BRN Discussion Ongoing

cosors

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Ok....try a different track haha

Mathworks had their Automotive conference recently in Oct.

Was a few presentations (slides & vids) and obviously revolves around Matlab, Simulink etc.

NXP, MAN, Stellantis amongst others there. Didn't notice NVIDIA on glance through.

I did find MB had a couple of presso's and one looked interesting.

Mention of work being done...e.g LSTM's.

How nice would it be for MB to have a prototype Akida 2.0 (LSTM) for test & design work?

Though, I might need to keep that box handy if @Diogenese reads this post :LOL:

Links which have the vids and slide packs below.




Couple snips.


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1671683444291.png
 
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jk6199

Regular
Merry Christmas me, even with Socio's news these prices are good value in my opinion, and just bought more.

Hope everyone has a safe and very Merry Christmas & a safe and Happy New Year.

Lets hope the couch surfing groupies of this site can get a great update to their lounges in 2023.
 
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Don’t suppose Foxconn and Socionext having a long association and multiple product partnerships would have any implications for Brainchip. 😂🤣😂🤡😂🤣😎😵‍💫😇
Just think how much more power efficient this could be with a little bit of AKIDA by its side:


A little bit of AKIDA in my life
A little bit of AKIDA by my side
A little bit of AKIDA’s all I need
A little bit of AKIDA what I see
A little bit of AKIDA in the sun
A little bit of AKIDA all night long
A little bit of AKIDA , here I am
A little bit of AKIDA makes SOCIONEXT a fan (ah)

My opinion only DYOR
FF

AKIDA BALLISTA
 
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HopalongPetrovski

I'm Spartacus!
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Just think how much more power efficient this could be with a little bit of AKIDA by its side:


A little bit of AKIDA in my life
A little bit of AKIDA by my side
A little bit of AKIDA’s all I need
A little bit of AKIDA what I see
A little bit of AKIDA in the sun
A little bit of AKIDA all night long
A little bit of AKIDA , here I am
A little bit of AKIDA makes SOCIONEXT a fan (ah)

My opinion only DYOR
FF

AKIDA BALLISTA


Not sure I’d want to be involved with Foxconn via Socionext; not sure they’re big enough to worry about:

In 2018, Foxconn achieved US$175 billion in revenue and has received an array of international accolades and recognition. The company was ranked 23rd in the Fortune Global 500 rankings in 2018 and 215th in the Forbes ranking of the World’s Best Employers that year. In 2019, the company was ranked 21st for Sales and was ranked 123rd overall in the Forbes Global 2000.

😂
 
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BaconLover

Founding Member
Christmas Shopping GIF


Merry Christmas to you all.

When we get our Christmas present 🎁 don't forget to have a plan. We don't want to be blind sighted when the quick run up comes along.

Enjoy HIS birthday and have a great New Year 🥳🥳🥳🥳
 
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This article preceded last years CES event:

In-Car Voice Makes Itself Heard at CES​

BY PYMNTS | JANUARY 4, 2022
|
CES, connected cars, automotive


Before CES even opened to the industry, in-car voice technology was making itself heard. On Monday (Jan. 3), the first of two media days preceding the opening of the show, connected mobility supplier Cerence received an award for its in-car voice assistant, Mercedes-Benz released details about two voice technologies featured on its new prototype electric car and the Consumer Technology Association (CTA) projected that auto tech will grow 7% in 2022.
Read more: Connected Cars to Strut Their Stuff at CES
These companies are among more than 200 from the transportation and vehicle technology industry — a record number for the event — represented at this year’s edition of the annual tech event. A total of 2,200 companies are taking part in person or in the event’s digital venues.
Proactively Delivering Information to Drivers
On Monday, Cerence received a CES 2022 Innovation Award for Cerence Co-Pilot, its new in-car voice assistant that is powered by artificial intelligence (AI).
The assistant not only responds to voice commands, but also uses data from the car’s sensors to understand situations inside and outside the vehicle and proactively deliver information when it’s needed. For example, as the vehicle nears the driver’s home, Cerence Co-Pilot may ask if they’d like it to initiate a smart home routine. This in-car voice assistant also integrates with cloud services.
“AI is deeply fundamental to the future of mobility, and we see our role as critical, not only in bringing convenient, enjoyable and safe experiences to drivers, but also giving OEMs the ability to maintain control of their brands and data while still giving drivers the secure, seamless and personalized connected experiences they want,” Cerence CEO Stefan Ortmanns said in a press release.
Sounding Impressively Real, Natural and Intuitive
On the same day, Mercedes-Benz previewed two voice technologies that will be displayed on its VISION EQXX, a research prototype car featuring an electric drivetrain and advanced software. The automaker says this prototype demonstrates its transformation into “an all-electric and software-driven company.”
One voice technology featured on the VISION EQXX makes it “fun to talk to,” Mercedes-Benz says. Developed in collaboration with voice synthesis experts Sonantic and with the help of machine learning, this version of the “Hey Mercedes” voice assistant has a distinctive character and personality.
“As well as sounding impressively real, the emotional expression places the conversation between driver and car on a completely new level that is more natural and intuitive,” Mercedes-Benz said in a press release.
The second voice-related technology previewed by Mercedes-Benz features neuromorphic computing, a form of information processing that reduces energy consumption, and AI software and hardware from BrainChip that is five to 10 times more efficient than conventional voice control.
“Although neuromorphic computing is still in its infancy, systems like these will be available on the market in just a few years,” Mercedes-Benz said. “When applied on scale throughout a vehicle, they have the potential to radically reduce the energy needed to run the latest AI technologies.”
Growing Demand for Automotive Tech
Also on Jan. 3, CTA announced that it projects that factory-installed automotive tech will grow 7% this year — from $14.9 billion in shipment revenues in 2021 to $16 billion in 2022 — driven by the beginning of a recovery in chip supplies as well as greater demand.
“Demand for automotive tech is increasing as auto manufacturers produce more and continue to develop advanced driver assistance systems that make vehicles more efficient and safer,” the association said when announcing the release of its twice-yearly “U.S. Consumer Technology One-Year Industry Forecast.”
In other vehicle tech news from CES, VinFast announced that customers in the U.S. and Vietnam will be able to make reservations for its first two electric vehicle models beginning Wednesday (Jan. 5) and that it will use blockchain technologies to certify reservations, payments and eventually vehicle ownership.
 
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alwaysgreen

Top 20
Most of all for my Xmas wish I am hoping Putin gets struck by lightning so Ukraine can have some peace to rebuild their country!

:)
Isn't he riddled with cancer?
 

Quatrojos

Regular
I’m hoping for a trading halt before CES. Surely, if there’s sensitive info, this must occur…
 
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This is a positive view of where Brainchip is going in automotive. Mercedes Benz sells around an average of 3 million passenger vehicles and Jerome Nadel places 70 AKIDA chips pre processing sensor inputs before passing on as meta data. If Blind Freddie's mental arithmetic is correct that is 210 million AKIDA smart sensors.


"BrainChip Akida


Mercedes-Benz's EQXX concept car, which debuted at CES earlier this year, uses BrainChip's Akida neuromorphic processor for in-vehicle keyword recognition. Billed as "the most efficient car Mercedes has ever made," the car utilizes neuromorphic technology that consumes less power than a deep learning-based keyword spotting system. That's crucial for a car with a range of 620 miles, or 167 miles more than Mercedes' flagship electric car, the EQS.

Mercedes said at the time that BrainChip's solution was five to 10 times more efficient than traditional voice controls at recognizing the wake word "Hey Mercedes."

Application of SNN in Vehicle Field


Mercedes said, “Although neuromorphic computing is still in its infancy, such systems will soon be on the market within a few years. When applied at scale throughout vehicles, they have the potential to radically reduce the amount of effort required to run the latest AI technologies. power consumption."


BrainChip's CMO Jerome Nadel said: "Mercedes is focused on big issues like battery management and transmission, but every milliwatt counts, and when you think about energy efficiency, even the most basic reasoning, like finding keywords, matters. important."

A typical car could have as many as 70 different sensors by 2022, Nadel said. For cockpit applications, these sensors can enable face detection, gaze assessment, emotion classification, and more.

He said: “From a system architecture perspective, we can do a 1:1 approach where there is a sensor that will do some preprocessing and then the data will be forwarded. The AI will do inference near the sensor...it will Instead of the full array of data from sensors, the inference metadata is passed forward.”

The idea is to minimize the size and complexity of packets sent to AI accelerators, while reducing latency and minimizing power consumption. Each vehicle will likely have 70 Akida chips or sensors with Akida technology, each of which will be "low-cost parts that won't notice them at all," Nadel said. He noted that attention needs to be paid to the BOM of all these sensors.


Application of SNN in Vehicle Field


BrainChip expects to have its neuromorphic processor next to every sensor on the vehicle

Going forward, Nadel said, neuromorphic processing will also be used in ADAS and autonomous driving systems. This has the potential to reduce the need for other types of power-hungry AI accelerators.

"If every sensor could have Akida configured on one or two nodes, it would do adequate inference, and the data passed would be an order of magnitude less, because that would be inference metadata...that would affect the servers you need," he said. power."


BrainChip's Akida chip accelerates SNNs (spike neural networks) and CNNs (by converting to SNNs). It's not tailored for any specific use case or sensor, so it can be paired with visual sensing for face recognition or people detection, or other audio applications like speaker ID. BrainChip also demonstrated Akida's smell and taste sensors, although it's hard to imagine how these could be used in cars (perhaps to detect air pollution or fuel quality through smell and taste).

Akida is set up to handle SNNs or deep learning CNNs that have been transformed into SNNs. Unlike the native spike network, the transformed CNN preserves some spike-level information, so it may require 2 or 4 bits of computation. However, this approach allows exploiting the properties of CNNs, including their ability to extract features from large datasets. Both types of networks can be updated at the edge using STDP. In the case of Mercedes-Benz, this might mean retraining the network after deployment to discover more or different keywords.

Application of SNN in Vehicle Field


According to Autocar, Mercedes-Benz confirmed that "many innovations" from the EQXX concept car, including "specific components and technologies," will be used in the production model. There's no word yet on whether new Mercedes-Benz models will feature artificial brains."

I do hope you read the whole post and not just the orange text😂🤣😂🤣 - (🐫x1000)

My opinion only DYOR
FF

AKIDA BALLISTA
 
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buena suerte :-)

BOB Bank of Brainchip
If my wife reads this she will confirm that I am a camel.

This is a positive view of where Brainchip is going in automotive. Mercedes Benz sells around an average of 3 million passenger vehicles and Jerome Nadel places 70 AKIDA chips pre processing sensor inputs before passing on as meta data. If Blind Freddie's mental arithmetic is correct that is 270 million AKIDA smart sensors.


"BrainChip Akida


Mercedes-Benz's EQXX concept car, which debuted at CES earlier this year, uses BrainChip's Akida neuromorphic processor for in-vehicle keyword recognition. Billed as "the most efficient car Mercedes has ever made," the car utilizes neuromorphic technology that consumes less power than a deep learning-based keyword spotting system. That's crucial for a car with a range of 620 miles, or 167 miles more than Mercedes' flagship electric car, the EQS.

Mercedes said at the time that BrainChip's solution was five to 10 times more efficient than traditional voice controls at recognizing the wake word "Hey Mercedes."

Application of SNN in Vehicle Field


Mercedes said, “Although neuromorphic computing is still in its infancy, such systems will soon be on the market within a few years. When applied at scale throughout vehicles, they have the potential to radically reduce the amount of effort required to run the latest AI technologies. power consumption."


BrainChip's CMO Jerome Nadel said: "Mercedes is focused on big issues like battery management and transmission, but every milliwatt counts, and when you think about energy efficiency, even the most basic reasoning, like finding keywords, matters. important."

A typical car could have as many as 70 different sensors by 2022, Nadel said. For cockpit applications, these sensors can enable face detection, gaze assessment, emotion classification, and more.

He said: “From a system architecture perspective, we can do a 1:1 approach where there is a sensor that will do some preprocessing and then the data will be forwarded. The AI will do inference near the sensor...it will Instead of the full array of data from sensors, the inference metadata is passed forward.”

The idea is to minimize the size and complexity of packets sent to AI accelerators, while reducing latency and minimizing power consumption. Each vehicle will likely have 70 Akida chips or sensors with Akida technology, each of which will be "low-cost parts that won't notice them at all," Nadel said. He noted that attention needs to be paid to the BOM of all these sensors.


Application of SNN in Vehicle Field


BrainChip expects to have its neuromorphic processor next to every sensor on the vehicle

Going forward, Nadel said, neuromorphic processing will also be used in ADAS and autonomous driving systems. This has the potential to reduce the need for other types of power-hungry AI accelerators.

"If every sensor could have Akida configured on one or two nodes, it would do adequate inference, and the data passed would be an order of magnitude less, because that would be inference metadata...that would affect the servers you need," he said. power."


BrainChip's Akida chip accelerates SNNs (spike neural networks) and CNNs (by converting to SNNs). It's not tailored for any specific use case or sensor, so it can be paired with visual sensing for face recognition or people detection, or other audio applications like speaker ID. BrainChip also demonstrated Akida's smell and taste sensors, although it's hard to imagine how these could be used in cars (perhaps to detect air pollution or fuel quality through smell and taste).

Akida is set up to handle SNNs or deep learning CNNs that have been transformed into SNNs. Unlike the native spike network, the transformed CNN preserves some spike-level information, so it may require 2 or 4 bits of computation. However, this approach allows exploiting the properties of CNNs, including their ability to extract features from large datasets. Both types of networks can be updated at the edge using STDP. In the case of Mercedes-Benz, this might mean retraining the network after deployment to discover more or different keywords.

Application of SNN in Vehicle Field


According to Autocar, Mercedes-Benz confirmed that "many innovations" from the EQXX concept car, including "specific components and technologies," will be used in the production model. There's no word yet on whether new Mercedes-Benz models will feature artificial brains."

I do hope you read the whole post and not just the orange text😂🤣😂🤣 - (🐫x1000)

My opinion only DYOR
FF

AKIDA BALLISTA
Each vehicle will likely have 70 Akida chips or sensors with Akida technology, each of which will be "low-cost parts that won't notice them at all," Nadel said. He noted that attention needs to be paid to the BOM of all these sensors.

Awesome post FF ..... :ROFLMAO::ROFLMAO::ROFLMAO::ROFLMAO:😝😝 🤪🤪
 
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ChipMan

Founding Member
Not sure I’d want to be involved with Foxconn via Socionext; not sure they’re big enough to worry about:

In 2018, Foxconn achieved US$175 billion in revenue and has received an array of international accolades and recognition. The company was ranked 23rd in the Fortune Global 500 rankings in 2018 and 215th in the Forbes ranking of the World’s Best Employers that year. In 2019, the company was ranked 21st for Sales and was ranked 123rd overall in the Forbes Global 2000.

😂
Foxconn getting into chip making


Foxconn and Vedanta to build $19bn India chip factory​

    • Published
      14 September
Share
In this photo illustration the Vedanta Limited logo seen displayed on a smartphone.
IMAGE SOURCE, GETTY IMAGES
Image caption,
Vedanta and Foxconn announce $19.5bn chip factory in India
Foxconn and Vedanta have announced $19.5bn (£16.9bn) to build one of the first chipmaking factories in India.
The Taiwanese firm and the Indian mining giant are tying up as the government pushes to boost chip manufacturing in the country.
Prime Minister Narendra Modi's government announced a $10bn package last year to attract investors.
The facility, which will be built in Mr Modi's home state of Gujarat, has been promised incentives.
Vedanta's chairman Anil Agarwal said they were still on the lookout for a site - about 400 acres of land - close to Gujarat's capital, Ahmedabad.
ADVERTISEMENT

But both Indian and foreign firms have struggled in the past to acquire large tracts of land for projects. And experts say that despite Mr Modi's signature 'Make in India' policy - designed to attract global manufacturers - challenges remain when it comes to navigating the country's red tape.

Gujarat Chief Minister Bhupendrabhai Patel, however, said the project "will be met with red carpet... instead of any red tapism".
The project is expected to create 100,000 jobs in the state, which is headed for elections in December, where the BJP is facing stiff competition from oppositions parties.
ADVERTISEMENT

ADVERTISEMENT

According to the Memorandum of Understanding, the facility is expected to start manufacturing chips within two years.
"India's own Silicon Valley is a step closer now," Mr Agarwal said in a tweet.
India has vowed to spend $30bn to overhaul its tech industry. The government said it will also expand incentives beyond the initial $10 billion for chipmakers in order to become less reliant on chip producers in places like Taiwan, the US and China.
"Gujarat has been recognized for its industrial development, green energy, and smart cities. The improving infrastructure and the government's active and strong support increases confidence in setting up a semiconductor factory," according to Brian Ho, a vice president of Foxconn Semiconductor Group.

Foxconn is the technical partner. Vedanta is financing the project as it looks to diversify its investments into the tech sector.
Vedanta is the third company to announce plans to build a chip plant in India. A partnership between ISMC and Singapore-based IGSS Ventures also said it had signed deals to build semiconductor plants in the country over the next five years.
 
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AARONASX

Holding onto what I've got
A lot of dot joining has been shared by wonderful people here.

There is too many to share and go over (otherwise I would be here all night) but anyone wants light reading.
Go to: https://ppubs.uspto.gov/pubwebapp/
Search: "Brainchip"
NOTE: While I understand not all would lead to Brainchip /Akida or anything directly to do, however "brainchip" gets a mention, therefor IMO the author(s) are aware of Brainchip.

-8 of them in the last year alone.
-7ish in the last and most recent 4C time frame.
-3 (Blumind Inc, Giant AI Inc & Digimarc Corporation)
- many many more.
-10 specific by Brainchip Inc


And I don't think this site lists all of them globally, extrapolate that information and we have a lot more out there aware.

1671691236784.png
 

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Pappagolla

Regular
This is a positive view of where Brainchip is going in automotive. Mercedes Benz sells around an average of 3 million passenger vehicles and Jerome Nadel places 70 AKIDA chips pre processing sensor inputs before passing on as meta data. If Blind Freddie's mental arithmetic is correct that is 270 million AKIDA smart sensors.


"BrainChip Akida


Mercedes-Benz's EQXX concept car, which debuted at CES earlier this year, uses BrainChip's Akida neuromorphic processor for in-vehicle keyword recognition. Billed as "the most efficient car Mercedes has ever made," the car utilizes neuromorphic technology that consumes less power than a deep learning-based keyword spotting system. That's crucial for a car with a range of 620 miles, or 167 miles more than Mercedes' flagship electric car, the EQS.

Mercedes said at the time that BrainChip's solution was five to 10 times more efficient than traditional voice controls at recognizing the wake word "Hey Mercedes."

Application of SNN in Vehicle Field


Mercedes said, “Although neuromorphic computing is still in its infancy, such systems will soon be on the market within a few years. When applied at scale throughout vehicles, they have the potential to radically reduce the amount of effort required to run the latest AI technologies. power consumption."


BrainChip's CMO Jerome Nadel said: "Mercedes is focused on big issues like battery management and transmission, but every milliwatt counts, and when you think about energy efficiency, even the most basic reasoning, like finding keywords, matters. important."

A typical car could have as many as 70 different sensors by 2022, Nadel said. For cockpit applications, these sensors can enable face detection, gaze assessment, emotion classification, and more.

He said: “From a system architecture perspective, we can do a 1:1 approach where there is a sensor that will do some preprocessing and then the data will be forwarded. The AI will do inference near the sensor...it will Instead of the full array of data from sensors, the inference metadata is passed forward.”

The idea is to minimize the size and complexity of packets sent to AI accelerators, while reducing latency and minimizing power consumption. Each vehicle will likely have 70 Akida chips or sensors with Akida technology, each of which will be "low-cost parts that won't notice them at all," Nadel said. He noted that attention needs to be paid to the BOM of all these sensors.


Application of SNN in Vehicle Field


BrainChip expects to have its neuromorphic processor next to every sensor on the vehicle

Going forward, Nadel said, neuromorphic processing will also be used in ADAS and autonomous driving systems. This has the potential to reduce the need for other types of power-hungry AI accelerators.

"If every sensor could have Akida configured on one or two nodes, it would do adequate inference, and the data passed would be an order of magnitude less, because that would be inference metadata...that would affect the servers you need," he said. power."


BrainChip's Akida chip accelerates SNNs (spike neural networks) and CNNs (by converting to SNNs). It's not tailored for any specific use case or sensor, so it can be paired with visual sensing for face recognition or people detection, or other audio applications like speaker ID. BrainChip also demonstrated Akida's smell and taste sensors, although it's hard to imagine how these could be used in cars (perhaps to detect air pollution or fuel quality through smell and taste).

Akida is set up to handle SNNs or deep learning CNNs that have been transformed into SNNs. Unlike the native spike network, the transformed CNN preserves some spike-level information, so it may require 2 or 4 bits of computation. However, this approach allows exploiting the properties of CNNs, including their ability to extract features from large datasets. Both types of networks can be updated at the edge using STDP. In the case of Mercedes-Benz, this might mean retraining the network after deployment to discover more or different keywords.

Application of SNN in Vehicle Field


According to Autocar, Mercedes-Benz confirmed that "many innovations" from the EQXX concept car, including "specific components and technologies," will be used in the production model. There's no word yet on whether new Mercedes-Benz models will feature artificial brains."

I do hope you read the whole post and not just the orange text😂🤣😂🤣 - (🐫x1000)

My opinion only DYOR
FF

AKIDA BALLISTA

Blind Freddie is incorrect but 210 million is still pretty huge so the point stands.
 
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This is a positive view of where Brainchip is going in automotive. Mercedes Benz sells around an average of 3 million passenger vehicles and Jerome Nadel places 70 AKIDA chips pre processing sensor inputs before passing on as meta data. If Blind Freddie's mental arithmetic is correct that is 270 million AKIDA smart sensors.


"BrainChip Akida


Mercedes-Benz's EQXX concept car, which debuted at CES earlier this year, uses BrainChip's Akida neuromorphic processor for in-vehicle keyword recognition. Billed as "the most efficient car Mercedes has ever made," the car utilizes neuromorphic technology that consumes less power than a deep learning-based keyword spotting system. That's crucial for a car with a range of 620 miles, or 167 miles more than Mercedes' flagship electric car, the EQS.

Mercedes said at the time that BrainChip's solution was five to 10 times more efficient than traditional voice controls at recognizing the wake word "Hey Mercedes."

Application of SNN in Vehicle Field


Mercedes said, “Although neuromorphic computing is still in its infancy, such systems will soon be on the market within a few years. When applied at scale throughout vehicles, they have the potential to radically reduce the amount of effort required to run the latest AI technologies. power consumption."


BrainChip's CMO Jerome Nadel said: "Mercedes is focused on big issues like battery management and transmission, but every milliwatt counts, and when you think about energy efficiency, even the most basic reasoning, like finding keywords, matters. important."

A typical car could have as many as 70 different sensors by 2022, Nadel said. For cockpit applications, these sensors can enable face detection, gaze assessment, emotion classification, and more.

He said: “From a system architecture perspective, we can do a 1:1 approach where there is a sensor that will do some preprocessing and then the data will be forwarded. The AI will do inference near the sensor...it will Instead of the full array of data from sensors, the inference metadata is passed forward.”

The idea is to minimize the size and complexity of packets sent to AI accelerators, while reducing latency and minimizing power consumption. Each vehicle will likely have 70 Akida chips or sensors with Akida technology, each of which will be "low-cost parts that won't notice them at all," Nadel said. He noted that attention needs to be paid to the BOM of all these sensors.


Application of SNN in Vehicle Field


BrainChip expects to have its neuromorphic processor next to every sensor on the vehicle

Going forward, Nadel said, neuromorphic processing will also be used in ADAS and autonomous driving systems. This has the potential to reduce the need for other types of power-hungry AI accelerators.

"If every sensor could have Akida configured on one or two nodes, it would do adequate inference, and the data passed would be an order of magnitude less, because that would be inference metadata...that would affect the servers you need," he said. power."


BrainChip's Akida chip accelerates SNNs (spike neural networks) and CNNs (by converting to SNNs). It's not tailored for any specific use case or sensor, so it can be paired with visual sensing for face recognition or people detection, or other audio applications like speaker ID. BrainChip also demonstrated Akida's smell and taste sensors, although it's hard to imagine how these could be used in cars (perhaps to detect air pollution or fuel quality through smell and taste).

Akida is set up to handle SNNs or deep learning CNNs that have been transformed into SNNs. Unlike the native spike network, the transformed CNN preserves some spike-level information, so it may require 2 or 4 bits of computation. However, this approach allows exploiting the properties of CNNs, including their ability to extract features from large datasets. Both types of networks can be updated at the edge using STDP. In the case of Mercedes-Benz, this might mean retraining the network after deployment to discover more or different keywords.

Application of SNN in Vehicle Field


According to Autocar, Mercedes-Benz confirmed that "many innovations" from the EQXX concept car, including "specific components and technologies," will be used in the production model. There's no word yet on whether new Mercedes-Benz models will feature artificial brains."

I do hope you read the whole post and not just the orange text😂🤣😂🤣 - (🐫x1000)

My opinion only DYOR
FF

AKIDA BALLISTA
What is interesting about this claim of 70 AKIDA made smart sensors in my opinion is the two facts that have recently emerged:

1. RENESAS is taping out its AKIDA MCU for automotive applications - ideally these budget versions of AKIDA will meet the BOM (Bill of Material) cost constraints to which Jerome Nadel refers in the article.

AND

2. SOCIONEXT is producing AKIDA for ADAS at 5 & 7 nm which will ideally replace the need for power hungry accelerators again as referenced by Jerome Nadel.

My opinion only DYOR
FF

AKIDA BALLISTA
 
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Blind Freddie is incorrect but 210 million is still pretty huge so the point stands.
Very brave to say Blind Freddie is wrong about anything it was the dumb typist. 😂🤣😂🤡😂🤣
Hopefully my admission will satisfy his thirst for revenge.
Post has been corrected.🤓
 
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miaeffect

Oat latte lover
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Hi Quatrojos,

Unfortunately this is not BrainChip. It is a software system developed by the authors for redistributing internet loads to avoid bottlenecks.

Page 10:
"Our study proposes AKIDA, a new architecture that strategically harvests the untapped compute capacity of the SmartNICs to offload transient workload spikes, thereby reducing the SLA violations. Usage of this untapped compute capacity is more favorable than adding and deploying additional servers, as SmartNICs are economically and operationally more desirable. AKIDA is a low-cost and scalable platform that orchestrates seamless offloading of serverless workloads to the SmartNICs at the network edge, eliminating the need for pre-allocating expensive compute power and over-utilization of host servers."

SmartNICs = Smart Network Interface Cards
I notice there is a patent application noted early on when an intern at HPE, I am guessing. 17/222160

SC
 
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