BRN Discussion Ongoing

[Fullmoonfever]

I often go back to the Renesas tape out Ann to reread its context.

Key question for me is the following couple of paragraphs plus the common theme we are now seeing with 22nm both through Renesas in Dec and by BRN with GF...

They way I read the below is that Renesas have had someone come to them and request the tape out and device but open to interpretation I guess.

The other part is that Renesas will let the mkt decide on something like this as to the uptake and then decide if they will bring in house for control or just continue external 3rd parties to run with it.

Renesas tapes out spiking neural network chip

Renesas is taping out a chip using Brainchip's spiking neural network (SNN) technology and expanding internal and foundry capacity

www.eenewseurope.com

“Now you have accelerators for driving AI with neural processing units rather than a dual core CPU. We are working with a third party taping out a device in December on 22nm CMOS,” said Chittipeddi.

The take up of the technology depends on the market adoption, he says.

“We want to see where the market reception is the highest, that is what determines whether we bring things in house or through a third party.”

Now...another recent article I was just reading is about GF and quite interesting group of names discussed including BRN.

Be nice to get in with Purdue as well. Intel in there and our own K Carlson published papers on STDP in 2011 via Purdue.

https://www.allaboutcircuits.com/news/in-series-agreements-globalfoundries-buoys-chip-supply-from-home/

In a Series of Agreements, GlobalFoundries Buoys Chip Supply From Home​

3 days ago by Biljana Ognenova

Tracking the goals of the CHIPS+ Act, GlobalFoundries is strengthening the U.S. semiconductor ecosystem with several partnerships and acquisitions.​

The U.S. semiconductor manufacturing industry is beginning to reap the benefits of the CHIPS+ Act passed in July 2022. In a series of collaborations and acquisitions, GlobalFoundries (GF) is progressing toward one of the act's goals: to strengthen a U.S.-based semiconductor supply chain and reduce dependence on Asia-based manufacturers. Throughout the chip shortage, GF claims it has remained profitable by steadying productivity, containing cost, differentiating products, and doubling down on supply chain security.

A comparison of GF's and TSMC's EBIT margins. Image courtesy of Seeking Alpha​

GlobalFoundries attributes its upward profitability to its strategic approach to chipmaking and its local partnership with key industry sectors, including automotive, memory, and computing as well as education.

​

GlobalFoundries Partners With GM, Renesas, and Purdue​

In a major win for GF in the automotive sector, the foundry signed an agreement with GM on Feb. 9, 2023, to dedicate a manufacturing corridor at GF's upstate New York facility for GM’s key chip suppliers.

GF also secured more production capacity by acquiring Renesas' NVM resistive RAM technology. Specifically, GF now owns Renesas' proprietary Conductive Bridging Random Access Memory (CBRAM) technology, a low-power memory solution built for home and industrial IoT and mobile devices.

GF has secured wins in the education and R&D sector as well, partnering with Purdue University on a semiconductor education program. Purdue was among the first institutions to use the funds from the CHIPS+ Act to finalize a university-level semiconductor program. Now, with this new collaboration, Purdue's staff and students will use GlobalFoundries' facility and resources to create innovative, interdisciplinary solutions for advanced semiconductors and microelectronics. The partnership between GF and Purdue will provide next-generation professionals with hands-on expertise overseen at GF Lab.

GF Supports Next-gen Vision and Computing Technologies​

GF is also championing the computing sector, namely with BrainChip's Akida neuromorphic chip built on 22 nm fully depleted silicon-on-insulator (FD-SOI) technology.

Akida architecture. Image courtesy of BrainChip​

BrainChip is the world’s first company to develop ultra-low-power, event-based, neuromorphic AI IP to be used for always-on sensor applications. The AKD1500 chip was built on GF’s low-leakage FD SOI platform, promising an array of applications that don't overload the CPU.

Vision sensor specialist Oculi also recently announced a strategic partnership with GF, commissioning the foundry to manufacture its single-chip, intelligent software-defined vision sensor. The new sensor will be based on GF’s 55LPx, a platform that supports RF, ultra-low power, embedded NVM, and high-voltage BCDLite (a process technology).

Expanding U.S.-based Semiconductor Production​

While GlobalFoundries relies on five-year agreements to ship chips from storage facilities in Dresden and Singapore, the foundry also plans to expand three U.S. locations, including one in Vermont and two in New York. Specifically, GF is broadening the scope of existing facilities to make 12 nm, 28 nm, and 40 nm chips rather than going back to the drawing board and investing in new technologies to compete with TSMC’s advanced 3 nm – 5 nm chips.

GlobalFoundries' headquarters in Malta, New York. Image (modified) courtesy of GlobalFoundries​

GF has increased existing domestic manufacturing capacity in another way, too. The company has invested in gallium nitride (GaN) RF chips, a wide-band semiconductor technology that outperforms silicon in terms of thermal resistance and durability. GF's development of GaN devices at its Essex Junction, Vermont, facility would have been impossible without a $30 million government grant to shorten the time to market for the GaN RF technology.

A GF facility in upstate New York is also getting a boost—this time, from collaborating with Qualcomm. The U.S. mobile chipmaker has previously agreed to spend $4.2 billion on chips made by GF for Qualcomm's 5G transceivers, automotive products, and IoT connectivity.

I also posted not long ago about NASA and the 22nm FDSOI requirements plus that there wrote the same chip was often used for automotive.

Well, just seen this older solicitation with some further support for the 22nm. Ends mid this year.

Radiation Tolerant Standard Cell Library in a 22nm FDSOI CMOS Process | SBIR.gov

www.sbir.gov

Radiation Tolerant Standard Cell Library in a 22nm FDSOI CMOS Process​

Award Information
Agency:National Aeronautics and Space Administration
Branch:N/A
Contract:80NSSC21C0516
Agency Tracking Number:205369
Amount:$759,984.00
Phasehase II
Program:SBIR
Solicitation Topic Code:Z2
Solicitation Number:SBIR_20_P2
Timeline
Solicitation Year:2020
Award Year:2021
Award Start Date (Proposal Award Date):2021-07-27
Award End Date (Contract End Date):2023-07-26
Small Business Information
ALPHACORE INC
304 South Rockford Drive
Tempe, AZ 85281-0000
United States

NASA is seeking radiation tolerant standard cell libraries for processes below 28nm that are suitable for NASA missions in the natural space environment. As a response, Alphacore proposes to develop for NASA an innovative library of radiation-hardened (rad-hard) standard cells implemented in the GlobalFoundries (GF) 22nm fully depleted silicon on insulator (FDSOI) CMOS fabrication process (GF 22FDX).nbsp;Alphacore has been able to have the first version of the radiation tolerant library important cells already designed by the end of the Phase I program. The Phase I program provided an excellent basis for the Phase II program in which an extended, innovative radiation-tolerant standard cell library will be designed, fabricated, and tested. The library elements will be taped in the middle of the Phase II program and knowing the fabrication time for chips in the 22FDX program is 2.5 - 3 months, the evaluations can start approximately 3.5 months after the tapeout date. The elements will be tested for functionality, circuit performance and radiation hardness, including both single event effects (SEE) and total ionizing dose (TID).During Phase II, Alphacore will not only support CMOS IP development for the most harsh NASA mission environments (low temperatures and high radiation), but also covering critical immediate needs in other areas such as the High-Performance Spaceflight Computing (HPSC) Chiplet, a radiation-hardened multi-core processor. NASA also will be developing single board computer based on this chiplet. These components are supposed to bring NASA (and DoD) a two orders of magnitude improvement in their space computing capabilities.

 

[Tothemoon24]

I often go back to the Renesas tape out Ann to reread its context.

Key question for me is the following couple of paragraphs plus the common theme we are now seeing with 22nm both through Renesas in Dec and by BRN with GF...

They way I read the below is that Renesas have had someone come to them and request the tape out and device but open to interpretation I guess.

The other part is that Renesas will let the mkt decide on something like this as to the uptake and then decide if they will bring in house for control or just continue external 3rd parties to run with it.

Renesas tapes out spiking neural network chip

Renesas is taping out a chip using Brainchip's spiking neural network (SNN) technology and expanding internal and foundry capacity

www.eenewseurope.com

“Now you have accelerators for driving AI with neural processing units rather than a dual core CPU. We are working with a third party taping out a device in December on 22nm CMOS,” said Chittipeddi.

The take up of the technology depends on the market adoption, he says.

“We want to see where the market reception is the highest, that is what determines whether we bring things in house or through a third party.”

Now...another recent article I was just reading is about GF and quite interesting group of names discussed including BRN.

Be nice to get in with Purdue as well. Intel in there and our own K Carlson published papers on STDP in 2011 via Purdue.

https://www.allaboutcircuits.com/news/in-series-agreements-globalfoundries-buoys-chip-supply-from-home/

In a Series of Agreements, GlobalFoundries Buoys Chip Supply From Home​

3 days ago by Biljana Ognenova

Tracking the goals of the CHIPS+ Act, GlobalFoundries is strengthening the U.S. semiconductor ecosystem with several partnerships and acquisitions.​

The U.S. semiconductor manufacturing industry is beginning to reap the benefits of the CHIPS+ Act passed in July 2022. In a series of collaborations and acquisitions, GlobalFoundries (GF) is progressing toward one of the act's goals: to strengthen a U.S.-based semiconductor supply chain and reduce dependence on Asia-based manufacturers. Throughout the chip shortage, GF claims it has remained profitable by steadying productivity, containing cost, differentiating products, and doubling down on supply chain security.

A comparison of GF's and TSMC's EBIT margins. Image courtesy of Seeking Alpha​

GlobalFoundries attributes its upward profitability to its strategic approach to chipmaking and its local partnership with key industry sectors, including automotive, memory, and computing as well as education.

​

GlobalFoundries Partners With GM, Renesas, and Purdue​

In a major win for GF in the automotive sector, the foundry signed an agreement with GM on Feb. 9, 2023, to dedicate a manufacturing corridor at GF's upstate New York facility for GM’s key chip suppliers.

GF also secured more production capacity by acquiring Renesas' NVM resistive RAM technology. Specifically, GF now owns Renesas' proprietary Conductive Bridging Random Access Memory (CBRAM) technology, a low-power memory solution built for home and industrial IoT and mobile devices.

GF has secured wins in the education and R&D sector as well, partnering with Purdue University on a semiconductor education program. Purdue was among the first institutions to use the funds from the CHIPS+ Act to finalize a university-level semiconductor program. Now, with this new collaboration, Purdue's staff and students will use GlobalFoundries' facility and resources to create innovative, interdisciplinary solutions for advanced semiconductors and microelectronics. The partnership between GF and Purdue will provide next-generation professionals with hands-on expertise overseen at GF Lab.

GF Supports Next-gen Vision and Computing Technologies​

GF is also championing the computing sector, namely with BrainChip's Akida neuromorphic chip built on 22 nm fully depleted silicon-on-insulator (FD-SOI) technology.

Akida architecture. Image courtesy of BrainChip​

BrainChip is the world’s first company to develop ultra-low-power, event-based, neuromorphic AI IP to be used for always-on sensor applications. The AKD1500 chip was built on GF’s low-leakage FD SOI platform, promising an array of applications that don't overload the CPU.

Vision sensor specialist Oculi also recently announced a strategic partnership with GF, commissioning the foundry to manufacture its single-chip, intelligent software-defined vision sensor. The new sensor will be based on GF’s 55LPx, a platform that supports RF, ultra-low power, embedded NVM, and high-voltage BCDLite (a process technology).

Expanding U.S.-based Semiconductor Production​

While GlobalFoundries relies on five-year agreements to ship chips from storage facilities in Dresden and Singapore, the foundry also plans to expand three U.S. locations, including one in Vermont and two in New York. Specifically, GF is broadening the scope of existing facilities to make 12 nm, 28 nm, and 40 nm chips rather than going back to the drawing board and investing in new technologies to compete with TSMC’s advanced 3 nm – 5 nm chips.

GlobalFoundries' headquarters in Malta, New York. Image (modified) courtesy of GlobalFoundries​

GF has increased existing domestic manufacturing capacity in another way, too. The company has invested in gallium nitride (GaN) RF chips, a wide-band semiconductor technology that outperforms silicon in terms of thermal resistance and durability. GF's development of GaN devices at its Essex Junction, Vermont, facility would have been impossible without a $30 million government grant to shorten the time to market for the GaN RF technology.

A GF facility in upstate New York is also getting a boost—this time, from collaborating with Qualcomm. The U.S. mobile chipmaker has previously agreed to spend $4.2 billion on chips made by GF for Qualcomm's 5G transceivers, automotive products, and IoT connectivity.

I also posted not long ago about NASA and the 22nm FDSOI requirements plus that there wrote the same chip was often used for automotive.

Well, just seen this older solicitation with some further support for the 22nm. Ends mid this year.

Radiation Tolerant Standard Cell Library in a 22nm FDSOI CMOS Process | SBIR.gov

www.sbir.gov

Radiation Tolerant Standard Cell Library in a 22nm FDSOI CMOS Process​

Award Information
Agency:National Aeronautics and Space Administration
Branch:N/A
Contract:80NSSC21C0516
Agency Tracking Number:205369
Amount:$759,984.00
Phasehase II
Program:SBIR
Solicitation Topic Code:Z2
Solicitation Number:SBIR_20_P2
Timeline
Solicitation Year:2020
Award Year:2021
Award Start Date (Proposal Award Date):2021-07-27
Award End Date (Contract End Date):2023-07-26
Small Business Information
ALPHACORE INC
304 South Rockford Drive
Tempe, AZ 85281-0000
United States

NASA is seeking radiation tolerant standard cell libraries for processes below 28nm that are suitable for NASA missions in the natural space environment. As a response, Alphacore proposes to develop for NASA an innovative library of radiation-hardened (rad-hard) standard cells implemented in the GlobalFoundries (GF) 22nm fully depleted silicon on insulator (FDSOI) CMOS fabrication process (GF 22FDX).nbsp;Alphacore has been able to have the first version of the radiation tolerant library important cells already designed by the end of the Phase I program. The Phase I program provided an excellent basis for the Phase II program in which an extended, innovative radiation-tolerant standard cell library will be designed, fabricated, and tested. The library elements will be taped in the middle of the Phase II program and knowing the fabrication time for chips in the 22FDX program is 2.5 - 3 months, the evaluations can start approximately 3.5 months after the tapeout date. The elements will be tested for functionality, circuit performance and radiation hardness, including both single event effects (SEE) and total ionizing dose (TID).During Phase II, Alphacore will not only support CMOS IP development for the most harsh NASA mission environments (low temperatures and high radiation), but also covering critical immediate needs in other areas such as the High-Performance Spaceflight Computing (HPSC) Chiplet, a radiation-hardened multi-core processor. NASA also will be developing single board computer based on this chiplet. These components are supposed to bring NASA (and DoD) a two orders of magnitude improvement in their space computing capabilities.

Absolute Brilliance

 

[Quatrojos]

I often go back to the Renesas tape out Ann to reread its context.

Key question for me is the following couple of paragraphs plus the common theme we are now seeing with 22nm both through Renesas in Dec and by BRN with GF...

They way I read the below is that Renesas have had someone come to them and request the tape out and device but open to interpretation I guess.

The other part is that Renesas will let the mkt decide on something like this as to the uptake and then decide if they will bring in house for control or just continue external 3rd parties to run with it.

Renesas tapes out spiking neural network chip

Renesas is taping out a chip using Brainchip's spiking neural network (SNN) technology and expanding internal and foundry capacity

www.eenewseurope.com

“Now you have accelerators for driving AI with neural processing units rather than a dual core CPU. We are working with a third party taping out a device in December on 22nm CMOS,” said Chittipeddi.

The take up of the technology depends on the market adoption, he says.

“We want to see where the market reception is the highest, that is what determines whether we bring things in house or through a third party.”

Now...another recent article I was just reading is about GF and quite interesting group of names discussed including BRN.

Be nice to get in with Purdue as well. Intel in there and our own K Carlson published papers on STDP in 2011 via Purdue.

https://www.allaboutcircuits.com/news/in-series-agreements-globalfoundries-buoys-chip-supply-from-home/

In a Series of Agreements, GlobalFoundries Buoys Chip Supply From Home​

3 days ago by Biljana Ognenova

Tracking the goals of the CHIPS+ Act, GlobalFoundries is strengthening the U.S. semiconductor ecosystem with several partnerships and acquisitions.​

The U.S. semiconductor manufacturing industry is beginning to reap the benefits of the CHIPS+ Act passed in July 2022. In a series of collaborations and acquisitions, GlobalFoundries (GF) is progressing toward one of the act's goals: to strengthen a U.S.-based semiconductor supply chain and reduce dependence on Asia-based manufacturers. Throughout the chip shortage, GF claims it has remained profitable by steadying productivity, containing cost, differentiating products, and doubling down on supply chain security.

A comparison of GF's and TSMC's EBIT margins. Image courtesy of Seeking Alpha​

GlobalFoundries attributes its upward profitability to its strategic approach to chipmaking and its local partnership with key industry sectors, including automotive, memory, and computing as well as education.

​

GlobalFoundries Partners With GM, Renesas, and Purdue​

In a major win for GF in the automotive sector, the foundry signed an agreement with GM on Feb. 9, 2023, to dedicate a manufacturing corridor at GF's upstate New York facility for GM’s key chip suppliers.

GF also secured more production capacity by acquiring Renesas' NVM resistive RAM technology. Specifically, GF now owns Renesas' proprietary Conductive Bridging Random Access Memory (CBRAM) technology, a low-power memory solution built for home and industrial IoT and mobile devices.

GF has secured wins in the education and R&D sector as well, partnering with Purdue University on a semiconductor education program. Purdue was among the first institutions to use the funds from the CHIPS+ Act to finalize a university-level semiconductor program. Now, with this new collaboration, Purdue's staff and students will use GlobalFoundries' facility and resources to create innovative, interdisciplinary solutions for advanced semiconductors and microelectronics. The partnership between GF and Purdue will provide next-generation professionals with hands-on expertise overseen at GF Lab.

GF Supports Next-gen Vision and Computing Technologies​

GF is also championing the computing sector, namely with BrainChip's Akida neuromorphic chip built on 22 nm fully depleted silicon-on-insulator (FD-SOI) technology.

Akida architecture. Image courtesy of BrainChip​

BrainChip is the world’s first company to develop ultra-low-power, event-based, neuromorphic AI IP to be used for always-on sensor applications. The AKD1500 chip was built on GF’s low-leakage FD SOI platform, promising an array of applications that don't overload the CPU.

Vision sensor specialist Oculi also recently announced a strategic partnership with GF, commissioning the foundry to manufacture its single-chip, intelligent software-defined vision sensor. The new sensor will be based on GF’s 55LPx, a platform that supports RF, ultra-low power, embedded NVM, and high-voltage BCDLite (a process technology).

Expanding U.S.-based Semiconductor Production​

While GlobalFoundries relies on five-year agreements to ship chips from storage facilities in Dresden and Singapore, the foundry also plans to expand three U.S. locations, including one in Vermont and two in New York. Specifically, GF is broadening the scope of existing facilities to make 12 nm, 28 nm, and 40 nm chips rather than going back to the drawing board and investing in new technologies to compete with TSMC’s advanced 3 nm – 5 nm chips.

GlobalFoundries' headquarters in Malta, New York. Image (modified) courtesy of GlobalFoundries​

GF has increased existing domestic manufacturing capacity in another way, too. The company has invested in gallium nitride (GaN) RF chips, a wide-band semiconductor technology that outperforms silicon in terms of thermal resistance and durability. GF's development of GaN devices at its Essex Junction, Vermont, facility would have been impossible without a $30 million government grant to shorten the time to market for the GaN RF technology.

A GF facility in upstate New York is also getting a boost—this time, from collaborating with Qualcomm. The U.S. mobile chipmaker has previously agreed to spend $4.2 billion on chips made by GF for Qualcomm's 5G transceivers, automotive products, and IoT connectivity.

I also posted not long ago about NASA and the 22nm FDSOI requirements plus that there wrote the same chip was often used for automotive.

Well, just seen this older solicitation with some further support for the 22nm. Ends mid this year.

Radiation Tolerant Standard Cell Library in a 22nm FDSOI CMOS Process | SBIR.gov

www.sbir.gov

Radiation Tolerant Standard Cell Library in a 22nm FDSOI CMOS Process​

Award Information
Agency:National Aeronautics and Space Administration
Branch:N/A
Contract:80NSSC21C0516
Agency Tracking Number:205369
Amount:$759,984.00
Phasehase II
Program:SBIR
Solicitation Topic Code:Z2
Solicitation Number:SBIR_20_P2
Timeline
Solicitation Year:2020
Award Year:2021
Award Start Date (Proposal Award Date):2021-07-27
Award End Date (Contract End Date):2023-07-26
Small Business Information
ALPHACORE INC
304 South Rockford Drive
Tempe, AZ 85281-0000
United States

NASA is seeking radiation tolerant standard cell libraries for processes below 28nm that are suitable for NASA missions in the natural space environment. As a response, Alphacore proposes to develop for NASA an innovative library of radiation-hardened (rad-hard) standard cells implemented in the GlobalFoundries (GF) 22nm fully depleted silicon on insulator (FDSOI) CMOS fabrication process (GF 22FDX).nbsp;Alphacore has been able to have the first version of the radiation tolerant library important cells already designed by the end of the Phase I program. The Phase I program provided an excellent basis for the Phase II program in which an extended, innovative radiation-tolerant standard cell library will be designed, fabricated, and tested. The library elements will be taped in the middle of the Phase II program and knowing the fabrication time for chips in the 22FDX program is 2.5 - 3 months, the evaluations can start approximately 3.5 months after the tapeout date. The elements will be tested for functionality, circuit performance and radiation hardness, including both single event effects (SEE) and total ionizing dose (TID).During Phase II, Alphacore will not only support CMOS IP development for the most harsh NASA mission environments (low temperatures and high radiation), but also covering critical immediate needs in other areas such as the High-Performance Spaceflight Computing (HPSC) Chiplet, a radiation-hardened multi-core processor. NASA also will be developing single board computer based on this chiplet. These components are supposed to bring NASA (and DoD) a two orders of magnitude improvement in their space computing capabilities.

 

[DingoBorat]

It's good to hear about a genuine Australian entrepreneur, as opposed to the corporate raiders the media awarded that badge to in the 80s.

The NDAs limit the shareholders view of what the sales and marketing (S&M) are up to.

It is the nature of the business of selling IP licences for ground breaking tech that is a prolonged task. First it is necessary to identify potential customers who have a process which can be improved by Akida and who have a sufficiently large market to justify the cost of developing an IC including the Akida IP. The company has identified the EAPs (from June 2020), and continues to work with them, but, as Peter pointed out, you have to intersect with their product development cycle. For example, Mercedes did not start to develop the EQXX because they found out about Akida. We happened along at the right time to be incorporated in "Hey Mercedes!"

We shareholders hope that Valeo's SCALA 3 LiDaR, due to be commercialized within a year or so, will incorporate Akida. We have been working with Valeo since mid-2020. So it has taken a few years to get to a position of preparing to go to market.

Similarly, Renesas, our first publicly announced licencee (December 2020), and an IC producer, may have a product on the market by the end of 2023.

Similarly, we have been working with Socionext since 2020 on a joint project.

So history shows there is a 3+ year lead time to get to market.

We have over a dozen EAPs with whom we are working, so let's hope we see some results in the next year or so.

That said, I don't think we can expect Sean to pull rabbits out of the hat while they are still in their shells.

"That said, I don't think we can expect Sean to pull rabbits out of the hat while they are still in their shells"

No! You need to sit on the eggs! ..

Maybe we need A.I...
An Artificial Incubator..

=

 

[dippY22]

Different time, different game, different strata of Business.
You will not attract and retain the experienced and suitably connected Managers and Directors we have, if only offering peanuts.
These people have a track record in the IP sales method we have embarked upon and also have relationships with already established, big and strategically important players. We are in the big leagues now and need credible representation with people who know how to talk the talk and walk the walk. With people who went to the right schools, belong to the right associations and who are accepted by our wannabe clients as credible and reliable people to do business with. With limited resources I want our people concentrated on doing the big deals and continuing to massage other like sized and larger organisations. Sure, we'll no doubt eventually filter down into doorbells and toys, that's what ubiquitous means, but that's some other guys job.
We make the sensor they'll wind up using so smart, cheap and energy efficient that it will be the obvious and best solution to whatever the problem they are trying to solve is. We are not the guy out there selling the particular vehicle or even just cars in general. We are selling the best in class, revolutionary vital component for anything out there now (and on the drawing boards for the near future) that wants/needs to see, hear, smell, taste or feel the world around us, in all the glory of the full available spectrum. And do it with a small physical and thermal footprint, off the leash as required and all the while making the job of everything downstream from us easier and reducing the required bandwidth to boot.
Yes, it's taking time. Rome wasn't built in a day.

I agree.

Doorbells are a nice market, sure, but I want hearing aids and other medical devices and applicationss,....robotics and especially robots in the future, drones, and every visual application imaginable. If the sensor is internet dependent I want a piece of that.

 

[miaeffect]

I agree.

Doorbells are a nice market, sure, but I want hearing aids and other medical devices and applicationss,....robotics and especially robots in the future, drones, and every visual application imaginable. If the sensor is internet dependent I want a piece of that.

YES

 

[BaconLover]

View attachment 30591
View attachment 30593
View attachment 30594

Nice one TLS!

Let's hope we can emulate this little Aussie beauty. Back in 2011 they were changing hands for 10c.
Within 10 years they were trading at $40

View attachment 30600

Their SOI is 131.6 million, and and has a current MC of 5B.

At this stage, I'd be happy to see 5B MC for us haha, but yeah, I do agree with your sentiment about that hockey stick growth.
Should come as we improve our financial numbers on the paper.

 

[M_C]

Qualcomm demos fastest local AI image generation with Stable Diffusion on mobile

Qualcomm demos local AI image generation in a trice.

www.theverge.com

 

[Dhm]

Watch the Video! In the LinkedIn link.

Amir start talking about BrainChip toward the last two minutes.

View attachment 30515 View attachment 30516 View attachment 30517

Amir Sherman on LinkedIn: Checkout my Presentation at the Mext Semiconductors Event. The "new"…

Checkout my Presentation at the Mext Semiconductors Event. The "new" Semiconductors market with focus on EdgeML & EdgeAI and how Edge Impulse can help…

www.linkedin.com

Learning 🏖

Aren’t we getting short changed with this Edge Impulse video? We are excellent at object detection and tracking, yet another company, Silabs amongst others is getting recognition for its system. Also Renesas. Or am I missing something here?

 

[Deleted member 118]

Not seen this before

The more progressive an AI is, the more advantages it confers - AICorespot

The focus of discourse within the AI community has for a while now, been around the concept of “Beneficial AI”. Strictly speaking, applications that intend to enhance the human or societal condition are considered as being beneficial.

aicorespot.io

The more progressive an AI is, the more advantages it confers​

The focus of discourse within the AI community has for a while now, been around the concept of “Beneficial AI”.
Strictly speaking, applications that intend to enhance the human or societal condition are considered as being beneficial. This might be applicable to medical practices, like diagnosing illnesses, research, or technologies that enhance patient outcomes. Conservation of the environment, like sensors that identify hazardous emissions, in addition to humanitarian initiatives, like agricultural or transport enhancements to assist an expanding populace also reap advantages from these technologies. Even simplistic, useful applications that make our daily lives simpler and streamlined are beneficial.
Usually when someone wishes to do the best, the wish is to do it as swiftly as possible and as efficiently as viable. So while there are innumerable ways through which AI can reap benefits, and several organizations are currently producing these functionalities, there are some obvious AI technologies that have the competitive edge with regards to providing beneficial AI.
You might recall a game from your youth called “Where’s Waldo.” The basis of the game is to find Waldo in his patented red-white stripes sweater and glasses. This is a HOB (Hidden object game) developed as a massive visual puzzle.
While it can prove to be difficult for the human eye, recognition of patterns and imagery is a typical AI activity – so commonplace, as a matter of fact, that even previous gen neural networks generate considerably precise results and find Waldo.

• What is the time taken?
• What is the level of effort and energy expenditure?
• What are the number of computations executed in the cause?
• What is the level of hardware that is needed?
• What are the expenses?

And what if Waldo changes the color of his sweater? Maybe the red and blue stripes isn’t cutting it for him anymore. The alters the playing field completely. The system has to carry out the search for Waldo one more time.
So while there are several AI technologies that can execute this simplistic activity, in our scenario, finding Waldo, there are major differences in their strategy, and effectiveness.
The BrainChip Akida processor, which leverages event-based Spiking Neural Networks (SNNs) is excellent in various ways, from reduced power consumption to learning in increments, and high-speed inferencing/one-shot training.
The Akida engine can identify Waldo quicker, with reduced exertion, and a lot lower computational expenditure.
Say, rather than tracking Waldo, we’re looking out for an endangered animal at risk from poaching, in a region renowned for such activities – an instance of Beneficial AI; we can observe why quickness and effectiveness is of paramount importance. Detecting members of that particular group of animals through image or the audio of their calls, and precisely categorizing ones with atypical features, like an absent ear enables for improved tracking and quicker intervention.
Going back to Waldo, what if you could keep an ear out for Waldo? This would furnish another method to quickly detect and find his location in each puzzle. BrainChip has executed several tests in vibrational analysis: the capacity to “feel”, document, and process mechanical vibration noise, with the outcome surpassing the capacities of the human ear.
With Akida’s speed, precision, and effectiveness in AI vibrational analysis, it can detect outliers swiftly. This is critical for detecting wear and tear in machines, for safety and preventive maintenance, or to enhance gas efficiency and minimize energy expenditure.
There are several ways in which we observe Beneficial AI taking place at the edge. Geographic, industrial, medical, and biometric, to list a few examples. Akida does not need an external processor, RAM, or Deep Learning Accelerator (DLA) and is extremely conservative in terms of energy, so it is particularly apt in these applications. To safeguard species at-risk, we are required to be deploying unmanned aircraft, or drones, with cameras and CPUs in tow, carrying out on-device data analytics where there is no cloud connectivity, and eating up minimal battery power which enables for a longer life. To evaluate for COVID-19 and manage outbreaks, we require hand-held diagnostic evaluation gadgets that can carry out analysis of breath sensor data in the real world.
Akida AI functionalities are superior, so we can generate beneficial outcomes not just quicker but more precisely – at almost 100% precision in various applications that have been evaluated.
As it leverages SNNs, Akida knows how to distinguish between “good” data and bad data or trash data. Lately, analysts detected susceptibilities in a common image categorization system that leverages previous-gen neural networks. Interestingly, the system was tricked by imagery containing text, for example, a piece of paper with iPod written on it fooled the system into categorizing the image as an iPod device.
Likewise, the background of imagery may consist of features that induce confusion in the neural network, leading it to a mis-categorization. If an object is part obscured, such errors often take place. A red-and-white striped curtain may be wrongly categorized as Waldo.
Obviously, identifying Waldo isn’t a question of life and death, but some AI activities will be. Edge devices outfitted for Beneficial AI must hence provide speed, precision, agility, and efficiency to do the most good.
by AICorespot Editorial Team
0
June 28, 2021
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[Learning]

Aren’t we getting short changed with this Edge Impulse video? We are excellent at object detection and tracking, yet another company, Silabs amongst others is getting recognition for its system. Also Renesas. Or am I missing something here?

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Hi Dhm,

At the end of the day Edge Impulse is also running a business. They have partnership with others start up before Brainchip. Hence, thay are also committed to those other company also.

As with other Edge Impulse presentation, they are a software solution platform, so married many hardware technology with Edge Impulse software is to show Edge Impulse is diverse in its platform.

Good things will eventually come with Brainchip and Edge Impulse (JMHO)

Learning 🏖

 

[chapman89]

Hi Dhm,

At the end of the day Edge Impulse is also running a business. They have partnership with others started up before Brainchip. Hence, thay are also committed to those other company also.

As with other Edge Impulse presentation, they are a software solution platform, so married many hardware technology with Edge Impulse software is to show Edge Impulse is diverse in its platform.

Good things will eventually come with Brainchip and Edge Impulse (JMHO)

Learning 🏖

Also don’t forget, edge impulse have referred to akida as science fiction, and we are the first and only “strategic” IP company.

“BrainChip the first strategic IP partner on the Edge Impulse platform”

Edge Impulse and BrainChip Partner to Further AI Development with Support for the Akida Platform

Edge Impulse has announced official support for BrainChip's neural processor AI IP.

www.edgeimpulse.com

 

[buena suerte :-)]

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EXCITING !!!! Very nice find TLS GO BRN

 

[Tothemoon24]

TSC Tata Consultancy’s Services
Have performed a ECG - SNN coupled with neuromorphic platforms

Benchmark : Intel Loihi / Brainchip Akida

There’s been an extensive white paper produced.



In situ real-time monitoring of ECG signal at wearable and implantable devices such as smart watch, ILR, Pacemaker etc. are crucial for early clinical intervention of Cardio-Vascular diseases. Existing deep learning based techniques are not suitable to run on such low-power, low-memory, battery driven devices. In this paper, we have designed and implemented a Reservoir based SNN & a Feed-forward SNN, and compared their performances for ECG pattern classification along with a new Peak-based spike encoder and two other spike encoders. Feed-forward SNN coupled with Peak-based encoder is observed to deliver the best performance spending least computational effort and thus minimal power consumption. Therefore, this SNN based system running on Neuromorphic Computing (NC) platforms can be a suitable solution for ECG pattern classification at the wearable edge.

…

https://neurips.cc/media/PosterPDFs/NeurIPS%202022/60044.png?t=1668067062.275098

 

[chapman89]

Also don’t forget, edge impulse have referred to akida as science fiction, and we are the first and only “strategic” IP company.

“BrainChip the first strategic IP partner on the Edge Impulse platform”

Edge Impulse and BrainChip Partner to Further AI Development with Support for the Akida Platform

Edge Impulse has announced official support for BrainChip's neural processor AI IP.

www.edgeimpulse.com

Just a refresher for those who haven’t/didn’t watch these-

“New IP innovation erasing limits”

From 58:50 timeframe-

Edge Impulse Imagine 2022 Day 1

The edge ML conference 00:18:10 | CEO Keynote - Zach Shelby, Edge Impulse01:05:03 | Industry Welcome - Simon Segars01:13:23 | Application of Edge ML in the S...

www.youtube.com

And then Rob telson presenting that day-

 

[chapman89]

Just a refresher for those who haven’t/didn’t watch these-

“New IP innovation erasing limits”

From 58:50 timeframe-

Edge Impulse Imagine 2022 Day 1

The edge ML conference 00:18:10 | CEO Keynote - Zach Shelby, Edge Impulse01:05:03 | Industry Welcome - Simon Segars01:13:23 | Application of Edge ML in the S...

www.youtube.com

And then Rob telson presenting that day-

And then the video where he refers to akida as science fiction at the 25:30 mark-

 

[cosors]

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To read something like this here in German almost brings tears to my eyes. Just - WoW

 

[Townyj]

To read something like this here in German almost brings tears to my eyes. Just - WoW

Oh come on.. Bodzin.. techno king! Sheesh!

 

[Tothemoon24]

• English

Loading

MENU Neuromorphic camera and machine learning aid nanoscopic imaging​ In a new study, researchers at the Indian Institute of Science (IISc) show how a brain-inspired image sensor can go beyond the diffraction limit of light to detect miniscule objects such as cellular components or nanoparticles invisible to current microscopes. Their novel technique, which combines optical microscopy with a neuromorphic camera and machine learning algorithms, presents a major step forward in pinpointing objects smaller than 50 nanometers in size. The results are published in Nature Nanotechnology. Since the invention of optical microscopes, scientists have strived to surpass a barrier called the diffraction limit, which means that the microscope cannot distinguish between two objects if they are smaller than a certain size (typically 200-300 nanometers). Their efforts have largely focused on either modifying the molecules being imaged, or developing better illumination strategies – some of which led to the 2014 Nobel Prize in Chemistry. “But very few have actually tried to use the detector itself to try and surpass this detection limit,” says Deepak Nair, Associate Professor at the Centre for Neuroscience (CNS), IISc, and corresponding author of the study. Transformation of cumulative probability density of ON and OFF processes allows localisation below the limit of classical single particle detection (Credit: Mangalwedhekar et al, 2023) Measuring roughly 40 mm (height) by 60 mm (width) by 25 mm (diameter), and weighing about 100 grams, the neuromorphic camera used in the study mimics the way the human retina converts light into electrical impulses, and has several advantages over conventional cameras. In a typical camera, each pixel captures the intensity of light falling on it for the entire exposure time that the camera focuses on the object, and all these pixels are pooled together to reconstruct an image of the object. In neuromorphic cameras, each pixel operates independently and asynchronously, generating events or spikes only when there is a change in the intensity of light falling on that pixel. This generates sparse and lower amount of data compared to traditional cameras, which capture every pixel value at a fixed rate, regardless of whether there is any change in the scene. This functioning of a neuromorphic camera is similar to how the human retina works, and allows the camera to “sample” the environment with much higher temporal resolution – because it is not limited by a frame rate like normal cameras – and also perform background suppression. “Such neuromorphic cameras have a very high dynamic range (>120 dB), which means that you can go from a very low-light environment to very high-light conditions. The combination of the asynchronous nature, high dynamic range, sparse data, and high temporal resolution of neuromorphic cameras make them well-suited for use in neuromorphic microscopy,” explains Chetan Singh Thakur, Assistant Professor at the Department of Electronic Systems Engineering (DESE), IISc, and co-author. View of the microscopy setup (Credit: Rohit Mangalwedhekar) In the current study, the group used their neuromorphic camera to pinpoint individual fluorescent beads smaller than the limit of diffraction, by shining laser pulses at both high and low intensities, and measuring the variation in the fluorescence levels. As the intensity increases, the camera captures the signal as an “ON” event, while an “OFF” event is reported when the light intensity decreases. The data from these events were pooled together to reconstruct frames. To accurately locate the fluorescent particles within the frames, the team used two methods. The first was a deep learning algorithm, trained on about one and a half million image simulations that closely represented the experimental data, to predict where the centroid of the object could be, explains Rohit Mangalwedhekar, former research intern at CNS and first author of the study. A wavelet segmentation algorithm was also used to determine the centroids of the particles separately for the ON and the OFF events. Combining the predictions from both allowed the team to zero in on the object’s precise location with greater accuracy than existing techniques. “In biological processes like self-organisation, you have molecules that are alternating between random or directed movement, or that are immobilised,” explains Nair. “Therefore, you need to have the ability to locate the centre of this molecule with the highest precision possible so that we can understand the thumb rules that allow the self-organisation.” The team was able to closely track the movement of a fluorescent bead moving freely in an aqueous solution using this technique. This approach can, therefore, have widespread applications in precisely tracking and understanding stochastic processes in biology, chemistry and physics. REFERENCE: Mangalwedhekar R, Singh N, Thakur CS, Seelamantula CS, Jose M, Nair D, Achieving nanoscale precision using neuromorphic localization microscopy, Nature Nanotechnology (2023).

 

[Tothemoon24]

Is Tesla s l o w l e y getting the idea

Tesla patents virtualization and machine learning software to improve FSD — TESLARATI

Tesla has applied for a set of patents that are set to significantly improve virtualization, recognition, and Full Self Driving overall.

apple.news