BRN Discussion Ongoing
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HopalongPetrovski
I'm Spartacus!
I thought I'd read that PVDM wouldn't be attending the AGM somewhere here also, and had it attributed in my mind as coming from you Tech.Good morning Xray1,
Just to clarify a point that you have mentioned above, who told you that Peter wasn't attending the AGM, as I'm sure that he will attend in
person here in Australia, alongside the CEO, Chairperson and any other staff member who's been invited by the Board to attend.
I personally know that Anil won't be attending in person this year, sadly, but that's all at this point.
Regards...Tech
But just had a quick look back and cant find it now. Also recalled that the reason was that they were too busy at the moment to attend and recall whoever said it commenting on their sterling work ethic.
Weird because I remember thinking at the time that it's probably a relief for him so he won't have to answer about AI taking over the world a hundred times again.
My dumb question.I would be happy to see and thank both him and Anil if they wished to attend but understand the Company has grown beyond that necessity now.
In the early days everyone naturally wants to see the originator and get a feel for his credibility and ethics, but now that Brainchip has grown and employs professional management as not only guidance but also representation, it's probably not necessary.
The dealing with investors thing is all a part of Sean and the other business managers schtick, but think it probably something of a strain for our more tech oriented fellows who would have to be constantly watching their P's and Q's so as not to let something inappropriate slip in conversation with our wily dot joiners.
Lets take a look at the calendar.
Within 7 days I'd expect to see an ASX announcement with all details pertaining to our upcoming AGM currently scheduled
for Tuesday 23 May...in Sydney or Perth, probably Sydney, but I'd certainly personally prefer it to be held in Perth, but I'm bias,
aren't I !!
The LDA Capital financial agreement was/is due for completion in late March or early April, so I would expect to hear from the
company of the positive conclusion to this agreement, with monies now having been deposited into our accounts at a fair and
reasonable averaged out price, that's not too much to ask for, is it ??
It's clear that Rob is continuing to expand our ecosystem and partnership base, is he very experienced ? oh yes he is, his network
would be as extensive as many top executives within our company, "oh what a feeling"
Love Brainchip and all our believers, not short-term punters, BUT true believers. Tech.
Within 7 days I'd expect to see an ASX announcement with all details pertaining to our upcoming AGM currently scheduled
for Tuesday 23 May...in Sydney or Perth, probably Sydney, but I'd certainly personally prefer it to be held in Perth, but I'm bias,
aren't I !!
The LDA Capital financial agreement was/is due for completion in late March or early April, so I would expect to hear from the
company of the positive conclusion to this agreement, with monies now having been deposited into our accounts at a fair and
reasonable averaged out price, that's not too much to ask for, is it ??
It's clear that Rob is continuing to expand our ecosystem and partnership base, is he very experienced ? oh yes he is, his network
would be as extensive as many top executives within our company, "oh what a feeling"
Love Brainchip and all our believers, not short-term punters, BUT true believers.
Tech.Probably already posted, but link: Appear to be testing/using Brainchip to assist with contraband ID etc in US. Could be huge?????
www.sciencedirect.com
From the other XXX.
Robust Classification of Contraband Substances using Longwave Robust Classification of Contraband Substances using Longwave Hyperspectral Imaging and Full Precision and Neuromorphic Hyperspectral
Abstract
Several agencies such as the US Department of Homeland Security (DHS) seek to improve the detection of illegal threats and materials passing through Ports of Entry (POE). A combined hardware/software solution that is portable, non-ionizing, handheld, low cost, and fast would represent a significant contribution towards that goal as existing systems do not fulfil many or all of these requirements. To design such a system, Quantum Ventura partnered with Bodkin Design and Engineering to combine long-wave infrared (LWIR) hyperspectral imaging (HSI) with convolutional neural networks (CNNs), implemented on full precision GPUs and neuromorphic computing modules.
Neuromorphic processors implement CNNs with dramatically reduced size, weight, power and cost (SWaP-C) compared to GPU versions. Here we describe converting the 3D CNN into a format that can be run on neuromorphic platforms. We had early access to BrainChip’s software developer kit (SDK) and simulator thus we focused our efforts
on this. We now have access to Intel Neuromorphic Research Consortium and are using it for other projects [11]. BrainChip can support many features of CNNs but not all. For example, it can only accept grayscale or RGB images, not hyperspectral images (HSIs) for convolutional input layers. (For regular input layers, it may be possible to input HSIs but only 4-bit precision can be used at this time.) Because of this, we had to remap the 61 bands of the HSI image into separate “grayscale” input channels and then fuse across input channels in groups. Furthermore, the skip connections in the original 3D CNN are implemented by copying activation values from one neural processor unit (NPU) to another, and then copying them to the original NPU with identical weights of the value 1. This was the recommendation from BrainChip. The AkidaTM chip has 80 NPUs so using a handful of extra NPUs to implement the skip connections would not prevent neuromorphic implementation [12]. In Figure 5, we show the translated CNN
compatible with the BrainChip hardware.
Robust Classification of Contraband Substances using Longwave Hyperspectral Imaging and Full Precision and Neuromorphic Convolutional Neural Networks
Several agencies such as the US Department of Homeland Security (DHS) seek to improve the detection of illegal threats and materials passing through P…
Robust Classification of Contraband Substances using Longwave Robust Classification of Contraband Substances using Longwave Hyperspectral Imaging and Full Precision and Neuromorphic Hyperspectral
Abstract
Several agencies such as the US Department of Homeland Security (DHS) seek to improve the detection of illegal threats and materials passing through Ports of Entry (POE). A combined hardware/software solution that is portable, non-ionizing, handheld, low cost, and fast would represent a significant contribution towards that goal as existing systems do not fulfil many or all of these requirements. To design such a system, Quantum Ventura partnered with Bodkin Design and Engineering to combine long-wave infrared (LWIR) hyperspectral imaging (HSI) with convolutional neural networks (CNNs), implemented on full precision GPUs and neuromorphic computing modules.
Neuromorphic processors implement CNNs with dramatically reduced size, weight, power and cost (SWaP-C) compared to GPU versions. Here we describe converting the 3D CNN into a format that can be run on neuromorphic platforms. We had early access to BrainChip’s software developer kit (SDK) and simulator thus we focused our efforts
on this. We now have access to Intel Neuromorphic Research Consortium and are using it for other projects [11]. BrainChip can support many features of CNNs but not all. For example, it can only accept grayscale or RGB images, not hyperspectral images (HSIs) for convolutional input layers. (For regular input layers, it may be possible to input HSIs but only 4-bit precision can be used at this time.) Because of this, we had to remap the 61 bands of the HSI image into separate “grayscale” input channels and then fuse across input channels in groups. Furthermore, the skip connections in the original 3D CNN are implemented by copying activation values from one neural processor unit (NPU) to another, and then copying them to the original NPU with identical weights of the value 1. This was the recommendation from BrainChip. The AkidaTM chip has 80 NPUs so using a handful of extra NPUs to implement the skip connections would not prevent neuromorphic implementation [12]. In Figure 5, we show the translated CNN
compatible with the BrainChip hardware.
Techinvestor17
Regular
They are critical of AKIDA in that it can’t handle the hyperspectrual imaging but they are also using the CNN2SNN converter. I wonder if they would get a better outcome using a native SNN?Probably already posted, but link: Appear to be testing/using Brainchip to assist with contraband ID etc in US. Could be huge?????
From the other XXX.Robust Classification of Contraband Substances using Longwave Hyperspectral Imaging and Full Precision and Neuromorphic Convolutional Neural Networks
Several agencies such as the US Department of Homeland Security (DHS) seek to improve the detection of illegal threats and materials passing through P…
Robust Classification of Contraband Substances using Longwave Robust Classification of Contraband Substances using Longwave Hyperspectral Imaging and Full Precision and Neuromorphic Hyperspectral
Abstract
Several agencies such as the US Department of Homeland Security (DHS) seek to improve the detection of illegal threats and materials passing through Ports of Entry (POE). A combined hardware/software solution that is portable, non-ionizing, handheld, low cost, and fast would represent a significant contribution towards that goal as existing systems do not fulfil many or all of these requirements. To design such a system, Quantum Ventura partnered with Bodkin Design and Engineering to combine long-wave infrared (LWIR) hyperspectral imaging (HSI) with convolutional neural networks (CNNs), implemented on full precision GPUs and neuromorphic computing modules.
Neuromorphic processors implement CNNs with dramatically reduced size, weight, power and cost (SWaP-C) compared to GPU versions. Here we describe converting the 3D CNN into a format that can be run on neuromorphic platforms. We had early access to BrainChip’s software developer kit (SDK) and simulator thus we focused our efforts
on this. We now have access to Intel Neuromorphic Research Consortium and are using it for other projects [11]. BrainChip can support many features of CNNs but not all. For example, it can only accept grayscale or RGB images, not hyperspectral images (HSIs) for convolutional input layers. (For regular input layers, it may be possible to input HSIs but only 4-bit precision can be used at this time.) Because of this, we had to remap the 61 bands of the HSI image into separate “grayscale” input channels and then fuse across input channels in groups. Furthermore, the skip connections in the original 3D CNN are implemented by copying activation values from one neural processor unit (NPU) to another, and then copying them to the original NPU with identical weights of the value 1. This was the recommendation from BrainChip. The AkidaTM chip has 80 NPUs so using a handful of extra NPUs to implement the skip connections would not prevent neuromorphic implementation [12]. In Figure 5, we show the translated CNN
compatible with the BrainChip hardware.
Space Cadet
Regular
If you take that one step further Bodkin seem to looking at Hyperspectral Imaging from drones in Agriculture as well. Wonder where they could get a chip for that? Looking at the size of the camera they may have a way to go.Probably already posted, but link: Appear to be testing/using Brainchip to assist with contraband ID etc in US. Could be huge?????
From the other XXX.Robust Classification of Contraband Substances using Longwave Hyperspectral Imaging and Full Precision and Neuromorphic Convolutional Neural Networks
Several agencies such as the US Department of Homeland Security (DHS) seek to improve the detection of illegal threats and materials passing through P…
Robust Classification of Contraband Substances using Longwave Robust Classification of Contraband Substances using Longwave Hyperspectral Imaging and Full Precision and Neuromorphic Hyperspectral
Abstract
Several agencies such as the US Department of Homeland Security (DHS) seek to improve the detection of illegal threats and materials passing through Ports of Entry (POE). A combined hardware/software solution that is portable, non-ionizing, handheld, low cost, and fast would represent a significant contribution towards that goal as existing systems do not fulfil many or all of these requirements. To design such a system, Quantum Ventura partnered with Bodkin Design and Engineering to combine long-wave infrared (LWIR) hyperspectral imaging (HSI) with convolutional neural networks (CNNs), implemented on full precision GPUs and neuromorphic computing modules.
Neuromorphic processors implement CNNs with dramatically reduced size, weight, power and cost (SWaP-C) compared to GPU versions. Here we describe converting the 3D CNN into a format that can be run on neuromorphic platforms. We had early access to BrainChip’s software developer kit (SDK) and simulator thus we focused our efforts
on this. We now have access to Intel Neuromorphic Research Consortium and are using it for other projects [11]. BrainChip can support many features of CNNs but not all. For example, it can only accept grayscale or RGB images, not hyperspectral images (HSIs) for convolutional input layers. (For regular input layers, it may be possible to input HSIs but only 4-bit precision can be used at this time.) Because of this, we had to remap the 61 bands of the HSI image into separate “grayscale” input channels and then fuse across input channels in groups. Furthermore, the skip connections in the original 3D CNN are implemented by copying activation values from one neural processor unit (NPU) to another, and then copying them to the original NPU with identical weights of the value 1. This was the recommendation from BrainChip. The AkidaTM chip has 80 NPUs so using a handful of extra NPUs to implement the skip connections would not prevent neuromorphic implementation [12]. In Figure 5, we show the translated CNN
compatible with the BrainChip hardware.
https://www.bodkindesign.com/products-page/hyperspectral-imaging/precision-agriculture/
SC
rgupta
Regular
A good analysis.
The other thing I want add here is,
Who is the biggest holders of brn?
Insiders!
If we look back 2 years company had issued shares and options around 500 million which is owned by brainchip staff.
If the problem here is so deep rooted brainchip should not able to retain old staff, leave alone hiring new ones.
So again my idea is, it is time to accomulate than to be fearful.
It is once a while opportunity to fry the shorts in there own game.
Dyor
Boab
I wish I could paint like Vincent
There was a post from a couple of weeks ago that I recall talking abut spotting concealed weapons/guns/knives etc.Probably already posted, but link: Appear to be testing/using Brainchip to assist with contraband ID etc in US. Could be huge?????
From the other XXX.Robust Classification of Contraband Substances using Longwave Hyperspectral Imaging and Full Precision and Neuromorphic Convolutional Neural Networks
Several agencies such as the US Department of Homeland Security (DHS) seek to improve the detection of illegal threats and materials passing through P…
Robust Classification of Contraband Substances using Longwave Robust Classification of Contraband Substances using Longwave Hyperspectral Imaging and Full Precision and Neuromorphic Hyperspectral
Abstract
Several agencies such as the US Department of Homeland Security (DHS) seek to improve the detection of illegal threats and materials passing through Ports of Entry (POE). A combined hardware/software solution that is portable, non-ionizing, handheld, low cost, and fast would represent a significant contribution towards that goal as existing systems do not fulfil many or all of these requirements. To design such a system, Quantum Ventura partnered with Bodkin Design and Engineering to combine long-wave infrared (LWIR) hyperspectral imaging (HSI) with convolutional neural networks (CNNs), implemented on full precision GPUs and neuromorphic computing modules.
Neuromorphic processors implement CNNs with dramatically reduced size, weight, power and cost (SWaP-C) compared to GPU versions. Here we describe converting the 3D CNN into a format that can be run on neuromorphic platforms. We had early access to BrainChip’s software developer kit (SDK) and simulator thus we focused our efforts
on this. We now have access to Intel Neuromorphic Research Consortium and are using it for other projects [11]. BrainChip can support many features of CNNs but not all. For example, it can only accept grayscale or RGB images, not hyperspectral images (HSIs) for convolutional input layers. (For regular input layers, it may be possible to input HSIs but only 4-bit precision can be used at this time.) Because of this, we had to remap the 61 bands of the HSI image into separate “grayscale” input channels and then fuse across input channels in groups. Furthermore, the skip connections in the original 3D CNN are implemented by copying activation values from one neural processor unit (NPU) to another, and then copying them to the original NPU with identical weights of the value 1. This was the recommendation from BrainChip. The AkidaTM chip has 80 NPUs so using a handful of extra NPUs to implement the skip connections would not prevent neuromorphic implementation [12]. In Figure 5, we show the translated CNN
compatible with the BrainChip hardware.
I don't recall the company.
This one has much more detail and looks very positive.
Thank you for sharing.
Always-On Smart Sensors: Solving the Connectivity Challenge
IoT isn’t really about the internet. It’s not even about […]
tealcom.io
Stable Genius
Regular
Hi @Boab
That was a different device, from memory using a hand held radar to scan people and use ML to identify objects on peoples bodies which were a threat, e.g. a gun/knife. It was a law enforcement company creating it. It’d be a bit like the TASER. Once one state has it they will all be wanting it. If it helps reduce the issues with shoot/no shoot scenario’s, and stop, search & detain laws to make things safer for the public/police then all the better!
Very interesting article that was shared. One of the reasons Brainchip will be successful is the price. How good it is that a $50 item is being compared with Nvidia’s $30K device.
Robust Classification of Contraband Substances using Longwave Hyperspectral Imaging and Full Precision and Neuromorphic Convolutional Neural Networks
Several agencies such as the US Department of Homeland Security (DHS) seek to improve the detection of illegal threats and materials passing through P…
Last edited:
Fullmoonfever
Top 20
Just noticed role ad by BAE which I mentioned in a previous post that BRE had won a subaward from for some project work.
Not saying related just that BAE into the neuromorphic sphere as well. Few things there that our partners like Intellisense, ISL and BRE who deal with DOD ecosystem, work on with Akida.
jobs.baesystems.com
Clearance Level - Must Be Able To ObtainTop Secret
ITAR: U.S. Citizen Or Green Card RequiredYes
Travel Percentage10%
Clearance Level - Must Currently PossessNone
Career LevelExperienced
Is Relocation AvailableYes
Job Description
Because this role involves a combination of collaborative/in-person and independent work, it will take the form of a hybrid work format, with time split between working onsite and remotely.
BAE Systems FAST Labs™ Advanced Electronics (AE) Tech Area is growing rapidly. AE architects novel and beyond-next-generation RF, mixed-signal, and digital integrated circuits and chipsets, and delivers those new custom microelectronics capabilities into systems. With this rapid growth, AE is seeking a technology leader to help establish and drive its strategy toward a future expanded state. The candidate for this position will lead AE’s Digital Microelectronics portfolio. The trajectory of this portfolio includes algorithmic hardware accelerators, secure microprocessors and secure microsystems, digital signal processor SOCs and ASICs, radiation hard microelectronics, and new high-risk/high-reward novel compute technologies.
The candidate will lead others in engagement with external and internal customers to build, win, and execute programs that support the research and development of digital microelectronics technology. They are expected to leverage their position, reputation, and technical expertise to build and maintain relationships with government R&D organization such as DARPA, AFRL, OUSD(R&E), etc. Additionally, the candidate is expected to define and advance the Digital microelectronics technical and programmatic roadmaps while maintaining alignment across the broader Advanced Electronics (AE) tech area.
In this job role, qualified candidates can expect to:
This position can be based out of our Merrimack, NH; Burlington, MA; or Arlington, VA facilities, though it will require collaboration with staff across our business areas and facilities in the Northeast.
Please note that pursuant to a government contract, this specific position requires US citizenship status and eligibility to obtain a US security clearance.
Required Education, Experience, & Skills
The ideal candidate will possess the following:
Preferred Education, Experience, & Skills
About BAE Systems Electronic Systems
BAE Systems, Inc. is the U.S. subsidiary of BAE Systems plc, an international defense, aerospace and security company which delivers a full range of products and services for air, land and naval forces, as well as advanced electronics, security, information technology solutions and customer support services. Improving the future and protecting lives is an ambitious mission, but it’s what we do at BAE Systems. Working here means using your passion and ingenuity where it counts – defending national security with breakthrough technology, superior products, and intelligence solutions. As you develop the latest technology and defend national security, you will continually hone your skills on a team—making a big impact on a global scale. At BAE Systems, you’ll find a rewarding career that truly makes a difference. Electronic Systems (ES) is the global innovator behind BAE Systems’ game-changing defense and commercial electronics. Exploiting every electron, we push the limits of what is possible, giving our customers the edge and our employees opportunities to change the world. Our products and capabilities can be found everywhere – from the depths of the ocean to the far reaches of space. At our core are more than 14,000 highly talented Electronic Systems employees with the brightest minds in the industry, we make an impact – for our customers and the communities we serve.
Not saying related just that BAE into the neuromorphic sphere as well. Few things there that our partners like Intellisense, ISL and BRE who deal with DOD ecosystem, work on with Akida.
FAST Labs - Portfolio Lead, Digital Microelectronics Technology Development (Hybrid) in Burlington, Massachusetts, United States | Engineering & Technology at BAE Systems
Apply for FAST Labs - Portfolio Lead, Digital Microelectronics Technology Development (Hybrid) job with BAE Systems in Burlington, Massachusetts, United States. Engineering & Technology at BAE Systems
FAST Labs - Portfolio Lead, Digital Microelectronics Technology Development (Hybrid)
Clearance Level - Must Be Able To ObtainTop Secret
ITAR: U.S. Citizen Or Green Card RequiredYes
Travel Percentage10%
Clearance Level - Must Currently PossessNone
Career LevelExperienced
Is Relocation AvailableYes
Job Description
Because this role involves a combination of collaborative/in-person and independent work, it will take the form of a hybrid work format, with time split between working onsite and remotely.
BAE Systems FAST Labs™ Advanced Electronics (AE) Tech Area is growing rapidly. AE architects novel and beyond-next-generation RF, mixed-signal, and digital integrated circuits and chipsets, and delivers those new custom microelectronics capabilities into systems. With this rapid growth, AE is seeking a technology leader to help establish and drive its strategy toward a future expanded state. The candidate for this position will lead AE’s Digital Microelectronics portfolio. The trajectory of this portfolio includes algorithmic hardware accelerators, secure microprocessors and secure microsystems, digital signal processor SOCs and ASICs, radiation hard microelectronics, and new high-risk/high-reward novel compute technologies.
The candidate will lead others in engagement with external and internal customers to build, win, and execute programs that support the research and development of digital microelectronics technology. They are expected to leverage their position, reputation, and technical expertise to build and maintain relationships with government R&D organization such as DARPA, AFRL, OUSD(R&E), etc. Additionally, the candidate is expected to define and advance the Digital microelectronics technical and programmatic roadmaps while maintaining alignment across the broader Advanced Electronics (AE) tech area.
In this job role, qualified candidates can expect to:
- Lead relevant research programs of 5-25 people as principal investigator
- Provide business and execution oversight of programs valued at $1-50M
- Contribute to and lead proposals
- Build and maintain strategic partnerships with government agencies, external companies, and other BAE Systems groups and business areas
- Implement a programmatic strategy to fund technology development
- Pursue personal and portfolio growth to target standing up, managing, and supporting a team of scientists, researchers, and technology developers
This position can be based out of our Merrimack, NH; Burlington, MA; or Arlington, VA facilities, though it will require collaboration with staff across our business areas and facilities in the Northeast.
Please note that pursuant to a government contract, this specific position requires US citizenship status and eligibility to obtain a US security clearance.
Required Education, Experience, & Skills
The ideal candidate will possess the following:
- Excellent written and oral communication skills
- Experience capturing and leading projects at the cutting edge of technology
- Track record of technical innovation as evidenced by journal and conference publications or patent filings
- Demonstrable entrepreneurial drive
- Experience in at least one of the following areas:
- Digital Signal Processing
- AI/ML Hardware Acceleration
- Digital system architecture
- Secure microprocessor architecture
- Neural networks or neuromorphic engineering
Preferred Education, Experience, & Skills
- Masters or PhD in electrical engineering or related field
- Prior experience as principal investigator on DoD R&D programs
About BAE Systems Electronic Systems
BAE Systems, Inc. is the U.S. subsidiary of BAE Systems plc, an international defense, aerospace and security company which delivers a full range of products and services for air, land and naval forces, as well as advanced electronics, security, information technology solutions and customer support services. Improving the future and protecting lives is an ambitious mission, but it’s what we do at BAE Systems. Working here means using your passion and ingenuity where it counts – defending national security with breakthrough technology, superior products, and intelligence solutions. As you develop the latest technology and defend national security, you will continually hone your skills on a team—making a big impact on a global scale. At BAE Systems, you’ll find a rewarding career that truly makes a difference. Electronic Systems (ES) is the global innovator behind BAE Systems’ game-changing defense and commercial electronics. Exploiting every electron, we push the limits of what is possible, giving our customers the edge and our employees opportunities to change the world. Our products and capabilities can be found everywhere – from the depths of the ocean to the far reaches of space. At our core are more than 14,000 highly talented Electronic Systems employees with the brightest minds in the industry, we make an impact – for our customers and the communities we serve.
Stable Genius
Regular
I’m dropping this report here for those new to Brainchip querying defence revenue. I’ve posted it a few times however in light of some of the recent news it’s worth a reminder there is a roadmap to success: and we’re firmly on it; in pole position!
The easiest way to navigate around it is to hit the ”View the interactive report” button I circled in yellow (On the link supplied).
There are a plethora of chapters we are perfect for. I’m sure once the scientists/engineers get their head around how to use Akida it will be become ubiquitous throughout Defence. It’s just time to go from proof of concept to operational/commercial production!
Enjoy
The easiest way to navigate around it is to hit the ”View the interactive report” button I circled in yellow (On the link supplied).
There are a plethora of chapters we are perfect for. I’m sure once the scientists/engineers get their head around how to use Akida it will be become ubiquitous throughout Defence. It’s just time to go from proof of concept to operational/commercial production!
Enjoy
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Bravo
If ARM was an arm, BRN would be its biceps💪!
Qualcomm's Snapdragon 8 Gen 3 Rumored For A Massive GPU Uplift For Android Devices
by Aaron Klotz — Tuesday, April 11, 2023, 05:20 PM EDT
The performance claim comes to us by way of 数码闲聊站 (translated to "Digital chatter") on Weibo. We don’t know how true this 50% performance upgrade could be, but it's not completely unreasonable. Qualcomm’s current Snapdragon Gen 2 is consistently 30% faster than the Snapdragon 8 Gen 1, in most graphical applications, and slightly less compared to Qualcomm’s mid-cycle refresh, the Snapdragon 8+ Gen 1, based on our review of the Samsung Galaxy S23 Ultra.
The Snapdragon 8 Gen 3 is rumored to arrive as soon as Q4 2023, and is rumored to feature a unique (1+5+2) core configuration, consisting of 1 speedy X4 mega-core clocked at 3.75GHz, five A720 performance cores clocked at 3GHz and two A520 efficiency cores clocked at 2 GHz. The Gen 3 chipset is also rumored to be manufactured on TSMC’s more efficient N4P node which promises up to 11% greater performance and 22% higher efficiency compared to the standard N4 node.
Gen 3’s rumored upgrades are shaping up to be a massive performance uplift over the Snapdragon Gen 2 and any of Apple’s current bionic chipsets. Hopefully, Qualcomm can accomplish this 50% performance target without killing the chip’s thermal performance as it did with the Snapdragon 8 Gen 1
Qualcomm's Snapdragon 8 Gen 3 Rumored For A Massive GPU Uplift For Android Devices
A new rumor suggests that Qualcomm’s upcoming Snapdragon 8 Gen 3 SoC will feature a whopping 50% greater graphical uplift over its already super strong predecessor the Snapdragon 8 Gen 2.
Bravo
If ARM was an arm, BRN would be its biceps💪!
Snapdragon 8 Gen 3 To Feature Cortex-X4 Running At A 15 Percent Higher Clock Speed Than Snapdragon 8 Gen 2’s Cortex-X3
The Snapdragon 8 Gen 3 is said to be equipped with a faster Cortex-X4, with one tipster highlighting its expected clock speed
wccftech.com
jtardif999
Regular
If this was anything to do with Akida (and I haven’t looked at it) then there would be no need to teach it to learn, it would just learn in the course of events. Nobody with a brain has to be taught to learn
What Is Few Shot Learning? (Definition, Applications) | Built In
Few-shot learning is a subfield of machine learning that aims to teach AI models how to learn from only a small number of labeled training data.buff.ly
which suggests it is describing some computer process with no real learning outcome. Am I right?, I should read it I guess but I felt compelled by the wording ‘taught to learn’.FrederikSchack
Regular
Very interesting! If Akida 1 could do an nVidia A100's job reasonably well at a fraction of the cost/size/price, then we're very competitive.Probably already posted, but link: Appear to be testing/using Brainchip to assist with contraband ID etc in US. Could be huge?????
From the other XXX.Robust Classification of Contraband Substances using Longwave Hyperspectral Imaging and Full Precision and Neuromorphic Convolutional Neural Networks
Several agencies such as the US Department of Homeland Security (DHS) seek to improve the detection of illegal threats and materials passing through P…
Robust Classification of Contraband Substances using Longwave Robust Classification of Contraband Substances using Longwave Hyperspectral Imaging and Full Precision and Neuromorphic Hyperspectral
Abstract
Several agencies such as the US Department of Homeland Security (DHS) seek to improve the detection of illegal threats and materials passing through Ports of Entry (POE). A combined hardware/software solution that is portable, non-ionizing, handheld, low cost, and fast would represent a significant contribution towards that goal as existing systems do not fulfil many or all of these requirements. To design such a system, Quantum Ventura partnered with Bodkin Design and Engineering to combine long-wave infrared (LWIR) hyperspectral imaging (HSI) with convolutional neural networks (CNNs), implemented on full precision GPUs and neuromorphic computing modules.
Neuromorphic processors implement CNNs with dramatically reduced size, weight, power and cost (SWaP-C) compared to GPU versions. Here we describe converting the 3D CNN into a format that can be run on neuromorphic platforms. We had early access to BrainChip’s software developer kit (SDK) and simulator thus we focused our efforts
on this. We now have access to Intel Neuromorphic Research Consortium and are using it for other projects [11]. BrainChip can support many features of CNNs but not all. For example, it can only accept grayscale or RGB images, not hyperspectral images (HSIs) for convolutional input layers. (For regular input layers, it may be possible to input HSIs but only 4-bit precision can be used at this time.) Because of this, we had to remap the 61 bands of the HSI image into separate “grayscale” input channels and then fuse across input channels in groups. Furthermore, the skip connections in the original 3D CNN are implemented by copying activation values from one neural processor unit (NPU) to another, and then copying them to the original NPU with identical weights of the value 1. This was the recommendation from BrainChip. The AkidaTM chip has 80 NPUs so using a handful of extra NPUs to implement the skip connections would not prevent neuromorphic implementation [12]. In Figure 5, we show the translated CNN
compatible with the BrainChip hardware.
It also sounds like that they may benefit greatly from Akida 2's raw sensor input, TENN and 8 bit weights. Akida-P's 50 TFLOPS and probably much larger neuron capacity could probably help a lot.
FrederikSchack
Regular
Oh, AI-stralia, you keep giving. Our family is humbly thanking you for handing over another 220.000 shares.
jtardif999
Regular
I am right, this is just another article about extending generative AI.If this was anything to do with Akida (and I haven’t looked at it) then there would be no need to teach it to learn, it would just learn in the course of events. Nobody with a brain has to be taught to learn
Steve10
Regular
Crucial moment for the markets tonight.
US CPI due at 10.30pm AEST. Forecast to have big drop down to 5.2-5.3% YOY from 6% YOY.
However, core CPI forecast to remain flattish or go up slightly to 5.5-5.6% YOY from 5.5% YOY.
If market rises it will rise fast as shorts will get sizzled. April is seasonally a very good month.
US CPI due at 10.30pm AEST. Forecast to have big drop down to 5.2-5.3% YOY from 6% YOY.
However, core CPI forecast to remain flattish or go up slightly to 5.5-5.6% YOY from 5.5% YOY.
If market rises it will rise fast as shorts will get sizzled. April is seasonally a very good month.
Taproot
Regular
Hi @Boab
That was a different device, from memory using a hand held radar to scan people and use ML to identify objects on peoples bodies which were a threat, e.g. a gun/knife. It was a law enforcement company creating it. It’s be a bit like the TASER. Once once state has it they will all be wanting it. If it helps reduce the issues with shoot/no shoot scenario’s, and stop, search & detain laws to make things safer for the public/police then all the better!
Very interesting article that was shared. One of the reasons Brainchip will be successful is the price. How good it is that a $50 item is being compared with Nvidia’s $30K device.
View attachment 34104
Robust Classification of Contraband Substances using Longwave Hyperspectral Imaging and Full Precision and Neuromorphic Convolutional Neural Networks
Several agencies such as the US Department of Homeland Security (DHS) seek to improve the detection of illegal threats and materials passing through P…
Meet the $10,000 Nvidia chip powering the race for A.I.
The $10,000 Nvidia A100has become one of the most critical tools in the artificial intelligence industry,
Nvidia CEO Jensen Huang couldn’t stop talking about AI on a call with analysts on Wednesday, suggesting that the recent boom in artificial intelligence is at the center of the company’s strategy.
“The activity around the AI infrastructure that we built, and the activity around inferencing using Hopper and Ampere to influence large language models has just gone through the roof in the last 60 days,” Huang said. “There’s no question that whatever our views are of this year as we enter the year has been fairly dramatically changed as a result of the last 60, 90 days.”
Ampere is Nvidia’s code name for the A100 generation of chips. Hopper is the code name for the new generation, including H100, which recently started shipping.
More computers needed
Nvidia A100 processor
Nvidia
Compared to other kinds of software, like serving a webpage, which uses processing power occasionally in bursts for microseconds, machine learning tasks can take up the whole computer’s processing power, sometimes for hours or days.
This means companies that find themselves with a hit AI product often need to acquire more GPUs to handle peak periods or improve their models.
These GPUs aren’t cheap. In addition to a single A100 on a card that can be slotted into an existing server, many data centers use a system that includes eight A100 GPUs working together.
This system, Nvidia’s DGX A100, has a suggested price of nearly $200,000, although it comes with the chips needed. On Wednesday, Nvidia said it would sell cloud access to DGX systems directly, which will likely reduce the entry cost for tinkerers and researchers.
It’s easy to see how the cost of A100s can add up.
For example, an estimate from New Street Research found that the OpenAI-based ChatGPT model inside Bing’s search could require 8 GPUs to deliver a response to a question in less than one second.
At that rate, Microsoft would need over 20,000 8-GPU servers just to deploy the model in Bing to everyone, suggesting Microsoft’s feature could cost $4 billion in infrastructure spending.
“If you’re from Microsoft, and you want to scale that, at the scale of Bing, that’s maybe $4 billion. If you want to scale at the scale of Google, which serves 8 or 9 billion queries every day, you actually need to spend $80 billion on DGXs.” said Antoine Chkaiban, a technology analyst at New Street Research. “The numbers we came up with are huge. But they’re simply the reflection of the fact that every single user taking to such a large language model requires a massive supercomputer while they’re using it.”
The latest version of Stable Diffusion, an image generator, was trained on 256 A100 GPUs, or 32 machines with 8 A100s each, according to information online posted by Stability AI, totaling 200,000 compute hours.
At the market price, training the model alone cost $600,000, Stability AI CEO Mostaque said on Twitter, suggesting in a tweet exchange the price was unusually inexpensive compared to rivals. That doesn’t count the cost of “inference,” or deploying the model.
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