Bravo
If ARM was an arm, BRN would be its biceps💪!
Slap yourself Big Boy you turn me on
OK. Take a cold shower please!
Slap yourself Big Boy you turn me on
Can you weave that into your next downramping MF article. Take care and hope to see you at the AGM in Sydney.Rob seems like a very nice guy. I liked him. Sad to see him go. Hoping that his replacement is equally likeable, and more importantly can get us some IP deals!
WoW, I'm thrilled, what beautiful music! You have really a good taste.
...I heard a study in a science podcast of the University of Innsbruck published at Science Report before I read your post about how song lyrics are becoming more and more simple minded in the last 40a. Better to dispense with words in these times and let the music flow.
Thanks again, I will listen to it more closely, didn't know him.
WoW, I'm thrilled, what beautiful music! You have really a good taste.
...I heard a study in a science podcast of the University of Innsbruck published at Science Report before I read your post about how song lyrics are becoming more and more simple minded in the last 40a. Better to dispense with words in these times and let the music flow.
Thanks again, I will listen to it more closely, didn't know him.
I'd blame your dance thongs.Sorry, what's your point? Tony should be telepathic and BrainChip is somehow responsible for Rob leaving to join another company that isn't doing so well?
I suppose I should blame BrainChip because I stubbed my toe on a rock this morning? Sheesh!
Haven’t had the time to watch it, yet, but here is a video of that talk resp. of a talk with the same name that was uploaded to YouTube just over a week prior to the Design&Reuse IP-SoC Silicon Valley 2024 Day event:
View attachment 62100
Given the previously published paper stating the use of Akida and the suggestions that BRN have been working with a communications company, the following Ericsson blog end of April on their MWC24 booth imo appears to have some different additional prototype work to my original Christmas eve post (link below) on:
Towards 6G Zero-Energy Internet of Things:
Standards, Trends, and Recent Results
- December 2023
https://www.researchgate.net/public...of_Things_Standards_Trends_and_Recent_Results
BRN Discussion Ongoing
Merry Christmas, happy holidays all Be safe and enjoy the time with loved ones, friends, fam and doing what makes you happy however you celebrate this time. Oct French article on the X320 I needed to work around through VPN, private tab etc to get to read with an English translation...thestockexchange.com.au
6G straight from the Ericsson labs
There were many questions for our experts who worked on the 6G demos in the “6G – straight from our labs” area at the Mobile World Congress (MWC) 2024. Among the most commonly-asked questions were: "Is it not too early to talk 6G?”, “What is new for 6G?”, “How does 6G relate to 5G?” and all sorts of questions on spectrum – new, existing, co-existing, reuse, coverage and more. So, let’s take the questions one-by-one… This blog will show how we take steps towards addressing some of these questions by describing what was on display at MWC 2024.
APR 30, 2024 | 7 min.
Marie Hogan
6G Portfolio Strategy, Business Area Networks
Johan Lundsjö
Strategic Research Communication Director
What is new for 6G?
6G will build on 5G Standalone and 5G-Advanced, evolving from today’s network towards the needs of 2030 and beyond. In other words, 6G will be a mix of both new and evolved concepts and use cases.
A selection of new concepts was on display straight from our 6G labs on the MWC floor.
Ultra-Low Power AI
This concept addresses two major areas of interest - AI and Energy Efficiency - topics that might seem mutually exclusive! There is rapid growth both in interest and usage of AI in mobile network operations, solutions and applications. There’s a risk that the massive amounts of data processing demanded in many AI scenarios lead to high power consumption in parallel. With increased usage of AI expected in the networks, it is important to have solutions to address this.
This live prototype for ultra-low power AI used a novel neuromorphic-AI-based approach for radio channel estimation and showcased the feasibility of low-compute and low-energy AI using AI-based radio receiver use-cases. The trick here is that in a neuromorphic neural network (like our human brain) only the neurons detecting a change are active, whereas no computations are needed for neurons in remember state. The fraction of inactive neurons translates directly to an energy efficiency gain as compared to a traditional deep neural network where computations are always needed for all neurons. The live demo showed how neural activity in the channel estimation computations varied with changes in the radio channel and how energy consumption could be reduced when less or no computations were ongoing. Radio channel estimation is only one of many areas where this exciting AI technology can be used. Keep an eye out for upcoming blogs on this topic from Ericsson soon!
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Hi Fullmoonfever,
the radio receiver algorithm prototype Ericsson demoed at MWC 2024 was unfortunately not implemented on Akida.
See my post dated April 18:
https://thestockexchange.com.au/threads/brn-discussion-ongoing.1/post-419427
Or in short here:
Ericsson Research Demonstrates How Intel Labs’ Neuromorphic AI Accelerator Reduces Compute Costs
Philipp Stratmann is a research scientist at Intel Labs, where he explores new neural network architectures for Loihi, Intel’s neuromorphic research AI accelerator. Co-author Péter Hága is a master researcher at Ericsson Research, where he leads research activities focusing on the applicability...community.intel.com
“Using neuromorphic computing technology from Intel Labs, Ericsson Research is developing custom telecommunications artificial intelligence (AI) models to optimize telecom architecture. Ericsson currently uses AI-based network performance diagnostics to analyze communications service providers’ radio access networks (RANs) to resolve network issues efficiently and provide specific parameter change recommendations. At Mobile World Congress (MWC) Barcelona 2024, Ericsson Research demoed a radio receiver algorithm prototype targeted for Intel’s Loihi 2 neuromorphic research AI accelerator, demonstrating a significant reduction in computational cost to improve signals across the RAN
(…)
Ericsson Research’s working prototype of a radio receiver algorithm was implemented in Lava for Loihi 2. In the demonstration, the neural network performs a common complex task of recognizing the effects of reflections and noise on radio signals as they propagate from the sender (base station) to the receiver (mobile). Then the neural network must reverse these environmental effects so that the information can be correctly decoded.”
Have a good weekend!
Frangipani
Although it would be grossly negligent for the Ericsson Team to test only the Intel chip (which according to BRN is still in research) when there are others available.
I very much doubt they would not be aware of BRN. I would also be very surprised if Ericsson and other telcos have not had high level contact from BRN.
The word slipped that we were tied with Mercedes. After that you can bet that every Auto is testing AKIDA. No one wants to be caught short.
The use for AKIDA in Autos is obvious.
For the 'layman' it's a little harder to identify Telco company uses.
BRN has worked hard to set up an eco system and together with enormous industry exposure its hard to imagine any decent Research departments of big business would be unaware of BRN. It would be just a matter of how, if at all, hey see AKIDA improving their business.
Cheers.Hi Fullmoonfever,
the radio receiver algorithm prototype Ericsson demoed at MWC 2024 was unfortunately not implemented on Akida.
See my post dated April 18:
https://thestockexchange.com.au/threads/brn-discussion-ongoing.1/post-419427
Or in short here:
Ericsson Research Demonstrates How Intel Labs’ Neuromorphic AI Accelerator Reduces Compute Costs
Philipp Stratmann is a research scientist at Intel Labs, where he explores new neural network architectures for Loihi, Intel’s neuromorphic research AI accelerator. Co-author Péter Hága is a master researcher at Ericsson Research, where he leads research activities focusing on the applicability...community.intel.com
“Using neuromorphic computing technology from Intel Labs, Ericsson Research is developing custom telecommunications artificial intelligence (AI) models to optimize telecom architecture. Ericsson currently uses AI-based network performance diagnostics to analyze communications service providers’ radio access networks (RANs) to resolve network issues efficiently and provide specific parameter change recommendations. At Mobile World Congress (MWC) Barcelona 2024, Ericsson Research demoed a radio receiver algorithm prototype targeted for Intel’s Loihi 2 neuromorphic research AI accelerator, demonstrating a significant reduction in computational cost to improve signals across the RAN
(…)
Ericsson Research’s working prototype of a radio receiver algorithm was implemented in Lava for Loihi 2. In the demonstration, the neural network performs a common complex task of recognizing the effects of reflections and noise on radio signals as they propagate from the sender (base station) to the receiver (mobile). Then the neural network must reverse these environmental effects so that the information can be correctly decoded.”
Have a good weekend!
Frangipani
Cheers.
Wasn't aware of that and given our existing prototype with them seemed a reasonable assumption it may have been us.
They also demoed a few other prototypes at MWC.
Was trying to see where Channel Estimation fits within the RAN as the Intel post speaks of noise and reflections while still related to the general radio receiver and Ericsson blog is more specific to the channel estimation and no mention of noise and reflections.
Intel.
"...Loihi 2. In the demonstration, the neural network performs a common complex task of recognizing the effects of reflections and noise on radio signals as they propagate from the sender (base station) to the receiver (mobile). Then the neural network must reverse these environmental effects so that the information can be correctly decoded."
Ericsson
"...The live demo showed how neural activity in the channel estimation computations varied with changes in the radio channel and how energy consumption could be reduced when less or no computations were ongoing."
Is it all one and the same...channels and environmental effects or diff components of the RAN process...I don't know as a bit above my head at this point.
Will need to see how fits together.
Could it be that they demonstrated their prototype on Loihi2-base but besides work/do further research with Akida?
... maybe they swapped Loihi 2 against Akida 2.
Intel Builds World’s Largest Neuromorphic System to Enable More...
Hala Point, the industry’s first 1.15 billion neuron neuromorphic system, builds a path toward more efficient and scalable AI.www.intel.com
"About Hala Point: Loihi 2 neuromorphic processors, which form the basis for Hala Point, apply brain-inspired computing principles, such as asynchronous, event-based spiking neural networks (SNNs), integrated memory and computing, and sparse and continuously changing connections to achieve orders-of-magnitude gains in energy consumption and performance. Neurons communicate directly with one another rather than communicating through memory, reducing overall power consumption.
Hala Point packages 1,152 Loihi 2 processors produced on Intel 4 process node in a six-rack-unit data center chassis the size of a microwave oven. The system supports up to 1.15 billion neurons and 128 billion synapses distributed over 140,544 neuromorphic processing cores, consuming a maximum of 2,600 watts of power. It also includes over 2,300 embedded x86 processors for ancillary computations.
Hala Point integrates processing, memory, and communication channels in a massively parallelized fabric, providing a total of 16 petabytes per second (PB/s) of memory bandwidth, 3.5 PB/s of inter-core communication bandwidth, and 5 terabytes per second (TB/s) of inter-chip communication bandwidth. The system can process over 380 trillion 8-bit synapses and over 240 trillion neuron operations per second.
Applied to bio-inspired spiking neural network models, the system can execute its full capacity of 1.15 billion neurons 20 times faster than a human brain and up to 200 times faster rates at lower capacity. While Hala Point is not intended for neuroscience modeling, its neuron capacity is roughly equivalent to that of an owl brain or the cortex of a capuchin monkey.
Loihi-based systems can perform AI inference and solve optimization problems using 100 times less energy at speeds as much as 50 times faster than conventional CPU and GPU architectures1. By exploiting up to 10:1 sparse connectivity and event-driven activity, early results on Hala Point show the system can achieve deep neural network efficiencies as high as 15 TOPS/W2 without requiring input data to be collected into batches, a common optimization for GPUs that significantly delays the processing of data arriving in real-time, such as video from cameras. While still in research, future neuromorphic LLMs capable of continuous learning could result in gigawatt-hours of energy savings by eliminating the need for periodic re-training with ever-growing datasets."
Intel Scales Neuromorphic Research System to 100 Million Neurons
Intel announces the readiness of Pohoiki Springs, its latest and most powerful neuromorphic research system.www.intel.com
"Intel’s neuromorphic systems, such as Pohoiki Springs, are still in the research phase and are not intended to replace conventional computing systems. Instead, they provide a tool for researchers to develop and characterize new neuro-inspired algorithms for real-time processing, problem solving, adaptation and learning."
Haven’t had the time to watch it, yet, but here is a video of that talk resp. of a talk with the same name that was uploaded to YouTube just over a week prior to the Design&Reuse IP-SoC Silicon Valley 2024 Day event:
View attachment 62100
Could it be that they demonstrated their prototype on Loihi2-base but besides work/do further research with Akida?
... maybe they swapped Loihi 2 against Akida 2.
Intel Builds World’s Largest Neuromorphic System to Enable More...
Hala Point, the industry’s first 1.15 billion neuron neuromorphic system, builds a path toward more efficient and scalable AI.www.intel.com
"About Hala Point: Loihi 2 neuromorphic processors, which form the basis for Hala Point, apply brain-inspired computing principles, such as asynchronous, event-based spiking neural networks (SNNs), integrated memory and computing, and sparse and continuously changing connections to achieve orders-of-magnitude gains in energy consumption and performance. Neurons communicate directly with one another rather than communicating through memory, reducing overall power consumption.
Hala Point packages 1,152 Loihi 2 processors produced on Intel 4 process node in a six-rack-unit data center chassis the size of a microwave oven. The system supports up to 1.15 billion neurons and 128 billion synapses distributed over 140,544 neuromorphic processing cores, consuming a maximum of 2,600 watts of power. It also includes over 2,300 embedded x86 processors for ancillary computations.
Hala Point integrates processing, memory, and communication channels in a massively parallelized fabric, providing a total of 16 petabytes per second (PB/s) of memory bandwidth, 3.5 PB/s of inter-core communication bandwidth, and 5 terabytes per second (TB/s) of inter-chip communication bandwidth. The system can process over 380 trillion 8-bit synapses and over 240 trillion neuron operations per second.
Applied to bio-inspired spiking neural network models, the system can execute its full capacity of 1.15 billion neurons 20 times faster than a human brain and up to 200 times faster rates at lower capacity. While Hala Point is not intended for neuroscience modeling, its neuron capacity is roughly equivalent to that of an owl brain or the cortex of a capuchin monkey.
Loihi-based systems can perform AI inference and solve optimization problems using 100 times less energy at speeds as much as 50 times faster than conventional CPU and GPU architectures1. By exploiting up to 10:1 sparse connectivity and event-driven activity, early results on Hala Point show the system can achieve deep neural network efficiencies as high as 15 TOPS/W2 without requiring input data to be collected into batches, a common optimization for GPUs that significantly delays the processing of data arriving in real-time, such as video from cameras. While still in research, future neuromorphic LLMs capable of continuous learning could result in gigawatt-hours of energy savings by eliminating the need for periodic re-training with ever-growing datasets."
Intel Scales Neuromorphic Research System to 100 Million Neurons
Intel announces the readiness of Pohoiki Springs, its latest and most powerful neuromorphic research system.www.intel.com
"Intel’s neuromorphic systems, such as Pohoiki Springs, are still in the research phase and are not intended to replace conventional computing systems. Instead, they provide a tool for researchers to develop and characterize new neuro-inspired algorithms for real-time processing, problem solving, adaptation and learning."
Ericsson switched to testing out Loihi 2 recently.Given the previously published paper stating the use of Akida and the suggestions that BRN have been working with a communications company, the following Ericsson blog end of April on their MWC24 booth imo appears to have some different additional prototype work to my original Christmas eve post (link below) on:
Towards 6G Zero-Energy Internet of Things:
Standards, Trends, and Recent Results
- December 2023
https://www.researchgate.net/public...of_Things_Standards_Trends_and_Recent_Results
BRN Discussion Ongoing
Merry Christmas, happy holidays all Be safe and enjoy the time with loved ones, friends, fam and doing what makes you happy however you celebrate this time. Oct French article on the X320 I needed to work around through VPN, private tab etc to get to read with an English translation...thestockexchange.com.au
6G straight from the Ericsson labs
There were many questions for our experts who worked on the 6G demos in the “6G – straight from our labs” area at the Mobile World Congress (MWC) 2024. Among the most commonly-asked questions were: "Is it not too early to talk 6G?”, “What is new for 6G?”, “How does 6G relate to 5G?” and all sorts of questions on spectrum – new, existing, co-existing, reuse, coverage and more. So, let’s take the questions one-by-one… This blog will show how we take steps towards addressing some of these questions by describing what was on display at MWC 2024.
APR 30, 2024 | 7 min.
Marie Hogan
6G Portfolio Strategy, Business Area Networks
Johan Lundsjö
Strategic Research Communication Director
What is new for 6G?
6G will build on 5G Standalone and 5G-Advanced, evolving from today’s network towards the needs of 2030 and beyond. In other words, 6G will be a mix of both new and evolved concepts and use cases.
A selection of new concepts was on display straight from our 6G labs on the MWC floor.
Ultra-Low Power AI
This concept addresses two major areas of interest - AI and Energy Efficiency - topics that might seem mutually exclusive! There is rapid growth both in interest and usage of AI in mobile network operations, solutions and applications. There’s a risk that the massive amounts of data processing demanded in many AI scenarios lead to high power consumption in parallel. With increased usage of AI expected in the networks, it is important to have solutions to address this.
This live prototype for ultra-low power AI used a novel neuromorphic-AI-based approach for radio channel estimation and showcased the feasibility of low-compute and low-energy AI using AI-based radio receiver use-cases. The trick here is that in a neuromorphic neural network (like our human brain) only the neurons detecting a change are active, whereas no computations are needed for neurons in remember state. The fraction of inactive neurons translates directly to an energy efficiency gain as compared to a traditional deep neural network where computations are always needed for all neurons. The live demo showed how neural activity in the channel estimation computations varied with changes in the radio channel and how energy consumption could be reduced when less or no computations were ongoing. Radio channel estimation is only one of many areas where this exciting AI technology can be used. Keep an eye out for upcoming blogs on this topic from Ericsson soon!
View attachment 62102
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Prev paper:
View attachment 62104
View attachment 62105