Pom down under
Top 20
Must be the 1st time since I’ve been invested in BRN that the SP has fallen before a quarterly thanks to LDA and hopefully this drop in SP continues as my super should be ready next week some time.
BRN Discussion Ongoing
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rgupta
Regular
Should we seriously believe Microsoft and open ai should talk specifically about brainchip involvement in their 100 billion dollar project.
On the other hand market is becoming very very competitive and it will be difficult to ignore better technology.
Smoothsailing
Regular
How’s our 3 year lead going ?.
Pom down under
Top 20
Last edited:
Makeme 2020
Regular
Up the shit.
Mmm, that person photographed next to Mischa, looks like a young Pia. I wonder if they are related.Here is a follow-up of my post on Ericsson (see above).
When I recently searched for info on Ericsson’s interest in neuromorphic technology besides the Dec 2023 paper, in which six Ericsson researchers described how they had built a prototype of an AI-enabled ZeroEnergy-IoT device utilising Akida, I not only came across an Ericsson Senior Researcher for Future Computing Platforms who was very much engaged with Intel’s Loihi (he even gave a presentation at the Intel Neuromorphic Research Community’s Virtual Summer 2023 Workshop), as well as an Intel podcast with Ericsson’s VP of Emerging Technologies, Mischa Dohler.
I also spotted the following LinkedIn post by a Greek lady, who had had a successful career at Ericsson spanning more than 23 years before taking the plunge into self-employment two years ago:
View attachment 61042
View attachment 61043
Since Maria Boura concluded her post by sharing that very Intel podcast with Mischa Dohler mentioned earlier, my gut feeling was that those Ericsson 6G researchers she had talked to at MWC (Mobile World Congress) 2024 in Barcelona at the end of February had most likely been collaborating with Intel, but a quick Google search didn’t come up with any results at the time I first saw that post of hers back in March.
Then, last night, while reading an article on Intel’s newly revealed Hala Point (https://www.eejournal.com/industry_...morphic-system-to-enable-more-sustainable-ai/), there it was - the undeniable evidence that those Ericsson researchers had indeed been utilising Loihi 2:
“Advancing on its predecessor, Pohoiki Springs, with numerous improvements, Hala Point now brings neuromorphic performance and efficiency gains to mainstream conventional deep learning models, notably those processing real-time workloads such as video, speech and wireless communications. For example, Ericsson Research is applying Loihi 2 to optimize telecom infrastructure efficiency, as highlighted at this year’s Mobile World Congress.”
The blue link connects to the following article on the Intel website, published yesterday:
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
Ericsson Research Demonstrates How Intel Labs’ Neuromorphic AI Accelerator Reduces Compute Costs
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Philipp_Stratmann
Employee
04-17-2024
00344
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 of neuromorphic and AI technologies to telecommunication tasks.
Highlights
- Using neuromorphic computing technology from Intel Labs, Ericsson Research is developing custom telecommunications AI models to optimize telecom architecture.
- Ericsson Research developed a radio receiver prototype for Intel’s Loihi 2 neuromorphic AI accelerator based on neuromorphic spiking neural networks, which reduced the data communication by 75 to 99% for energy efficient radio access networks (RANs).
- As a member of Intel’s Neuromorphic Research Community, Ericsson Research is searching for new AI technologies that provide energy efficiency and low latency inference in telecom systems.
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.
In 2021, Ericsson Research joined the Intel Neuromorphic Research Community (INRC), a collaborative research effort that brings together academic, government, and industry partners to work with Intel to drive advances in real-world commercial usages of neuromorphic computing.
Ericsson Research is actively searching for new AI technologies that provide low latency inference and energy efficiency in telecom systems. Telecom networks face many challenges, including tight latency constraints driven by the need for data to travel quickly over the network, and energy constraints due to mobile system battery limitations. AI will play a central role in future networks by optimizing, controlling, and even replacing key components across the telecom architecture. AI could provide more efficient resource utilization and network management as well as higher capacity.
Neuromorphic computing draws insights from neuroscience to create chips that function more like the biological brain instead of conventional computers. It can deliver orders of magnitude improvements in energy efficiency, speed of computation, and adaptability across a range of applications, including real-time optimization, planning, and decision-making from edge to data center systems. Intel's Loihi 2 comes with Lava, an open-source software framework for developing neuro-inspired applications.
Radio Receiver Algorithm Prototype
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.
During training, researchers rewarded the model based on accuracy and the amount of communication between neurons. As a result, the neural communication was reduced, or sparsified, by 75 to 99% depending on the difficulty of the radio environment and the amount of work needed by the AI to correct the environmental effects on the signal.
Loihi 2 is built to leverage such sparse messaging and computation. With its asynchronous spike-based communication, neurons do not need to compute or communicate information when there is no change. Furthermore, Loihi 2 can compute with substantially less power due to its tight compute-memory integration. This reduces the energy and latency involved in moving data between the compute unit and the memory.
Like the human brain’s biological neural circuits that can intelligently process, respond to, and learn from real-world data at microwatt power levels and millisecond response times, neuromorphic computing can unlock orders of magnitude gains in efficiency and performance.
Neuromorphic computing AI solutions could address the computational power needed for future intelligent telecom networks. Complex telecom computation results must be produced in tight deadlines down to the millisecond range. Instead of using GPUs that draw substantial amounts of power, neuromorphic computing can provide faster processing and improved energy efficiency.
Emerging Technologies and Telecommunications
Learn more about emerging technologies and telecommunications in this episode of InTechnology. Host Camille Morhardt interviews Mischa Dohler, VP of Emerging Technologies at Ericsson, about neuromorphic computing, quantum computing, and more.
While Ericsson being deeply engaged with Intel even in the area of neuromorphic research doesn’t preclude them from also knocking on BrainChip’s door, this new reveal reaffirms my hesitation about adding Ericsson to our list of companies above the waterline, given the lack of any official acknowledgment by either party to date.
So to sum it up: Ericsson collaborating with Intel in neuromorphic research is a verifiable fact, while an NDA with BrainChip is merely speculation so far.
Frangipani
Top 20
New LinkedIn post by Laurent Hili from ESA who is looking forward to attending the 2024 Hardware & Edge AI Summit in San Jose in September…
www.linkedin.com
Note that the pictures were taken at the 2022 Hardware & Edge AI Summit.
LinkedIn Login, Sign in | LinkedIn
Login to LinkedIn to keep in touch with people you know, share ideas, and build your career.
Note that the pictures were taken at the 2022 Hardware & Edge AI Summit.
BrainShit
Regular
BrainChip selected by U.S. Air Force Research Laboratory to develop AI-based radar
2024-04-19 17:52
BrainChip, the world's first commercial producer of neuromorphic artificial intelligence chips and IP, today announced that Information Systems Laboratories (ISL) is developing an AI-based radar for the U.S. Air Force Research Laboratory (AFRL) based on its Akida™ Neural Network Processor Research solutions.
ISL is a specialist in expert research and complex analysis, software and systems engineering, advanced hardware design and development, and high-quality manufacturing for a variety of clients worldwide.
Military and Space are always leaders of new technologies
2024-04-19 17:52
BrainChip, the world's first commercial producer of neuromorphic artificial intelligence chips and IP, today announced that Information Systems Laboratories (ISL) is developing an AI-based radar for the U.S. Air Force Research Laboratory (AFRL) based on its Akida™ Neural Network Processor Research solutions.
ISL is a specialist in expert research and complex analysis, software and systems engineering, advanced hardware design and development, and high-quality manufacturing for a variety of clients worldwide.
Military and Space are always leaders of new technologies
Frank Zappa
Regular
All this secrecy is running a little thin . Especially when everyone else is discussing what everyone else is doing with who ever
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Case Study - Design and Development of Motor Control Unit (MCU) for Electric Vehicles
Explore our case study on the development of a Motor Control Unit (MCU) for Electric vehicles through the NEVIC partnership with Renesas
eu1.hubs.ly
Frangipani
Top 20
BrainChip selected by U.S. Air Force Research Laboratory to develop AI-based radar
2024-04-19 17:52
BrainChip, the world's first commercial producer of neuromorphic artificial intelligence chips and IP, today announced that Information Systems Laboratories (ISL) is developing an AI-based radar for the U.S. Air Force Research Laboratory (AFRL) based on its Akida™ Neural Network Processor Research solutions.
ISL is a specialist in expert research and complex analysis, software and systems engineering, advanced hardware design and development, and high-quality manufacturing for a variety of clients worldwide.
Military and Space are always leaders of new technologies
Hi BrainShit,
while it looks like it is hot off the press-news due to today’s publication date on the website, this press release is actually more than two years old:
Information Systems Labs Joins BrainChip Early Access Program
BrainChip announced that Information Systems Labs is developing an AI-based radar research solution for the AFRL based on Akida™
brainchip.com
Regards
Frangipani
Sorry Frangi, but i have to say that has to be a record shortest post from you doesn't it?!Hi BrainShit,
while it looks like it is hot off the press-news due to today’s publication date on the website, this press release is actually more than two years old:
Information Systems Labs Joins BrainChip Early Access Program
BrainChip announced that Information Systems Labs is developing an AI-based radar research solution for the AFRL based on Akida™
Regards
Frangipani
Frangipani
Top 20
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.Here is a follow-up of my post on Ericsson (see above).
When I recently searched for info on Ericsson’s interest in neuromorphic technology besides the Dec 2023 paper, in which six Ericsson researchers described how they had built a prototype of an AI-enabled ZeroEnergy-IoT device utilising Akida, I not only came across an Ericsson Senior Researcher for Future Computing Platforms who was very much engaged with Intel’s Loihi (he even gave a presentation at the Intel Neuromorphic Research Community’s Virtual Summer 2023 Workshop), as well as an Intel podcast with Ericsson’s VP of Emerging Technologies, Mischa Dohler.
I also spotted the following LinkedIn post by a Greek lady, who had had a successful career at Ericsson spanning more than 23 years before taking the plunge into self-employment two years ago:
View attachment 61042
View attachment 61043
Since Maria Boura concluded her post by sharing that very Intel podcast with Mischa Dohler mentioned earlier, my gut feeling was that those Ericsson 6G researchers she had talked to at MWC (Mobile World Congress) 2024 in Barcelona at the end of February had most likely been collaborating with Intel, but a quick Google search didn’t come up with any results at the time I first saw that post of hers back in March.
Then, last night, while reading an article on Intel’s newly revealed Hala Point (https://www.eejournal.com/industry_...morphic-system-to-enable-more-sustainable-ai/), there it was - the undeniable evidence that those Ericsson researchers had indeed been utilising Loihi 2:
“Advancing on its predecessor, Pohoiki Springs, with numerous improvements, Hala Point now brings neuromorphic performance and efficiency gains to mainstream conventional deep learning models, notably those processing real-time workloads such as video, speech and wireless communications. For example, Ericsson Research is applying Loihi 2 to optimize telecom infrastructure efficiency, as highlighted at this year’s Mobile World Congress.”
The blue link connects to the following article on the Intel website, published yesterday:
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
Ericsson Research Demonstrates How Intel Labs’ Neuromorphic AI Accelerator Reduces Compute Costs
Subscribe
Article Options
Philipp_Stratmann
Employee
04-17-2024
00344
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 of neuromorphic and AI technologies to telecommunication tasks.
Highlights
- Using neuromorphic computing technology from Intel Labs, Ericsson Research is developing custom telecommunications AI models to optimize telecom architecture.
- Ericsson Research developed a radio receiver prototype for Intel’s Loihi 2 neuromorphic AI accelerator based on neuromorphic spiking neural networks, which reduced the data communication by 75 to 99% for energy efficient radio access networks (RANs).
- As a member of Intel’s Neuromorphic Research Community, Ericsson Research is searching for new AI technologies that provide energy efficiency and low latency inference in telecom systems.
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.
In 2021, Ericsson Research joined the Intel Neuromorphic Research Community (INRC), a collaborative research effort that brings together academic, government, and industry partners to work with Intel to drive advances in real-world commercial usages of neuromorphic computing.
Ericsson Research is actively searching for new AI technologies that provide low latency inference and energy efficiency in telecom systems. Telecom networks face many challenges, including tight latency constraints driven by the need for data to travel quickly over the network, and energy constraints due to mobile system battery limitations. AI will play a central role in future networks by optimizing, controlling, and even replacing key components across the telecom architecture. AI could provide more efficient resource utilization and network management as well as higher capacity.
Neuromorphic computing draws insights from neuroscience to create chips that function more like the biological brain instead of conventional computers. It can deliver orders of magnitude improvements in energy efficiency, speed of computation, and adaptability across a range of applications, including real-time optimization, planning, and decision-making from edge to data center systems. Intel's Loihi 2 comes with Lava, an open-source software framework for developing neuro-inspired applications.
Radio Receiver Algorithm Prototype
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.
During training, researchers rewarded the model based on accuracy and the amount of communication between neurons. As a result, the neural communication was reduced, or sparsified, by 75 to 99% depending on the difficulty of the radio environment and the amount of work needed by the AI to correct the environmental effects on the signal.
Loihi 2 is built to leverage such sparse messaging and computation. With its asynchronous spike-based communication, neurons do not need to compute or communicate information when there is no change. Furthermore, Loihi 2 can compute with substantially less power due to its tight compute-memory integration. This reduces the energy and latency involved in moving data between the compute unit and the memory.
Like the human brain’s biological neural circuits that can intelligently process, respond to, and learn from real-world data at microwatt power levels and millisecond response times, neuromorphic computing can unlock orders of magnitude gains in efficiency and performance.
Neuromorphic computing AI solutions could address the computational power needed for future intelligent telecom networks. Complex telecom computation results must be produced in tight deadlines down to the millisecond range. Instead of using GPUs that draw substantial amounts of power, neuromorphic computing can provide faster processing and improved energy efficiency.
Emerging Technologies and Telecommunications
Learn more about emerging technologies and telecommunications in this episode of InTechnology. Host Camille Morhardt interviews Mischa Dohler, VP of Emerging Technologies at Ericsson, about neuromorphic computing, quantum computing, and more.
While Ericsson being deeply engaged with Intel even in the area of neuromorphic research doesn’t preclude them from also knocking on BrainChip’s door, this new reveal reffirms my hesitation about adding Ericsson to our list of companies above the waterline, given the lack of any official acknowledgment by either party to date.
So to sum it up: Ericsson collaborating with Intel in neuromorphic research is a verifiable fact, while an NDA with BrainChip is merely speculation so far.
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.
Evening BrainShit ,BrainChip selected by U.S. Air Force Research Laboratory to develop AI-based radar
2024-04-19 17:52
BrainChip, the world's first commercial producer of neuromorphic artificial intelligence chips and IP, today announced that Information Systems Laboratories (ISL) is developing an AI-based radar for the U.S. Air Force Research Laboratory (AFRL) based on its Akida™ Neural Network Processor Research solutions.
ISL is a specialist in expert research and complex analysis, software and systems engineering, advanced hardware design and development, and high-quality manufacturing for a variety of clients worldwide.
Military and Space are always leaders of new technologies
1 , if one could find a more salubrious forum name would be great.
But yes , truely sense & feel ones torment.
2, Not 100% certain , but pretty sure this is a rehashed announcement from some time ago..... with the pressent day date attached , purely to confuse.
Regards,
Esq.
DingoBorat
Slim
Assuming Apple make the decision to pursue their own way and manage to cobble something together which is workable (which is highly likely, given their resources).
What are the chances of them offering this tech, to their competitors, to also use?...
The problems Apple had, with their flagship iPhone 15 overheating last year, goes to show they are maybe not as "hot shit" as they think they are..
Maybe they are literally...
Happy to change my opinion, if they come to their senses
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I wonder how long ISL had been playing with Akida before they got the Airforce radar SBIR?
This patent was filed in mid 2021, so 5 months before the announcement.
US11256988B1 Process and method for real-time sensor neuromorphic processing 20210719
[0003] The invention relates to the general field of deep learning neural networks, which has enjoyed a great deal of success in recent years. This is attributed to more advanced neural architectures that more closely resemble the human brain. Neural networks work with functionalities similar to the human brain. The invention includes both a training cycle and a live (online) operation. The training cycle includes five elements and comprises the build portion of the deep learning process. The training cycle requirements ensure adequate convergence and performance. The live (online) operation includes the live operation of a Spiking Neural Network (SNN) designed by the five steps of the training cycle. The invention is part of a new generation of neuromorphic computing architectures, including Integrated Circuits (IC). This new generation of neuromorphic computing architectures includes IC, deep learning and machine learning.
1. A method of providing real-time sensor neuromorphic processing, the method comprising the steps of:
providing a training cycle and a live operation cycle;
wherein the training cycle includes:
(1) the establishment of a build portion or training cycle of a deep learning process with AI;
wherein the build portion or training cycle begins with the process taking performance requirements in the form of generation of a scenario as inputs;
(2) selecting a sensor model application, with associated performance specifications set forth in the generation of a scenario;
(3) providing a Hi-Fi Radio-Frequency (RF) sensor model which is used to augment any real data for training;
(4) providing a computer model surrogate which is used instead of, or in addition to, a non-surrogate computer model;
(5) the sensor model application being one or more of radar, sonar or LIDAR;
(6) specifying an operating environment details wherein the Hi-Fi sensor model generates requisite training data and/or training environment;
(7) the Hi-Fi sensor model generates training data in a quantity to ensure convergence of DNN neuron weights, wherein as an input enters the node, the input gets multiplied by a weight value and the resulting output is either observed, or passed to the next layer in the neural network;
(8) raw sensor training data is preprocessed into a format suitable for presentation to a DNN from a DNN interface, the sensor model application training data is forwarded to the DNN;
(9) the training environment information is then output from the DNN to a DNN-to-SNN operation through a DNN-to-SNN conversion; and
(10) thereafter, the DNN is converted to an SNN and the SNN outputs the SNN information to an neuromorphic integrated circuit (IC), creating a neuromorphic sensor application;
(11) providing and utilizing a statistical method which ensures reliable performance of the neuromorphic integrated circuit, wherein reliability is 99%.
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If their potential customers have done their DD, they will be aware of Akida.Assuming Apple make the decision to pursue their own way and manage to cobble something together which is workable (which is highly likely, given their resources).
What are the chances of them offering this tech, to their competitors, to also use?...
The problems Apple had, with their flagship iPhone 15 overheating last year, goes to show they are maybe not as "hot shit" as they think they are..
Maybe they are literally...
Happy to change my opinion, if they come to their senses
Using MACs makes the Apple system much less efficient and slower than Akida.
That would make it a different equation for the potential customers in not having the sunk costs of developing a second-rate in-house system.
As you point out, using the iPhone as a handwarmer did use up the battery quickly.
Diogenes, just a query concerning tge original patent that expires in 2028.
It's been improved over and over with several more patents added over the years until we got GEN 2 with GEN 3 next year with possibly another patent application.
How do you see the expiry of the original patent effecting our business?
My view is that its old tech now with ongoing further patents improving the original, but it will still enable others to add their own improvements.
I guess it depends mainly on how much market traction we can gain over the next few years with Edge AI starting to take off.
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