its about Frontgrade contract
BRN Discussion Ongoing
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FNU Sidharth on LinkedIn: 🎉 Excited to share that our paper, "𝗗𝗲𝗰𝗼𝗱𝗶𝗻𝗴 𝗣𝗮𝗶𝗻:…
🎉 Excited to share that our paper, "𝗗𝗲𝗰𝗼𝗱𝗶𝗻𝗴 𝗣𝗮𝗶𝗻: 𝗦𝘁𝗮𝘁𝗶𝘀𝘁𝗶𝗰𝗮𝗹 𝗜𝗱𝗲𝗻𝘁𝗶𝗳𝗶𝗰𝗮𝘁𝗶𝗼𝗻 𝗼𝗳 𝗕𝗶𝗼𝗺𝗮𝗿𝗸𝗲𝗿𝘀 𝗳𝗿𝗼𝗺…
Linas Beliūnas on LinkedIn: America is back! Softbank CEO Masayoshi Son just announced a $100 billion… | 274 comments
America is back! Softbank CEO Masayoshi Son just announced a $100 billion investment focused on building out AI & creating 100,000 jobs in the US 👇😳 The… | 274 comments on LinkedIn
Dallas
Regular
Mercedes-Benz Media
Exklusive Einblicke und individuelle Angebote: Erleben Sie mit Mercedes-Benz das Maximum aus digitaler Live-PR. Exclusive insights and individual offers: Experience the maximum of digital live PR with Mercedes-Benz.
Dallas
Regular
PROPHESEE on LinkedIn: #ces2025 #ces2025 #ar #vr #edgecomputing #iot #smartdevices #eventbased…
👀 #CES2025 is just around the corner! Heading to Vegas? Discover the event-based future of Eye Tracking, IoT, AR/VR/XR, next-generation mobile features and…
Pedro has posted about Brainchip in the past, dyor.
www.linkedin.com
www.frontiersin.org
Pedro Machado on LinkedIn: Advancing Adaptive and Energy-Efficient Neuromorphic Computing for…
I am delighted to inform that I am the Topic Editor for the new Research Theme "Advancing Adaptive and Energy-Efficient Neuromorphic Computing for Real-Time…
Advancing Adaptive and Energy-Efficient Neuromorphic Computing for Real-Time Edge AI and Robotics
Neuromorphic computing represents a paradigm shift in artificial intelligence, directly inspired by the operational mechanisms of the human brain. Designed to replicate the brain's high efficiency, neuromorphic systems utilize event-driven, spike-based protocols, vastly reducing energy...
WOW, I hope everyone takes the time to read @BrainShit post, Prof. Rajiv Ranjan is very impressive.
Bravo
If ARM was an arm, BRN would be its biceps💪!
American Lithium Energy Expanding Production Capabilities
San Diego Business Journal
https://www.sdbj.com › Energy
27 Aug 2024 — American Lithium Energy Expanding Production Capabilities ... Clients include Raytheon, Lockheed Martin, Northrop Grumman, Boeing, NASA ...
Smoothsailing
Regular
I imagine this to be the last week for BRN staff for 2024 , looking forward to the upcoming video from Sean to close the year out.
Merry Christmas everyone and wishing a fantastic 2025 for Akida.
We are going to win
Merry Christmas everyone and wishing a fantastic 2025 for Akida.
We are going to win
Fullmoonfever
Top 20
Nothing majorly new, just Tata Elxsi Chief Data Scientist talking about Edge, Federated learning, model challenges, oh and the emerging neuromorphic tech.
Publication Name: Hindustantimes.com
Date: December 12, 2024
Edge Artificial Intelligence (Edge AI) has revolutionised the way data is processed in modern IoT applications, bringing real-time capabilities closer to the source of data generation—at the "edge" of networks. This shift in computing has profound implications for industries reliant on Internet of Things (IoT) devices, allowing for faster decision-making, reduced latency, and more efficient use of network resources. As we explore the role of Edge AI, it becomes clear that its ability to enhance real-time data processing is driving new possibilities for various sectors.
Traditional IoT systems have depended heavily on cloud-based models, where data is collected from edge devices, transmitted to centralised cloud servers, and then processed. While effective in some scenarios, this approach comes with inherent challenges. Latency issues arise from the distance between the edge device and the cloud server, often leading to delays that are unacceptable for time-sensitive applications. Additionally, the sheer volume of data generated by IoT devices can strain network bandwidth, causing inefficiencies and higher costs.
This is where Edge AI offers a transformative solution. By moving computation closer to where the data is generated—on the edge devices themselves—it becomes possible to perform data analysis and make decisions locally and in real-time. Instead of transmitting every piece of raw data to the cloud, edge devices can filter and process the data before sending only the most relevant information. This not only reduces network congestion but also significantly speeds up response times, which is crucial for applications like autonomous vehicles, industrial automation, and smart health care systems.
One of the major breakthroughs in Edge AI involves the development of lightweight AI models. Traditional AI models, such as deep learning networks and neural networks, are often too large and computationally heavy to run on edge devices with limited resources. These models typically require powerful GPUs or cloud-based servers to operate efficiently. However, advancements in model optimisation techniques, such as quantization and hyperparameter tuning, have enabled AI models to be compressed and optimised for edge environments. As a result, these smaller models can now run on devices with minimal processing power and memory, like microcontrollers, without compromising performance.
A key advantage of Edge AI lies in its ability to provide immediate insights through local processing. For example, in industrial settings, machines equipped with Edge AI can monitor production lines and detect anomalies in real-time. By processing the data directly on-site, these systems can identify potential faults or inefficiencies without waiting for cloud-based analysis. This allows for rapid intervention, reducing downtime and improving overall operational efficiency.
The health care industry is another sector benefiting from Edge AI. Medical devices, such as wearables and diagnostic tools, generate vast amounts of data. In scenarios where immediate action is critical—such as monitoring a patient’s vital signs—Edge AI enables real-time analysis, allowing for quicker responses to changes in a patient's condition. This capability is especially valuable in remote health care settings, where connectivity to cloud servers might be unreliable or slow.
Another significant development is the growing use of federated learning at the edge. In federated learning, multiple edge devices collaborate to train a shared AI model while keeping the data local. This decentralised approach enhances privacy and security by ensuring that sensitive data never leaves the device. Instead of sending raw data to the cloud for training, only the model updates are transmitted. This approach not only protects user privacy but also reduces the risks associated with data breaches and regulatory non-compliance.
As the edge computing ecosystem continues to mature, more sophisticated tasks can be handled locally. Emerging technologies, such as neuromorphic computing, offer even more potential by mimicking the brain’s neural architecture. These systems, designed for ultra-low-power environments, can process complex data streams with incredible speed and efficiency, making them ideal for applications that require real-time decision-making, such as robotics and autonomous systems.
However, challenges remain in fully realising the potential of Edge AI. One of the primary hurdles is the complexity of deploying AI models on resource-constrained devices. While there have been significant advancements in reducing model size and improving efficiency, many AI models still require more memory and processing power than what edge devices can provide. Additionally, maintaining model accuracy when dealing with reduced datasets at the edge can be difficult, especially in environments where the data is noisy or incomplete.
Another challenge is the variability in hardware platforms for edge computing. The diversity of edge devices—from sensors and cameras to industrial machinery—means that AI models need to be highly adaptable to different architectures. Open-source tools like Apache TVM have made strides in addressing this issue by providing frameworks that allow models to run on a wide range of hardware. This interoperability is crucial for ensuring that AI models can be deployed across various industries without extensive customisation.
Despite these challenges, the progress in Edge AI is undeniable. The ongoing evolution of processors and AI accelerators designed specifically for edge computing is making it possible to perform more complex tasks on smaller, energy-efficient devices. As Edge AI continues to develop, it is poised to unlock new applications that were previously thought to be beyond the capabilities of IoT devices.
In conclusion, the role of Edge AI in enhancing real-time data processing for modern IoT applications is pivotal. By bringing computation closer to the source of data, Edge AI reduces latency, optimises bandwidth usage, and enables faster decision-making. The convergence of AI and edge computing is driving innovation across a wide range of industries, from manufacturing and health care to agriculture and retail. While challenges remain, the advancements in lightweight AI models, federated learning, and hardware optimization are paving the way for a future where real-time, on-device intelligence becomes the norm rather than the exception.
Author: Biswajit Biswas, Chief Data Scientist, Tata Elxsi.
Publication Name: Hindustantimes.com
Date: December 12, 2024
Role of Edge AI in enhancing real-time data processing
Edge Artificial Intelligence (Edge AI) has revolutionised the way data is processed in modern IoT applications, bringing real-time capabilities closer to the source of data generation—at the "edge" of networks. This shift in computing has profound implications for industries reliant on Internet of Things (IoT) devices, allowing for faster decision-making, reduced latency, and more efficient use of network resources. As we explore the role of Edge AI, it becomes clear that its ability to enhance real-time data processing is driving new possibilities for various sectors.
Traditional IoT systems have depended heavily on cloud-based models, where data is collected from edge devices, transmitted to centralised cloud servers, and then processed. While effective in some scenarios, this approach comes with inherent challenges. Latency issues arise from the distance between the edge device and the cloud server, often leading to delays that are unacceptable for time-sensitive applications. Additionally, the sheer volume of data generated by IoT devices can strain network bandwidth, causing inefficiencies and higher costs.
This is where Edge AI offers a transformative solution. By moving computation closer to where the data is generated—on the edge devices themselves—it becomes possible to perform data analysis and make decisions locally and in real-time. Instead of transmitting every piece of raw data to the cloud, edge devices can filter and process the data before sending only the most relevant information. This not only reduces network congestion but also significantly speeds up response times, which is crucial for applications like autonomous vehicles, industrial automation, and smart health care systems.
One of the major breakthroughs in Edge AI involves the development of lightweight AI models. Traditional AI models, such as deep learning networks and neural networks, are often too large and computationally heavy to run on edge devices with limited resources. These models typically require powerful GPUs or cloud-based servers to operate efficiently. However, advancements in model optimisation techniques, such as quantization and hyperparameter tuning, have enabled AI models to be compressed and optimised for edge environments. As a result, these smaller models can now run on devices with minimal processing power and memory, like microcontrollers, without compromising performance.
A key advantage of Edge AI lies in its ability to provide immediate insights through local processing. For example, in industrial settings, machines equipped with Edge AI can monitor production lines and detect anomalies in real-time. By processing the data directly on-site, these systems can identify potential faults or inefficiencies without waiting for cloud-based analysis. This allows for rapid intervention, reducing downtime and improving overall operational efficiency.
The health care industry is another sector benefiting from Edge AI. Medical devices, such as wearables and diagnostic tools, generate vast amounts of data. In scenarios where immediate action is critical—such as monitoring a patient’s vital signs—Edge AI enables real-time analysis, allowing for quicker responses to changes in a patient's condition. This capability is especially valuable in remote health care settings, where connectivity to cloud servers might be unreliable or slow.
Another significant development is the growing use of federated learning at the edge. In federated learning, multiple edge devices collaborate to train a shared AI model while keeping the data local. This decentralised approach enhances privacy and security by ensuring that sensitive data never leaves the device. Instead of sending raw data to the cloud for training, only the model updates are transmitted. This approach not only protects user privacy but also reduces the risks associated with data breaches and regulatory non-compliance.
As the edge computing ecosystem continues to mature, more sophisticated tasks can be handled locally. Emerging technologies, such as neuromorphic computing, offer even more potential by mimicking the brain’s neural architecture. These systems, designed for ultra-low-power environments, can process complex data streams with incredible speed and efficiency, making them ideal for applications that require real-time decision-making, such as robotics and autonomous systems.
However, challenges remain in fully realising the potential of Edge AI. One of the primary hurdles is the complexity of deploying AI models on resource-constrained devices. While there have been significant advancements in reducing model size and improving efficiency, many AI models still require more memory and processing power than what edge devices can provide. Additionally, maintaining model accuracy when dealing with reduced datasets at the edge can be difficult, especially in environments where the data is noisy or incomplete.
Another challenge is the variability in hardware platforms for edge computing. The diversity of edge devices—from sensors and cameras to industrial machinery—means that AI models need to be highly adaptable to different architectures. Open-source tools like Apache TVM have made strides in addressing this issue by providing frameworks that allow models to run on a wide range of hardware. This interoperability is crucial for ensuring that AI models can be deployed across various industries without extensive customisation.
Despite these challenges, the progress in Edge AI is undeniable. The ongoing evolution of processors and AI accelerators designed specifically for edge computing is making it possible to perform more complex tasks on smaller, energy-efficient devices. As Edge AI continues to develop, it is poised to unlock new applications that were previously thought to be beyond the capabilities of IoT devices.
In conclusion, the role of Edge AI in enhancing real-time data processing for modern IoT applications is pivotal. By bringing computation closer to the source of data, Edge AI reduces latency, optimises bandwidth usage, and enables faster decision-making. The convergence of AI and edge computing is driving innovation across a wide range of industries, from manufacturing and health care to agriculture and retail. While challenges remain, the advancements in lightweight AI models, federated learning, and hardware optimization are paving the way for a future where real-time, on-device intelligence becomes the norm rather than the exception.
Author: Biswajit Biswas, Chief Data Scientist, Tata Elxsi.
DaBenjamins
Regular
Did someone say Pico. Very juicy comment from Arm!Extract/quote from an interview with Arm CEO, Renee Haas on 17th December 2024.
”What we are observing — and I think this is only going to accelerate — is that whether you’re talking about an AI data center or an AirPod or a wearable in your ear, there’s an AI workload that’s now running and that’s very clear. This doesn’t necessarily need to be ChatGPT-5 running six months of training to figure out the next level of sophistication, but this could be just running a small level of inference that is helping the AI model run wherever it’s at. We are seeing AI workloads, as I said, running absolutely everywhere. So, what does that mean for Arm?
Our core business is around CPUs, but we also do GPUs, NPUs, and neural processing engines. What we are seeing is the need to add more and more compute capability to accelerate these AI workloads. We’re seeing that as table stakes. Either put a neural engine inside the GPU that can run acceleration or make the CPU more capable to run extensions that can accelerate your AI. We are seeing that everywhere. I wouldn’t even say that’s going to accelerate; that’s going to be the default.
What you’re going to have is an AI workload running on top of everything else you have to do, from the tiniest of devices at the edge to the most sophisticated data centers. So if you look at a mobile phone or a PC, it has to run graphics, a game, the operating system, and apps — by the way, it now needs to run some level of Copilot or an agent. What that means is I need more and more compute capability inside a system that’s already kind of constrained on cost, size, and area. It’s great for us because it gives us a bunch of hard problems to go off and solve, but it’s clear what we’re seeing. So, I’d say AI is everywhere.”
Arm CEO Rene Haas on the AI chip race, Intel, and what Trump means for tech
The ubiquitous chip designer on what the “breathtaking” pace of AI means for the future.www.theverge.com
When did he say pico
TheDon
Regular
I want 2more
I have a list of investment criteria for BRN.
One of my criteria is that the BRN publicly stated "Why Invest" remains intact.
I have recently during communication established that BRN retains a commercial relationship with the Marquee brands.
You can rest easy.
buena suerte :-)
BOB Bank of Brainchip
RobjHunt
Regular
Just wear ya thongs cause they look a bit sharp.View attachment 74393
I have seen a platueing on the charts recently. Just a tad higher than the last. My opinion/guess at the picture above is that we're a bit higher than prior
One step at a time.
Pantene Peeps
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