all this on battery power in edge-node devices “is industry leading and pretty unique in what we’re bringing to the table, said Sabolcik.
Part of the bold claim is also based on the SoCs’ radio, which is the company’s highest performance radio in the 2.4-GHz space, offering a high output power of up to 20 dBm, along with “extremely” good sensitivity on the receive side and very low power consumption, targeting line-powered or battery-powered end nodes.
“There are a couple reasons why output power could be important,” said Sabolcik. “The first is if you’re trying to achieve a longer range. If you can broadcast at a higher output power that can better result in a longer range.”
Another reason is a constrained form factor, he added. “If your form factor is constrained and you don’t have the luxury of having a super-efficient antenna that you can put either on your board or in your case, output power can overcome some of those antenna limitations.”
A third reason is to improve your link budget, Sabolcik said. “Even if you need a shorter range, you could see better throughput and less data error.”
Silicon Labs also didn’t skimp on the compute side. In addition to the high-performance 2.4-GHz radio, the single-die BG24 and MG24 SoCs combine a 78-MHz ARM Cortex-M33 processor, an optimized combination of Flash (up to 1536 kB), and up to 256 kB of RAM. The AI/ML accelerator processes the ML algorithms while offloading the ARM Cortex-M33.
The devices deliver the company’s largest memory and flash capacity in its portfolio. “This means that the device can evolve for multi-protocol support, Matter, and trained ML algorithms for large datasets, said Sabolcik.
The company also beefed up the peripherals, in particular with a 20-bit ADC. “In addition, we have a lot of peripherals that we’ve added to this device including a very high-performance ADC to interface and sense with the world,” he said. “We’ve always had ADCs but getting it to a 20-bit ADC is new for us and opens a lot of new applications.”
Building on its security roadmap, the SoCs come with PSA Level 3 security. With PSA Level 3 Secure Vault protection, the devices provide the security needed for devices in smart homes, medical equipment, and industrial applications.
On the tool side, in addition to natively supporting TensorFlow, Silicon Labs has partnered with leading AI and ML tool providers like SensiML and Edge Impulse to simplify the development of machine-learning models for embedded wireless applications. These tools, together with Silicon Labs’s Simplicity Studio and the BG24 and MG24 SoCs, enable developers to create applications that use data from various connected devices, all communicating with each other using Matter to make intelligent machine learning-driven decisions, said the company.
Development and testing the new platform is underway in a closed Alpha program. The EFR32BG24 and EFR32MG24 SoCs in 5 × 5 mm QFN40 and 6 × 6 mm QFN48 packages will be available for mass deployment in April 2022. Multiple evaluation boards are available now. Modules based on the BG24 and MG24 SoCs will be available in the second half of 2022. Resources for more information about the new SoCs include a Tech Talk, “Unboxing the new BG24 and MG24 SoCs.”
AI accelerator in 2.4GHz wireless MCU for Matter and Bluetooth
Silicon Labs has introduced a wireless microcontroller with a hardware AI accelerator that can work with Matter, Zigbee, OpenThread, Bluetooth Low Energy
www.electronicsweekly.com
I like the way we are marketing ourselves.
There's a lot of talk about NDA's. They work in our favour.
Ever played bridge? And finessed your opponent? The same logic applies here - because you don't know exactly who is dealing with Brainchip you need to assume your competitors are, so you had better find out about this Akida chip and IP.
Intel must be concerned that AMD could use our IP in a new Ryzen just as ARM must be concerned that Risc-V / SiFive are checking us out,
Just as BMW must be concerned that they didn't know Mercedes were looking at us.
We should remember these things when the Quarterly comes out and doomers throw up their arms ("no names, no 1000% revenue increase..")
I'll definitely be putting in a low offer for more shares in case.
JK200SX
Regular
Stole the words from Brainchip......?
Pony.ai Becomes the First Autonomous Driving Company to Receive a Taxi License in China
Pony.ai, the leading global autonomous driving technology company, announced it is the first and only autonomous driving (AD) company to obtain a taxi
www.businesswire.com
I'm guessing the EAP would have been aimed mainly at the potential Akida IP customers, and the $50k hand-holding fee would have been to assist in interfacing Akida IP with the customers product.
So I would think that anybody who came along now seeking a licence for 1 million plus devices would still get a helping hand.
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