Pretty sure Vorago was not acquired by Brainchip. Vorago is an Early Access Program participant/partner.
Definitely correct we did not acquire vorago.
42.
He’s definitely got nice hair. I think we can all agree on that. I’m just not too sure what a turtlebot burger is. Are you supposed to order fries with it?I’ll probably get the left field post of the day with this one
Anyone come across Open Robotics or Turtlebot3 before?
Could just be a student project?
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From ChatGPT so needs further verification
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If BRN is not involved then interesting developments in AI edge etc by Qualcomm. It appears there is not way knowing if Qualcomm has a form of edge AI compatible to BRN without infringing on BRN patents. Are they using Akida now with snapdragon or upgraded to Akida linked to this announcement?. I assume only the money trail via 4c’s will give answers.Wait! I found something!!!!
This was published online 2 hours ago.
You can't have a truly intelligent Connected Intelligent Edge IMO without AKIDA so I don't know what to think.
15
SOCIAL BUZZ
Qualcomm flaunts its ability in AI and Connected Intelligent EdgeSource: Shutterstock
Qualcomm flaunts its ability in AI and Connected Intelligent Edge
By Dashveenjit Kaur | 3 April, 2023
Back at Qualcomm Investor Day in 2021, President and CEO Cristiano Amon shared how the company is leading the realization of the “connected intelligent edge,” where the convergence of wireless connectivity, efficient computing, and distributed AI will power devices and experiences. “We are working to enable a world where everyone and everything is intelligently connected,” Amon said.
- A few months after Qualcomm shared how the company is driving the emergence of the Connected Intelligent Edge, the US chip designer shared further updates in a media briefing in Kuala Lumpur.
He meant that Qualcomm’s mobile heritage and DNA put the company in “an incredible position to provide high-performance, low-power computing, on-device intelligence, all wireless technologies, and leadership across AI processing and connectivity and camera graphics and sensors.” For Qualcomm, Amon sees this as an opportunity to engage a US$700 billion addressable market in the next decade.
ANDROID PHONE MAKERS AND QUALCOMM COLLABORATE TO ENABLE SATELLITE TEXT MESSAGING
Dashveenjit Kaur | 1 March, 2023
Fast forward to today, Qualcomm, as part of its ongoing campaign, “The Edge of Possible,” highlighted the significance of driving edge computing innovations to transform industries, power new consumer experiences, and fuel the growth of the digital economy. In a media briefing held in Kuala Lumpur on March 29, Qualcomm shared its progress in the space.
Connected Intelligent Edge with Qualcomm
For starters, the Connected Intelligent Edge, according to Qualcomm, is the convergence of 5G networks, edge computing, and AI. “This integration allows data to be processed and analyzed in real time, allowing faster decision-making and response times. As a result, various industries, including manufacturing, transportation, and security, can use this technology,” said Alicia Lim, head of marketing for Qualcomm Southeast Asia.
In terms of sector, for instance, Connected Intelligent Edge devices monitor patients in real-time, like tracking a patient’s vital signs, such as blood pressure and heart rate, and send the information to healthcare providers in real-time. This method allows medical professionals to identify issues and take appropriate action quickly.
In transportation, Connected Intelligent Edge devices are used to make vehicles safer and more efficient. “For example, sensors placed throughout a car can monitor its performance and provide real-time data to the driver, alerting them to any issues. The information can help prevent accidents and improve fuel efficiency, reducing costs and environmental impact,” Lim told the press during the briefing.
From a client’s perspective, Malaysia’s telco provider YTL Communications shared how Qualcomm’s Connected Intelligent Edge devices have helped the company maintain a seamless operation. “We are fortunate to have been given early access to a Connected Intelligent Edge device known as the AI Box by Qualcomm to be deployed in our building,” CEO Wing K Lee shared.
The AI Box is an intelligent edge device that supports traditional IP camera video streams to be offloaded and processed via the box — powered by system-on-chip (SOC) designed for high computing and high concurrency of machine learning-based analytics across multiple camera streams.
“With the AI box, we have applied localized processing intelligence from an analytics standpoint. The AI Box approach is aligned with the industry’s shift from video management software (VMS) to Video software as a service (VsaaS) implementation, enabling us to optimize our operations,” Lee noted.
Separately, Lim also shared how Qualcomm and Microsoft collaborated on Snapdragon to enable these new AI-accelerated experiences for Windows 11 — without impacting performance and power efficiency. “Instead, Snapdragon intelligently offloads these computationally intensive tasks to a dedicated AI engine, which frees up the CPU and GPU resources,” she said.
To top it off, adding on-device AI processing to powerful CPU and GPU capabilities adds the ability to offload compute-intensive processes. “As a result, performance is dramatically increased, delivering a higher user experience. Snapdragon computes platforms offer AI-accelerated user experiences to reach a new level of mobile computing performance,” Lim shared.
Tech Wire Asia
Tech Wire Asia is an independent tech news website that covers enterprise and technology in Asiatechwireasia.com
What do they mean that AI will “soon” become impossible to comprehend? I’m having a hard enough time understanding it now.
Very interesting Bravo. Lots of details. Sounds like mega different 4-bit AI Inferencing, then packing or unpacking? Pretty deep.Micro tile inferencing....So this is how Qualcomm are reducing their power consumption?
"The company also announced a new technology called micro tile inferencing, which breaks a neural network into many small pieces so that it can be processed all at once, rather than layer by layer, and very efficiently, saving a great deal of power."
Unlocking unexpected new AI use cases with the Connected Intelligent Edge
March 7, 2023 6:20 AM
Image Credit: Adobe Images
Presented by Qualcomm Technologies, Inc.
To data scientists, the raw potential of AI and complex neural networks was clear from the start, or close to it. But it’s only in the past five years that device hardware has become sophisticated enough to make good on the full promise, and bring AI all the way to the edge. On-device AI is what makes AI a reality for the consumers. And now devices of every size, even with lower battery capacity, are able to handle powerful, power-efficient on-device neural networks. It’s the evolution of computing from the cloud, taking inferencing right to the source.
“We’ve spent almost a decade of research on how to make AI work best on the edge,” says Ziad Asghar, senior vice president of product management, Qualcomm Technologies, Inc. “From that, we’ve developed hardware that’s able to do more inferencing for any given amount of power, and AI software stack (Qualcomm AI Stack) and tools to bring the Connected Intelligent Edge to life.”
Leveling up AI use cases and unlocking new ones
AI use cases have made their way to devices already — AI enhanced pictures and videos, AI-based voice assistants, better sound and voice quality, real-time language translation, and more are significantly improved with connectivity and data processing, while numerous brand-new use cases are just starting to make themselves known across camera, gaming, sensors and connectivity, on all devices at the edge.
On the consumer-facing side, use cases embrace everything from smartphones, XR, compute and earbuds to connected intelligent vehicles and smart homes. On the business side, they support digital transformation in the industrial and manufacturing space, connected healthcare and a leap ahead for the AI software tools and platforms companies need to stay competitive in a rapidly changing environment.
Asghar describes the Connected Intelligent Edge itself as a network with multiple nodes, or different products, within it — and many of the new possibilities lie in these device clouds. In a smart home, for example, that might include security cameras, the cars in the garage, appliances, PCs, mobile devices and tablets, all with some amount of AI processing capability.
Those security cameras might recognize a family member in order to open up the smart lock at the front door and activate environmental controls. But the Connected Intelligent Edge also disseminates AI across the whole network, so that use cases are handled with the best accuracy with the best power consumption. If there’s not enough processing power on one product, it can be handed up the line to a more powerful device.
For instance, a security camera shifting a possible false alarm to the unit that can handle anomalies and more complex incidents. The data never leaves the device or local network, so that privacy is assured. And handling latency-sensitive use cases on the device means real-time results, and a a better consumer experience.
Purpose-built AI hardware and developer tools
“From an AI developer perspective, they want a product that excels in terms of performance and in terms of power,” Asghar says. “Which means you want the best-in-class underlying hardware capability.”
That means more processing for any given amount of power. It also means the ability to write software quickly and get to a product quicker, because time to market is key. At the same time, developers need the flexibility of using AI frameworks they’re familiar with, and tools to optimize and improve performance even further. On the hardware side, Qualcomm offers the Hexagon processor with three accelerators (scalar, vector and tensor) that allow a developer to go from mapping directly to how a neural network is set up all the way to the fully connected layer.
At the most recent Snapdragon Summit, Qualcomm had several significant AI announcements, among them, considerable hardware improvements, including an upgrade of Snapdragon 8 Gen 2 AI performance by a factor up to 4.35x. It’s the first commercial design on the edge that can do integer 4-bit (INT4) AI inferencing, which means the same calculations take far fewer bits, and expend significantly less energy while maintaining accuracy.
The company also announced a new technology called micro tile inferencing, which breaks a neural network into many small pieces so that it can be processed all at once, rather than layer by layer, and very efficiently, saving a great deal of power.
And just before MWC, the company announced that it was able to run Stable Diffusion, a text-to-image AI model on a reference device powered by Snapdragon 8 Gen 2. Typically, generating images using Stable Diffusion requires vast amounts of computing power in the cloud but thanks to Qualcomm Technologies’ AI research and the power of on-device AI on the Snapdragon 8 Gen 2, they were able to not only optimize, quantize and deploy these large models using Qualcomm AI Stack onto the device but they were able to run these models in a matter of seconds. This is a significant leap from current capabilities and will allow massive AI models to run locally on devices which means better convenience, power savings, security and more.
But, Asghar says, having great hardware for AI isn’t enough. As the company continues to optimize its hardware, they’ve focused on leveling up software and tools at the same time. For instance, a loss in accuracy has historically been a primary challenge behind reducing a network running at 32-bit floating point down to four bits. Now the Qualcomm AI Studio provides the tools necessary to maintain precision even at integer INT, reducing the power by a factor of 64 and exponentially increasing the number of neural networks that can be run in the same power envelope. This is critical to do AI processing at the edge.
“The huge benefit to the Qualcomm AI Stack is enabling people to use Qualcomm technology easily and effectively without having to do a lot of setup work,” he explains. “Now I can take a mobile model to a security camera application without having to do new work. Why? Because it’s the same AI stack across all of our products. It’s really the notion of create once, and then take it anywhere.”
The Qualcomm AI stack supports popular AI frameworks and runtimes and offers developer libraries and services. The company has built SDKs for its product lines on top of this foundation — for example, Snapdragon Ride for automotive, Intelligent Multimedia SDK for IoT processing, Spaces (part of the Snapdragon Spaces XR Developer Platform for AR glasses) and more, including SDKs focused on specific verticals.
In Snapdragon Spaces, for example, Qualcomm has built AR-specific functions directly into the platform for hand and eye tracking for foveated rendering, 3D reconstruction of spaces, plane detection and more. A developer creating a new use case using AR or VR can pick up those routines and other pre-built pieces and build the final product on top, and get to a finished product faster.
The recently announced Qualcomm AI Studio brings together all the AI stack tools into a new GUI, along with visualization tools to simplify the developer experience — and provide the ability to see the complete model workflow from model design to optimization to deployment and profiling in action.
“If you’re doing anything at the edge, in a constrained power envelope or form factor, then really the best technology for you to bring it into production is based on Qualcomm,” he says. “With Qualcomm AI stack we want to make it as easy as possible for developers to be able to deploy their projects and get to market fast.”
Unlocking unexpected new AI use cases with the Connected Intelligent Edge
Device hardware has now become sophisticated enough to make good on the full promise of AI, brining it all the way to the edge.venturebeat.com
Your Holiness,If BRN is not involved then interesting developments in AI edge etc by Qualcomm. It appears there is not way knowing if Qualcomm has a form of edge AI compatible to BRN without infringing on BRN patents. Are they using Akida now with snapdragon or upgraded to Akida linked to this announcement?. I assume only the money trail via 4c’s will give answers.
Then if qualcomm have some similar tech to BRN as they are saying and it’s not us then we may be in some poo poo. But then Rob telson likes a couple of posters of companies linked to Qualcomm via twitter. Surely he isn’t just saying kudos to direct competition unless BRN are involved in some way. Hmmm
Then if kudos only there is a concern because Akida was meant to be three years in advance of its competitors. Surely they couldn’t have underestimated the competition that poorly.
The big jump in performance suggest they introduced something new. Hopefully it’s Akida.
The above might jump start some healthy chat on bravo’s post as there is many likes etc but no one (maybe one) appears to have provided supporting posts with such a announcement from Qualcomm assuming BRN are linked in some way.
FF, dio etc do you have any comments to support BRN involvement with Qualcomm’s recent news?
Micro tile inferencing....So this is how Qualcomm are reducing their power consumption?
"The company also announced a new technology called micro tile inferencing, which breaks a neural network into many small pieces so that it can be processed all at once, rather than layer by layer, and very efficiently, saving a great deal of power."
Unlocking unexpected new AI use cases with the Connected Intelligent Edge
March 7, 2023 6:20 AM
Image Credit: Adobe Images
Presented by Qualcomm Technologies, Inc.
To data scientists, the raw potential of AI and complex neural networks was clear from the start, or close to it. But it’s only in the past five years that device hardware has become sophisticated enough to make good on the full promise, and bring AI all the way to the edge. On-device AI is what makes AI a reality for the consumers. And now devices of every size, even with lower battery capacity, are able to handle powerful, power-efficient on-device neural networks. It’s the evolution of computing from the cloud, taking inferencing right to the source.
“We’ve spent almost a decade of research on how to make AI work best on the edge,” says Ziad Asghar, senior vice president of product management, Qualcomm Technologies, Inc. “From that, we’ve developed hardware that’s able to do more inferencing for any given amount of power, and AI software stack (Qualcomm AI Stack) and tools to bring the Connected Intelligent Edge to life.”
Leveling up AI use cases and unlocking new ones
AI use cases have made their way to devices already — AI enhanced pictures and videos, AI-based voice assistants, better sound and voice quality, real-time language translation, and more are significantly improved with connectivity and data processing, while numerous brand-new use cases are just starting to make themselves known across camera, gaming, sensors and connectivity, on all devices at the edge.
On the consumer-facing side, use cases embrace everything from smartphones, XR, compute and earbuds to connected intelligent vehicles and smart homes. On the business side, they support digital transformation in the industrial and manufacturing space, connected healthcare and a leap ahead for the AI software tools and platforms companies need to stay competitive in a rapidly changing environment.
Asghar describes the Connected Intelligent Edge itself as a network with multiple nodes, or different products, within it — and many of the new possibilities lie in these device clouds. In a smart home, for example, that might include security cameras, the cars in the garage, appliances, PCs, mobile devices and tablets, all with some amount of AI processing capability.
Those security cameras might recognize a family member in order to open up the smart lock at the front door and activate environmental controls. But the Connected Intelligent Edge also disseminates AI across the whole network, so that use cases are handled with the best accuracy with the best power consumption. If there’s not enough processing power on one product, it can be handed up the line to a more powerful device.
For instance, a security camera shifting a possible false alarm to the unit that can handle anomalies and more complex incidents. The data never leaves the device or local network, so that privacy is assured. And handling latency-sensitive use cases on the device means real-time results, and a a better consumer experience.
Purpose-built AI hardware and developer tools
“From an AI developer perspective, they want a product that excels in terms of performance and in terms of power,” Asghar says. “Which means you want the best-in-class underlying hardware capability.”
That means more processing for any given amount of power. It also means the ability to write software quickly and get to a product quicker, because time to market is key. At the same time, developers need the flexibility of using AI frameworks they’re familiar with, and tools to optimize and improve performance even further. On the hardware side, Qualcomm offers the Hexagon processor with three accelerators (scalar, vector and tensor) that allow a developer to go from mapping directly to how a neural network is set up all the way to the fully connected layer.
At the most recent Snapdragon Summit, Qualcomm had several significant AI announcements, among them, considerable hardware improvements, including an upgrade of Snapdragon 8 Gen 2 AI performance by a factor up to 4.35x. It’s the first commercial design on the edge that can do integer 4-bit (INT4) AI inferencing, which means the same calculations take far fewer bits, and expend significantly less energy while maintaining accuracy.
The company also announced a new technology called micro tile inferencing, which breaks a neural network into many small pieces so that it can be processed all at once, rather than layer by layer, and very efficiently, saving a great deal of power.
And just before MWC, the company announced that it was able to run Stable Diffusion, a text-to-image AI model on a reference device powered by Snapdragon 8 Gen 2. Typically, generating images using Stable Diffusion requires vast amounts of computing power in the cloud but thanks to Qualcomm Technologies’ AI research and the power of on-device AI on the Snapdragon 8 Gen 2, they were able to not only optimize, quantize and deploy these large models using Qualcomm AI Stack onto the device but they were able to run these models in a matter of seconds. This is a significant leap from current capabilities and will allow massive AI models to run locally on devices which means better convenience, power savings, security and more.
But, Asghar says, having great hardware for AI isn’t enough. As the company continues to optimize its hardware, they’ve focused on leveling up software and tools at the same time. For instance, a loss in accuracy has historically been a primary challenge behind reducing a network running at 32-bit floating point down to four bits. Now the Qualcomm AI Studio provides the tools necessary to maintain precision even at integer INT, reducing the power by a factor of 64 and exponentially increasing the number of neural networks that can be run in the same power envelope. This is critical to do AI processing at the edge.
“The huge benefit to the Qualcomm AI Stack is enabling people to use Qualcomm technology easily and effectively without having to do a lot of setup work,” he explains. “Now I can take a mobile model to a security camera application without having to do new work. Why? Because it’s the same AI stack across all of our products. It’s really the notion of create once, and then take it anywhere.”
The Qualcomm AI stack supports popular AI frameworks and runtimes and offers developer libraries and services. The company has built SDKs for its product lines on top of this foundation — for example, Snapdragon Ride for automotive, Intelligent Multimedia SDK for IoT processing, Spaces (part of the Snapdragon Spaces XR Developer Platform for AR glasses) and more, including SDKs focused on specific verticals.
In Snapdragon Spaces, for example, Qualcomm has built AR-specific functions directly into the platform for hand and eye tracking for foveated rendering, 3D reconstruction of spaces, plane detection and more. A developer creating a new use case using AR or VR can pick up those routines and other pre-built pieces and build the final product on top, and get to a finished product faster.
The recently announced Qualcomm AI Studio brings together all the AI stack tools into a new GUI, along with visualization tools to simplify the developer experience — and provide the ability to see the complete model workflow from model design to optimization to deployment and profiling in action.
“If you’re doing anything at the edge, in a constrained power envelope or form factor, then really the best technology for you to bring it into production is based on Qualcomm,” he says. “With Qualcomm AI stack we want to make it as easy as possible for developers to be able to deploy their projects and get to market fast.”
Unlocking unexpected new AI use cases with the Connected Intelligent Edge
Device hardware has now become sophisticated enough to make good on the full promise of AI, brining it all the way to the edge.
Yup. Ella said “It isn’t where you’ve been, it’s where your going to that counts”. Then she apparently yelled out “Akida Ballista!”
Caution: This response was generated by ChatGPT
Position requires a PhD in artificial intelligence yet it pays $28 an hour.
Or Bard??? Just saw this to get on their wait list:
Weird I posted it here 19 mins ago but the tweet is younger than thatLooks blank..
