BRN Discussion Ongoing

[SharesForBrekky]

As others have remarked earlier, Wavious seems to be out of business and their website is no longer accessible.

Apparently, their former CEO Benny Malek recently joined the Board of Endura Technologies:
View attachment 59908

Intriguingly, quite a number of previous Wavious employees (all with a Qualcomm background before joining Wavious, by the way, just like former CEO Benny Malek and former CTO Hanan Cohen) have since ended up at Apple - more than a coincidence? A quick Google search didn’t come up with any evidence of a takeover/acquisition of Wavious by the Cupertino-headquartered tech giant, though.


View attachment 59909

View attachment 59915
View attachment 59916



View attachment 59912
View attachment 59917

Good work Frangipani.

Here is an interesting website, which has some interesting connections:

Advancing semiconductor tech, transistor to connectivity

Semiconductor technologies are building blocks of modern tech. We enable advancements from transistor to connectivity via unique portfolio companies & platforms

www.bridgewestgroup.com

Bridgewest Group are very much involved with Endura Technologies as well as a couple of very interesting mentions with Wavious.

"Wavious offers a mix and match, plug and play chiplet platform that spurs innovation, accelerates time to market and reduces cost."

"2022
Semiconductor
Chiplet solutions company Wavious is acquired by a Fortune 5 company."

Couldn't find anything about this Fortune 5 company though.

 

[Diogenese]

I usually post interesting (but also ridiculous) stuff from TSE in the German forum. This time however, the user “perhaps” who also is sometimes here active, postet today something interesting, I will share.

“I've taken a closer look at MYWAI because I see a lot of potential there. The MYWAI projects use a novel neuromorphic memristor technology (computing processes executed in memory) from General Vision.

If everything works as they claim, they have made a breakthrough in one of the biggest problems in neuromorphic computing. When the task changes, an automatic adjustment of the required neurons is necessary; otherwise, there will be increased inaccuracies. So far, there has been no solution to this problem. Akida is also affected by this issue. With General Vision's approach, it is theoretically possible to control the neurons of the Akida processor via the memristor, unlocking the full potential of Akida. Therefore, the progress of the MYWAI projects deserves increased attention, as this could be where the big breakthrough happens.

Sources: https://general-vision.com/download/neuromem-technology-reference-guide/?wpdmdl=12284&refresh=660275755e5f11711437173 https://neurotechnologijos.com/zusammenarbeit/?lang=de https://www.myw.ai/projects “

Not finding any patents for General Vision (GV), but I found this one for Norlitech:

US2020082241A1 COGNITIVE STORAGE DEVICE 20180911

a system comprising a non-volatile storage memory, a controller, and a cognitive memory. The storage memory can store data. During operation, the controller programs a function for the system based on a configuration file. The function indicates one or more operations for the data stored in the storage memory. The cognitive memory can include a set of neuron memory cells, which can store a knowledge base for facilitating the function and execute a pattern matching operation between the data stored in the storage memory and the data stored in the set of neuron memory cells. The controller can then execute the one or more operations within the system based on an output of the pattern matching operation from the cognitive memory.

[0052] FIG. 1B illustrates an exemplary architecture of a CSD, in accordance with an embodiment of the present application. Search engine 130 can include a programmable hardware module 170 , which can be a configurable piece of hardware capable of accessing storage memory 150 , at least in part, to search for reference patterns loaded in cognitive memory 158 . Module 170 can execute firmware-level codes, and operate as a interface logic between storage memory 150 , cognitive memory 158 , cache 156 , and the host (i.e., storage node 116 ). In some embodiments, module 170 can be an FPGA-based module coupled to cognitive memory 158 . Module 170 can be based on one or more of: integrated circuitry, and a semiconductor intellectual property (IP) core. Cognitive memory 158 can include a neuron-based integrated circuit and/or a semiconductor IP core arranged as a single memory bank or a plurality of neuron banks 172 , 174 , and 176 . The components of cognitive memory 158 may be coupled via a PCB, or on multiple chips. Module 170 and cognitive memory 158 can also be parts of an integrated circuit on a common substrate (e.g., on the same die).

What makes this relevant to GV is the inventors are listed as GV employees:
PAILLET GUY; MENENDEZ ANNE

Looks pretty clunky.

 

[Fullmoonfever]

Not finding any patents for General Vision (GV), but I found this one for Norlitech:

US2020082241A1 COGNITIVE STORAGE DEVICE 20180911

View attachment 59920


a system comprising a non-volatile storage memory, a controller, and a cognitive memory. The storage memory can store data. During operation, the controller programs a function for the system based on a configuration file. The function indicates one or more operations for the data stored in the storage memory. The cognitive memory can include a set of neuron memory cells, which can store a knowledge base for facilitating the function and execute a pattern matching operation between the data stored in the storage memory and the data stored in the set of neuron memory cells. The controller can then execute the one or more operations within the system based on an output of the pattern matching operation from the cognitive memory.

[0052] FIG. 1B illustrates an exemplary architecture of a CSD, in accordance with an embodiment of the present application. Search engine 130 can include a programmable hardware module 170 , which can be a configurable piece of hardware capable of accessing storage memory 150 , at least in part, to search for reference patterns loaded in cognitive memory 158 . Module 170 can execute firmware-level codes, and operate as a interface logic between storage memory 150 , cognitive memory 158 , cache 156 , and the host (i.e., storage node 116 ). In some embodiments, module 170 can be an FPGA-based module coupled to cognitive memory 158 . Module 170 can be based on one or more of: integrated circuitry, and a semiconductor intellectual property (IP) core. Cognitive memory 158 can include a neuron-based integrated circuit and/or a semiconductor IP core arranged as a single memory bank or a plurality of neuron banks 172 , 174 , and 176 . The components of cognitive memory 158 may be coupled via a PCB, or on multiple chips. Module 170 and cognitive memory 158 can also be parts of an integrated circuit on a common substrate (e.g., on the same die).

What makes this relevant to GV is the inventors are listed as GV employees:
PAILLET GUY; MENENDEZ ANNE

Looks pretty clunky.

You might be interested in this GV presentation I just found which includes your Norlitech so good sleuthing D.

Full presso could maybe a few years old poss looking at the Rev dates?

It's HERE

 

[jtardif999]

A couple of years back Sean Hehir made a dsmissive statement that Akida 1000 should be seen as an early reference chip and the focus was going be on the later, more developed versions.

Since then Akida 1000, in chip form, has made it into Edge boxes, gone into space and looks like being our major income stream, It also goes out to universities, as chips alone or on sample boards. Good that we made a few chips up front.

Version 1500, taped out a while back, for 22nm, seems to have sunk without trace.

Version 2000 will not be made as a chip, probably because it is very expensive to make, perhaps beyond Brainchip’s resources. So we wait for potential users to be so attracted by its capability that they will fund the manufacture of their own chips, with our IP. And we wait and wait. Meanwhile the peloton closes in.

Not sure this all adds up to a convincing corporate strategy. There is a good case for physical chips to be available for early investigators and researchers, particularly if universities are seen as important cradles of understanding the full possibilities of AKIDA.

The Accenture patent is based on Akida2 IP imo. The patent describes prediction which is not an Akida1 capability.

 

[Fullmoonfever]

Not finding any patents for General Vision (GV), but I found this one for Norlitech:

US2020082241A1 COGNITIVE STORAGE DEVICE 20180911

View attachment 59920


a system comprising a non-volatile storage memory, a controller, and a cognitive memory. The storage memory can store data. During operation, the controller programs a function for the system based on a configuration file. The function indicates one or more operations for the data stored in the storage memory. The cognitive memory can include a set of neuron memory cells, which can store a knowledge base for facilitating the function and execute a pattern matching operation between the data stored in the storage memory and the data stored in the set of neuron memory cells. The controller can then execute the one or more operations within the system based on an output of the pattern matching operation from the cognitive memory.

[0052] FIG. 1B illustrates an exemplary architecture of a CSD, in accordance with an embodiment of the present application. Search engine 130 can include a programmable hardware module 170 , which can be a configurable piece of hardware capable of accessing storage memory 150 , at least in part, to search for reference patterns loaded in cognitive memory 158 . Module 170 can execute firmware-level codes, and operate as a interface logic between storage memory 150 , cognitive memory 158 , cache 156 , and the host (i.e., storage node 116 ). In some embodiments, module 170 can be an FPGA-based module coupled to cognitive memory 158 . Module 170 can be based on one or more of: integrated circuitry, and a semiconductor intellectual property (IP) core. Cognitive memory 158 can include a neuron-based integrated circuit and/or a semiconductor IP core arranged as a single memory bank or a plurality of neuron banks 172 , 174 , and 176 . The components of cognitive memory 158 may be coupled via a PCB, or on multiple chips. Module 170 and cognitive memory 158 can also be parts of an integrated circuit on a common substrate (e.g., on the same die).

What makes this relevant to GV is the inventors are listed as GV employees:
PAILLET GUY; MENENDEZ ANNE

Looks pretty clunky.

This is for the NeuroTile using the NM500. Again 2019.

HERE

 

[jtardif999]

I usually post interesting (but also ridiculous) stuff from TSE in the German forum. This time however, the user “perhaps” who also is sometimes here active, postet today something interesting, I will share.

“I've taken a closer look at MYWAI because I see a lot of potential there. The MYWAI projects use a novel neuromorphic memristor technology (computing processes executed in memory) from General Vision.

If everything works as they claim, they have made a breakthrough in one of the biggest problems in neuromorphic computing. When the task changes, an automatic adjustment of the required neurons is necessary; otherwise, there will be increased inaccuracies. So far, there has been no solution to this problem. Akida is also affected by this issue. With General Vision's approach, it is theoretically possible to control the neurons of the Akida processor via the memristor, unlocking the full potential of Akida. Therefore, the progress of the MYWAI projects deserves increased attention, as this could be where the big breakthrough happens.

Sources: https://general-vision.com/download/neuromem-technology-reference-guide/?wpdmdl=12284&refresh=660275755e5f11711437173 https://neurotechnologijos.com/zusammenarbeit/?lang=de https://www.myw.ai/projects “

“f everything works as they claim, they have made a breakthrough in one of the biggest problems in neuromorphic computing. When the task changes, an automatic adjustment of the required neurons is necessary; otherwise, there will be increased inaccuracies. So far, there has been no solution to this problem. Akida is also affected by this issue. With General Vision's approach, it is theoretically possible to control the neurons of the Akida processor via the memristor, unlocking the full potential of Akida. Therefore, the progress of the MYWAI projects deserves increased attention, as this could be where the big breakthrough happens.”

Where’s the proof that Akida is affected? You can’t go making statements like this without supporting evidence.

 

[7für7]

Not finding any patents for General Vision (GV), but I found this one for Norlitech:

US2020082241A1 COGNITIVE STORAGE DEVICE 20180911

View attachment 59920


a system comprising a non-volatile storage memory, a controller, and a cognitive memory. The storage memory can store data. During operation, the controller programs a function for the system based on a configuration file. The function indicates one or more operations for the data stored in the storage memory. The cognitive memory can include a set of neuron memory cells, which can store a knowledge base for facilitating the function and execute a pattern matching operation between the data stored in the storage memory and the data stored in the set of neuron memory cells. The controller can then execute the one or more operations within the system based on an output of the pattern matching operation from the cognitive memory.

[0052] FIG. 1B illustrates an exemplary architecture of a CSD, in accordance with an embodiment of the present application. Search engine 130 can include a programmable hardware module 170 , which can be a configurable piece of hardware capable of accessing storage memory 150 , at least in part, to search for reference patterns loaded in cognitive memory 158 . Module 170 can execute firmware-level codes, and operate as a interface logic between storage memory 150 , cognitive memory 158 , cache 156 , and the host (i.e., storage node 116 ). In some embodiments, module 170 can be an FPGA-based module coupled to cognitive memory 158 . Module 170 can be based on one or more of: integrated circuitry, and a semiconductor intellectual property (IP) core. Cognitive memory 158 can include a neuron-based integrated circuit and/or a semiconductor IP core arranged as a single memory bank or a plurality of neuron banks 172 , 174 , and 176 . The components of cognitive memory 158 may be coupled via a PCB, or on multiple chips. Module 170 and cognitive memory 158 can also be parts of an integrated circuit on a common substrate (e.g., on the same die).

What makes this relevant to GV is the inventors are listed as GV employees:
PAILLET GUY; MENENDEZ ANNE

Looks pretty clunky.

I’m not a technical expert but it’s better than the speculations about what happens to robs LinkedIn profile or if he flushed the toilet once or twice and if so, why he don’t like postings about Japanese toilets?

 

[7für7]

“f everything works as they claim, they have made a breakthrough in one of the biggest problems in neuromorphic computing. When the task changes, an automatic adjustment of the required neurons is necessary; otherwise, there will be increased inaccuracies. So far, there has been no solution to this problem. Akida is also affected by this issue. With General Vision's approach, it is theoretically possible to control the neurons of the Akida processor via the memristor, unlocking the full potential of Akida. Therefore, the progress of the MYWAI projects deserves increased attention, as this could be where the big breakthrough happens.”

Where’s the proof that Akida is affected? You can’t go making statements like this without supporting evidence.

It’s not my statement I just find it interesting and plausible.

 

[Fullmoonfever]

Not finding any patents for General Vision (GV), but I found this one for Norlitech:

US2020082241A1 COGNITIVE STORAGE DEVICE 20180911

View attachment 59920


a system comprising a non-volatile storage memory, a controller, and a cognitive memory. The storage memory can store data. During operation, the controller programs a function for the system based on a configuration file. The function indicates one or more operations for the data stored in the storage memory. The cognitive memory can include a set of neuron memory cells, which can store a knowledge base for facilitating the function and execute a pattern matching operation between the data stored in the storage memory and the data stored in the set of neuron memory cells. The controller can then execute the one or more operations within the system based on an output of the pattern matching operation from the cognitive memory.

[0052] FIG. 1B illustrates an exemplary architecture of a CSD, in accordance with an embodiment of the present application. Search engine 130 can include a programmable hardware module 170 , which can be a configurable piece of hardware capable of accessing storage memory 150 , at least in part, to search for reference patterns loaded in cognitive memory 158 . Module 170 can execute firmware-level codes, and operate as a interface logic between storage memory 150 , cognitive memory 158 , cache 156 , and the host (i.e., storage node 116 ). In some embodiments, module 170 can be an FPGA-based module coupled to cognitive memory 158 . Module 170 can be based on one or more of: integrated circuitry, and a semiconductor intellectual property (IP) core. Cognitive memory 158 can include a neuron-based integrated circuit and/or a semiconductor IP core arranged as a single memory bank or a plurality of neuron banks 172 , 174 , and 176 . The components of cognitive memory 158 may be coupled via a PCB, or on multiple chips. Module 170 and cognitive memory 158 can also be parts of an integrated circuit on a common substrate (e.g., on the same die).

What makes this relevant to GV is the inventors are listed as GV employees:
PAILLET GUY; MENENDEZ ANNE

Looks pretty clunky.

Excerpt from a 2021 Science Direct paper. NeuroEdge licenced GV NM500 and Neuromem.

https://www.sciencedirect.com/science/article/pii/S240595952100059X

3. System model and detailed description of the NeuroEdge computing system​

A detailed block diagram of the NeuroEdge system showing the role and placement of the NM500 chip, is presented in Fig. 1. The NeuroEdge system uses a neuromorphic device combined with Raspberry Pi to achieve real-time field training and inference on devices. NM500, however, is a neuromorphic chip whose neurons can learn and recognize patterns extracted from any data sources with less energy and complexity than modern microprocessors. NeuroEdge and NM500 are produced under the license of NeuroMem technology from General Vision Inc. [11]. Fig. 3 shows the structure of the NM500 and its application in various AI systems such as image, voice or video identification, classification, and anomaly detection.

 

[Diogenese]

So far, the lack of an interface to implement the technology and make it mass-market compatible is the problem. That's why we are constantly hovering in the field of "testing" and quasi-successful integration of Akida into studies and prototypes, etc. It's about mass marketability.

For the last couple of years, we have been focusing on the highly specialized market of IP licensing.

However the Akida AI Edge Box is directed to a much broader market, and it is pretty much plug-and-play. It's just a matter of loading the appropriate models and configuration.

 

[Fullmoonfever]

Not finding any patents for General Vision (GV), but I found this one for Norlitech:

US2020082241A1 COGNITIVE STORAGE DEVICE 20180911

View attachment 59920


a system comprising a non-volatile storage memory, a controller, and a cognitive memory. The storage memory can store data. During operation, the controller programs a function for the system based on a configuration file. The function indicates one or more operations for the data stored in the storage memory. The cognitive memory can include a set of neuron memory cells, which can store a knowledge base for facilitating the function and execute a pattern matching operation between the data stored in the storage memory and the data stored in the set of neuron memory cells. The controller can then execute the one or more operations within the system based on an output of the pattern matching operation from the cognitive memory.

[0052] FIG. 1B illustrates an exemplary architecture of a CSD, in accordance with an embodiment of the present application. Search engine 130 can include a programmable hardware module 170 , which can be a configurable piece of hardware capable of accessing storage memory 150 , at least in part, to search for reference patterns loaded in cognitive memory 158 . Module 170 can execute firmware-level codes, and operate as a interface logic between storage memory 150 , cognitive memory 158 , cache 156 , and the host (i.e., storage node 116 ). In some embodiments, module 170 can be an FPGA-based module coupled to cognitive memory 158 . Module 170 can be based on one or more of: integrated circuitry, and a semiconductor intellectual property (IP) core. Cognitive memory 158 can include a neuron-based integrated circuit and/or a semiconductor IP core arranged as a single memory bank or a plurality of neuron banks 172 , 174 , and 176 . The components of cognitive memory 158 may be coupled via a PCB, or on multiple chips. Module 170 and cognitive memory 158 can also be parts of an integrated circuit on a common substrate (e.g., on the same die).

What makes this relevant to GV is the inventors are listed as GV employees:
PAILLET GUY; MENENDEZ ANNE

Looks pretty clunky.

From last year's TinyML forum. We didn't appear to be in on this one.

tinyML On Device Learning Forum 2023

We held tinyML On Device Learning Forum 2023 on May 16th, 2023. To date, most ultra-low power machine learning (ML) applications at the edge are trained “off device” (typically in the cloud where virtually unlimited computing assets are available) while the edge devices perform the...

forums.tinyml.org

NeuroMem®, Ultra Low Power hardwired incremental learning and parallel pattern recognition
Guy Paillet, Co-founder and Chairman, General Vision Holdings

GV will present a Tiny RTML platform comprising of ST Nucleo64, together with a NeuroShield including 37 parallelized NM500 chips. This allows maintaining a parallel content addressable set of for example 21,000 Chinese characters. Submitting the image (16 x 16 pixels pattern) of a Chinese character, will return a category pointing on the English meaning within a constant search time of 30 microseconds. Learning time for additional character (on the spot learning) will also take about 30 microseconds per unknown character. The ANM5500 just released will make the same with only 4 chips and 5 times faster always, at milliwatts power. General Vision goal is to solve real world image recognition with learning and recognition on a small battery into for example a standalone (no network connection) Barbie doll, hence the patented “Monolithic Image Perception Device” successor of MTVS (Miniature Trainable Vision Sensor) allowing on “image sensor learning” and recognition.

Guy’s background is hardware design since 1976 starting with Motorola MC6800 as application engineer. He has been innovating on high performance Tiny Machine Learning since 1993 while inventing the ZISC36 with IBM Paris, Guy and family moved from France in 1996 and co-founder General Vision in 2000. Since, General has licensed its NeuroMem ZISC technology giving birth to 4 additional successful Neuromorphic AISC from 2007 to 2022, including the Intel Curie for “NeuroMEMS.”

 

[7für7]

For the last couple of years, we have been focusing on the highly specialized market of IP licensing.

However the Akida AI Edge Box is directed to a much broader market, and it is pretty much plug-and-play. It's just a matter of loading the appropriate models and configuration.

For which broader market do you think a costumer would take the AI edge box? This is again just a trial device in my opinion. More important is, what kind of steps our partner are doing in the ecosystem in my opinion.

 

[Diogenese]

This is for the NeuroTile using the NM500. Again 2019.

HERE

View attachment 59924

1152 silicon neurons - was this found at Cheops?

 

[Fullmoonfever]

1152 silicon neurons - was this found at Cheops?

Maybe....the Egyptians were pretty advanced a few thousand years ago.

 

[cosors]

For anyone speculative... answers in screenshot

Thanks Reuben for clearing that up for us, and thanks to you mrgds too!

TD I'm sorry, there are more important things to do. Please excuse us for keeping you from work today, we're just very interested.

 

[Justchilln]

A couple of years back Sean Hehir made a dsmissive statement that Akida 1000 should be seen as an early reference chip and the focus was going be on the later, more developed versions.

Since then Akida 1000, in chip form, has made it into Edge boxes, gone into space and looks like being our major income stream, It also goes out to universities, as chips alone or on sample boards. Good that we made a few chips up front.

Version 1500, taped out a while back, for 22nm, seems to have sunk without trace.

Version 2000 will not be made as a chip, probably because it is very expensive to make, perhaps beyond Brainchip’s resources. So we wait for potential users to be so attracted by its capability that they will fund the manufacture of their own chips, with our IP. And we wait and wait. Meanwhile the peloton closes in.

Not sure this all adds up to a convincing corporate strategy. There is a good case for physical chips to be available for early investigators and researchers, particularly if universities are seen as important cradles of understanding the full possibilities of AKIDA.

The Unigen cupcake uses akd1500

 

[chapman89]

Posted by Edge Impulse this morning.
There’s a lot of pages but I’ll just post some of it where it mentions Brainchip, as it won’t allow me to copy and paste for some reason, and those smarter than me can break down the whole thing if they have time and are willing.

https://assets.website-files.com/618cdeef45d18e4ef2fd85f3/65f826d1c27e243a45437112_Edge%20Impulse%20Industrial%20Productivity%20Solutions%20Guide%20v1.0_compressed%20(1).pdf?utm_content=287309981&utm_medium=social&utm_source=linkedin&hss_channel=lcp-19239958

What engine does Edge Impulse use to compile the Impulse?


It depends on the hardware.


For general-purpose MCUs, we typically use EON Compiler with TFLite Micro kernels (including hardware optimization, e.g. via CMSIS-NN, ESP-NN).


On Linux, if you run the Impulse on the CPU, we use TensorFlow Lite.


For accelerators, we use a wide variety of other runtimes, e.g., hardcoded network in silicon for Syntiant, custom SNN-based inference engine for Brainchip Akida, DRP-Al for Renesas RZV2L, etc.

 

[Pom down under]

BrainChip at Embedded World 2024 Explores AI: Terrestrial and Extra-Terrestrial!​

BrainChip is thrilled to participate in the 22nd annual Embedded World Conference and Exhibition, taking place from April 9thto 11th, 2024 in Nuremberg, Germany. At the leading event for embedded systems learn how Akida, our neuromorphic design, contributes to the future of AI in Industrial, Consumer, Automotive, Health, and Space Technology.

Here's what you can expect from BrainChip at the conference:

Transforming AI: Discover how AI is transforming industries on Earth and beyond, with insights into the latest advancements and applications. Untethered Edge AI: Explore the rapid growth of Edge AI solutions and learn about their key benefits in various sectors, including Industrial, Automotive, Consumer Electronics, and Space Technology. Deploying Akida: Explore how Edge AI Boxes, built in partnership with partners such as VVDN Technologies, can accelerate deployments for Intelligent Retail or Scalable Security solutions. Akida Technology Enablement: Discover how BrainChip streamlines solution delivery with reference platforms, development kits, and SoC-ready designs like the AKD1500. Experience seamless AI model development using BrainChip’s MetaTF and Edge Impulse’s platform. Akida in Action: Experience Akida in action with our demos and explore a range of other use case demonstrations. Space: The Next Frontier: Gain insights into the future of Space Technology and how BrainChip is contributing to advancements in this field.

Don’t miss this opportunity to engage with our team and explore the cutting-edge technologies shaping the future. Join us at Hall 2, Booth# 2-338 in the tinyML Pavilion and immerse yourself in the future of Edge AI and Space Exploration. To schedule a meeting with our team, click the Calendly link below:

 

[IloveLamp]

@Slade

 

[supersonic001]

BrainChip on LinkedIn: #ai #edgeai #neuromorphic

Revolution (noun): A dynamic, transformative shift in technological paradigms, reshaping societal norms, industries, and human interaction. Join the #AI…

www.linkedin.com