BRN Discussion Ongoing
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Teach22
Regular
Hey @TECH aka Texta.Excellent post S G
This explanation below is first-class !!
Despite not specifically mentioning us, I'd politely suggest if you still don't think that a world leader in Neuromorphic Computing, with two proof of concept chips AKD 1000 and AKD 1500 and proof of concept in IP only at this stage in AKD 2000 are still wrapped up with MB, well then
stick with your Nickel, Iron Ore and Lithium stocks....Technology is the future, I know it, you know it and we are donkey deep at the cutting-edge !
Regards...Texta
In neuromorphic computing, those human neurons and synapses are modelled in circuits and communication is event-driven, with information coded in spikes, mimicking the processing fundamentals of the brain. Those spikes propagate through a Spiking Neural Network of artificial neurons and synapses to predict results. Information processing is measured by spike rate or spike time instead of the number of calculations. Thus, neuromorphic chips are more energy efficient and have lower latency than conventional CPUs and GPUs. That means much faster computation using considerably less power.
How about providing some evidence of your previous post. (Below).
BrainChip’s Akida™ neuromorphic processor has been integrated into several microcontroller units (MCUs) and embedded systems-on-chip (SoCs). Here are some notable instances:
- SiFive Essential™ Processors with Akida-E:
- These compact processors are suitable for deeply embedded applications. They combine SiFive Essential™ processors with BrainChip’s Akida-E, providing efficient solutions for low-power, small form factor devices at the edge. These MCUs are ideal for applications like audio processing, filtering, denoising for hearing aids, and hearables1.
- SiFive X280 Intelligence™ AI Dataflow Processors with Akida-S:
- For more complex applications such as object detection and robotics, the SiFive X280 Intelligence™ AI Dataflow Processors are tightly integrated with BrainChip’s Akida-S. These processors offer enhanced digital signal processing (DSP) and machine learning (ML) capabilities, making them suitable for a wide range of intelligent edge devices1.
ndefries
Regular
this isn't the case - you can search for past comments and they are there.
No comment to a number of posters...they weren't my words, the dark web conjures up all sorts of information, unless Brainchip and SiFive
make a joint statement it's purely speculation, do you believe everything you read over the internet, I honestly can't say it's fact or not.
Only thing I can say with any certainty is that our 1st quarter finishes in 13 business days, then we wait to see how the company has been performing when the 4C is released in late April 2024.
Have a good day...Tech.
Smoothsailing
Regular
Any ties to BRN does anyone know ?
I/ONX Neuromorphic
https://www.linkedin.com/company/i-onx/?miniCompanyUrn=urn:li:fs_miniCompany:96327740
Computational processes using traditional GPUs and CPUs in large data centers running complex data processing tasks significantly lack energy efficiency.
We are essentially at the fundamental limit of Artificial intelligence and machine Learning using traditional data processing technologies.
Enter neuromorphic computing. It has the potential to achieve High Performance Computing and yet consumes 1/1000th of the energy! Sound interesting?
Get in contact with one of our team members today to find out how we can transform your data center into an energy saving force by mimicking the brain's parallel processing capabilities!
View attachment 58939
Bravo
If ARM was an arm, BRN would be its biceps💪!
#ai #neuromorphiccomputing #leadincarsoftware | Magnus Östberg | 19 comments
I received many great questions from the community in response to my recent post on neuromorphic computing, so I’ll jump right in and answer a few. How does a more powerful processor increase energy efficiency? #AI is already used in advanced driving assistance systems (ADAS) and infotainment...www.linkedin.com
View attachment 58928
I received many great questions from the community in response to my recent post on neuromorphic computing, so I’ll jump right in and answer a few.
How does a more powerful processor increase energy efficiency?
#AI is already used in advanced driving assistance systems (ADAS) and infotainment, and the complex calculations are currently performed on traditional CPUs, GPUs and NPUs, which are not energy efficient. #Neuromorphiccomputing requires for the same tasks less energy. As the number of AI functions continue to increase, the increased computing efficiency of neuromorphic hardware will require less energy in comparison to legacy hardware. Reduced energy usage will also increase vehicle range and improve sustainability.
When can I experience neuromorphic computing?
Widespread use of neuromorphic computing will depend on many factors. The technology requires new programming and algorithms, so it will not immediately replace traditional processors. One key factor for us is that automotive-grade chips must meet extremely strict reliability requirements. However, we are already actively working to drive development and we are committed to being the first to use this technology in the automotive industry.
If you haven’t read the article yet, check it out here https://lnkd.in/epnUc5Sy. Be sure to ask more questions so we can keep the conversation going.
Neuromorphic computing? We’ve got that.
Because it’s still nascent technology, I am frequently asked to describe #neuromorphic computing. It is a paradigm shift for how we perform computations in machine learning (#ML) and artificial intelligence (AI), which process massive amounts of data requiring tons of fast memory.
Currently available processor architecture separates data calculations from system memory, which is inefficient. The biological inspiration for neural networks is the human brain, where computing and memory are combined, and data processing uses neurons to communicate through electrical signals and chemical processes known as neurotransmitters.
In neuromorphic computing, those human neurons and synapses are modelled in circuits and communication is event-driven, with information coded in spikes, mimicking the processing fundamentals of the brain. Those spikes propagate through a Spiking Neural Network of artificial neurons and synapses to predict results. Information processing is measured by spike rate or spike time instead of the number of calculations. Thus, neuromorphic chips are more energy efficient and have lower latency than conventional CPUs and GPUs. That means much faster computation using considerably less power.
However, this change in data processing also requires new software algorithms specifically designed to work with neuromorphic hardware. Existing algorithms can only partially leverage the many benefits of neural technology. Thanks to Valerij, Alexander, Christina in the Innovations & Future Technology area and the rest of our team for tackling this huge project!
𝗔𝗽𝗽𝗹𝗶𝗰𝗮𝘁𝗶𝗼𝗻𝘀
Neuromorphic computing reduces the power required for advanced AI computation, which is useful in applications where energy is limited, like electric vehicles. However, we still need automotive-grade chips with neuromorphic technology before this technology becomes common in cars.
We at Mercedes-Benz AG are currently working on novel algorithms that take advantage of neuromorphic computing to improve the energy efficiency and performance of our cars. Our primary goals are to extend vehicle range, make safety systems react faster, and increase the number of #AI functions possible. In 2020, we already joined the #Intel Neuromorphic Research Community and since then we are continuously expanding our collaborations with other research partners and universities to ensure our software and hardware solutions continue to lead the industry.
It's an exciting time to be in the world of automotive technology. Please share any questions and comments below.
Some interesting likes!
Hmmm but Rob don’t like it 🫡
Bravo
If ARM was an arm, BRN would be its biceps💪!
#ai #neuromorphiccomputing #leadincarsoftware | Magnus Östberg | 19 comments
I received many great questions from the community in response to my recent post on neuromorphic computing, so I’ll jump right in and answer a few. How does a more powerful processor increase energy efficiency? #AI is already used in advanced driving assistance systems (ADAS) and infotainment...
View attachment 58928
I received many great questions from the community in response to my recent post on neuromorphic computing, so I’ll jump right in and answer a few.
How does a more powerful processor increase energy efficiency?
#AI is already used in advanced driving assistance systems (ADAS) and infotainment, and the complex calculations are currently performed on traditional CPUs, GPUs and NPUs, which are not energy efficient. #Neuromorphiccomputing requires for the same tasks less energy. As the number of AI functions continue to increase, the increased computing efficiency of neuromorphic hardware will require less energy in comparison to legacy hardware. Reduced energy usage will also increase vehicle range and improve sustainability.
When can I experience neuromorphic computing?
Widespread use of neuromorphic computing will depend on many factors. The technology requires new programming and algorithms, so it will not immediately replace traditional processors. One key factor for us is that automotive-grade chips must meet extremely strict reliability requirements. However, we are already actively working to drive development and we are committed to being the first to use this technology in the automotive industry.
If you haven’t read the article yet, check it out here https://lnkd.in/epnUc5Sy. Be sure to ask more questions so we can keep the conversation going.
Neuromorphic computing? We’ve got that.
Because it’s still nascent technology, I am frequently asked to describe #neuromorphic computing. It is a paradigm shift for how we perform computations in machine learning (#ML) and artificial intelligence (AI), which process massive amounts of data requiring tons of fast memory.
Currently available processor architecture separates data calculations from system memory, which is inefficient. The biological inspiration for neural networks is the human brain, where computing and memory are combined, and data processing uses neurons to communicate through electrical signals and chemical processes known as neurotransmitters.
In neuromorphic computing, those human neurons and synapses are modelled in circuits and communication is event-driven, with information coded in spikes, mimicking the processing fundamentals of the brain. Those spikes propagate through a Spiking Neural Network of artificial neurons and synapses to predict results. Information processing is measured by spike rate or spike time instead of the number of calculations. Thus, neuromorphic chips are more energy efficient and have lower latency than conventional CPUs and GPUs. That means much faster computation using considerably less power.
However, this change in data processing also requires new software algorithms specifically designed to work with neuromorphic hardware. Existing algorithms can only partially leverage the many benefits of neural technology. Thanks to Valerij, Alexander, Christina in the Innovations & Future Technology area and the rest of our team for tackling this huge project!
𝗔𝗽𝗽𝗹𝗶𝗰𝗮𝘁𝗶𝗼𝗻𝘀
Neuromorphic computing reduces the power required for advanced AI computation, which is useful in applications where energy is limited, like electric vehicles. However, we still need automotive-grade chips with neuromorphic technology before this technology becomes common in cars.
We at Mercedes-Benz AG are currently working on novel algorithms that take advantage of neuromorphic computing to improve the energy efficiency and performance of our cars. Our primary goals are to extend vehicle range, make safety systems react faster, and increase the number of #AI functions possible. In 2020, we already joined the #Intel Neuromorphic Research Community and since then we are continuously expanding our collaborations with other research partners and universities to ensure our software and hardware solutions continue to lead the industry.
It's an exciting time to be in the world of automotive technology. Please share any questions and comments below.
Hi @Stable Genius,
It's funny when you think that NVIDIA and Mercedes only just announced in January this year their partnership in building the world's most advanced software defined vehicles.
Now we have Magnus Ostberg (Cheif Software Officer Mercedes) confirming that "traditional CPUs, GPUs and NPUs, which are not energy efficient". Additionally he states "We are already actively working to drive development and we are committed to being the first to use this technology in the automotive industry."
And in this Reuters article (see link below) in September 2023 Ola Kaellenius (CEO Mercedes) had this to say.
These statements confirm in my mind that we will be incorporated into NVIDIA's Drive Thor which is going to be launched towards the end of this year.
I wonder if we'll get any further reveals at NVIDIA's 2024 GTC Conference on 18 March?
https://www.reuters.com/business/au...iciency-is-new-currency-ev-market-2023-09-01/
Last edited:
Teach22
Regular
ndefries
Regular
hey - not sure why you are not letting this go. SiFive are a partner and they offer Akida in their chips as an option. They clearly recommend it but it may not be desirable in all sales. Ultimately it is the customer that decides this no Sifive. We have been successfully incorporated into their chip design and hopefully their work with NASA includes us given they have been testing Akida for many years now.
I wish.. but he also saysHi @Stable Genius,
It's funny when you think that NVIDIA and Mercedes only just announced in January this year their partnership in building the world's most advanced software defined vehicles.
Now we have Magnus Ostberg (Cheif Software Officer Mercedes) confirming that "traditional CPUs, GPUs and NPUs, which are not energy efficient". Additionally he states "We are already actively working to drive development and we are committed to being the first to use this technology in the automotive industry."
And in this Reuters article(link below) in Septmeber 2023 Ola Kaellenius (CEO Mercedes) had this to say.
View attachment 58959
These statements confirm in my mind that we will be incorporated into NVIDIA's Drive Thor which is going to be launched towards the end of this year.
I wonder if we'll get any further reveals at NVIDIA's 2024 GTC Conference on 18 March?
https://www.reuters.com/business/au...iciency-is-new-currency-ev-market-2023-09-01/
“Widespread use of neuromorphic computing will depend on many factors. The technology requires new programming and algorithms, so it will not immediately replace traditional processors”
I think for the long term holders of us, this will be interesting. But I don’t expect end of the year. The technology behind this is too complicated. And we need urgent a certification for using such a sensitive area like car security etc ! Let’s say 2025/2026
Bravo
If ARM was an arm, BRN would be its biceps💪!
#ai #neuromorphiccomputing #leadincarsoftware | Magnus Östberg | 19 comments
I received many great questions from the community in response to my recent post on neuromorphic computing, so I’ll jump right in and answer a few. How does a more powerful processor increase energy efficiency? #AI is already used in advanced driving assistance systems (ADAS) and infotainment...
View attachment 58928
I received many great questions from the community in response to my recent post on neuromorphic computing, so I’ll jump right in and answer a few.
How does a more powerful processor increase energy efficiency?
#AI is already used in advanced driving assistance systems (ADAS) and infotainment, and the complex calculations are currently performed on traditional CPUs, GPUs and NPUs, which are not energy efficient. #Neuromorphiccomputing requires for the same tasks less energy. As the number of AI functions continue to increase, the increased computing efficiency of neuromorphic hardware will require less energy in comparison to legacy hardware. Reduced energy usage will also increase vehicle range and improve sustainability.
When can I experience neuromorphic computing?
Widespread use of neuromorphic computing will depend on many factors. The technology requires new programming and algorithms, so it will not immediately replace traditional processors. One key factor for us is that automotive-grade chips must meet extremely strict reliability requirements. However, we are already actively working to drive development and we are committed to being the first to use this technology in the automotive industry.
If you haven’t read the article yet, check it out here https://lnkd.in/epnUc5Sy. Be sure to ask more questions so we can keep the conversation going.
Neuromorphic computing? We’ve got that.
Because it’s still nascent technology, I am frequently asked to describe #neuromorphic computing. It is a paradigm shift for how we perform computations in machine learning (#ML) and artificial intelligence (AI), which process massive amounts of data requiring tons of fast memory.
Currently available processor architecture separates data calculations from system memory, which is inefficient. The biological inspiration for neural networks is the human brain, where computing and memory are combined, and data processing uses neurons to communicate through electrical signals and chemical processes known as neurotransmitters.
In neuromorphic computing, those human neurons and synapses are modelled in circuits and communication is event-driven, with information coded in spikes, mimicking the processing fundamentals of the brain. Those spikes propagate through a Spiking Neural Network of artificial neurons and synapses to predict results. Information processing is measured by spike rate or spike time instead of the number of calculations. Thus, neuromorphic chips are more energy efficient and have lower latency than conventional CPUs and GPUs. That means much faster computation using considerably less power.
However, this change in data processing also requires new software algorithms specifically designed to work with neuromorphic hardware. Existing algorithms can only partially leverage the many benefits of neural technology. Thanks to Valerij, Alexander, Christina in the Innovations & Future Technology area and the rest of our team for tackling this huge project!
𝗔𝗽𝗽𝗹𝗶𝗰𝗮𝘁𝗶𝗼𝗻𝘀
Neuromorphic computing reduces the power required for advanced AI computation, which is useful in applications where energy is limited, like electric vehicles. However, we still need automotive-grade chips with neuromorphic technology before this technology becomes common in cars.
We at Mercedes-Benz AG are currently working on novel algorithms that take advantage of neuromorphic computing to improve the energy efficiency and performance of our cars. Our primary goals are to extend vehicle range, make safety systems react faster, and increase the number of #AI functions possible. In 2020, we already joined the #Intel Neuromorphic Research Community and since then we are continuously expanding our collaborations with other research partners and universities to ensure our software and hardware solutions continue to lead the industry.
It's an exciting time to be in the world of automotive technology. Please share any questions and comments below.
"We are committed to being the first to use this technology in the automotive industry".
Mercedes/NVIDIA better get a wriggle on then! They might get pipped at the post by Valeo (Scala 3).
Here it is!
Definition of DUMBASS
a foolish or stupid person : idiot… See the full definition
Bravo
If ARM was an arm, BRN would be its biceps💪!
Coolio!
Except I haven't got a subscription so I can't access the whole article.
Except I haven't got a subscription so I can't access the whole article.
Edge AI to boost automotive and industrial chip demand, says Renesas
The automotive and industrial automation sectors remain the most significant drivers of development for edge AI, according to Renesas during a recent technology forum it hosted in Taiwan.
www.digitimes.com
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