BRN Discussion Ongoing

[gilti]

Is this us?
View attachment 38955 View attachment 38956 View attachment 38957

Socionext | 60GHz Radar Sensor | Automotive Applications

60GHz sensors with (TDM-MIMO) processing for detecting the position and movement of vehicle passengers with maximum accuracy

socionextus.com

If you read the text it says it is us

 

[Fox151]

Is this us?
View attachment 38955 View attachment 38956 View attachment 38957

Socionext | 60GHz Radar Sensor | Automotive Applications

60GHz sensors with (TDM-MIMO) processing for detecting the position and movement of vehicle passengers with maximum accuracy

socionextus.com

How is this compatible with Sean Hehir's commentary re. The success, or lack thereof, of Akida 1?

 

[Damo4]

If you read the text it says it is us

I think they screenshotted the original Brainchip/Socionext article and then provided the link for this new press release.
It doesn't specifically say it's us, but looking at the "SC1260" naming and the links we already have, it's 99.99% us.
This release is as follows:

Socionext Introduces Ultra-compact, Ultra-low-power 60GHz Radio-wave Ranging Sensors for Automotive Applications​

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New SC1260 Series Detects Position, Movement of Vehicle Passengers with Maximum Accuracy​

Yokohama, Japan and Milpitas, Calif., May 30, 2023 --- Socionext Inc., a global leader in high-precision sensor technology, today introduced the SC1260 Series radio-wave ranging sensors for automotive applications. This new series utilizes the 60GHz frequency band and time-division multiplexing (TDM-MIMO) processing with multiple transmitting and receiving antennas that can detect the position and movement of passengers in a vehicle with maximum accuracy.

SC1260AR3
This SC1260 has an all-in-one configuration incorporating antennas and radar signal processing circuits to achieve maximum detection accuracy in an ultra-low-power, ultra-compact package. The high-precision sensors use a wide band of 6.8GHz (57.1 to 63.9GHz) with an expanded number of receiving antennas by TDM-MIMO processing and built-in radar signal processing circuits for range-finding and angle calculation. The devices enable easy acquisition of three-dimensional (3D) position information without requiring advanced expertise in high-frequency devices and signal processing. combines antennas, wireless circuits, AD converters, FIFO memory, SPI interface, and intelligent power control sequencer for flexible duty cycle control. The power required is only 0.72mW at 0.1% duty cycle operation.
These capabilities make the series optimal for applications such as tracking the position and movements of passengers in a vehicle while suppressing the load on the vehicle battery when the engine is off.
The SC1260 Series complies with the global broadband 60GHz radio equipment standard. Sample and evaluation kit shipments are scheduled for June, with production volumes to be available in Q1 2024.
“By leveraging our extensive experience and knowledge accumulated through the development of millimeter-wave wireless communication ICS and 24GHz radio-wave ranging sensors, Socionext has become the first company worldwide to develop ultra-compact, ultra-low power 60GHz radio-wave ranging sensors for automotive applications with built-in radar signal processing circuits for range-finding and angle calculations in automotive applications,” said Teruaki Hasegawa, Head of Socionext's IoT & Radar Sensor Business Unit.
Moreover, TDM-MIMO processing enables high-precision sensing, such as the detection of passengers when three people are sitting in a row inside the vehicle.
Following the debut of its SC1260AR3, Socionext will continue to develop products that meet customer needs and applications.
Socionext is a leader in the advancement of sensing applications through its unique lineup of radio-wave ranging sensor devices and will continue to be a world leader in this field by championing new product development for enhancing user experience.
SC1260 Series (SC1260AR3) Specifications

Key Features	TDM-MIMO operation, 3D position detection (X, Y, Z coordinate output), 3D presence/absence detection, range FFT output, automatic intermittent measurement, high-performance power supply noise filter, 11-bit oversampling ADC, advanced sequencer
Average Power Consumption	0.72mW at 0.1% duty cycle operation
Transmission Frequency	57.1 to 63.9GHz
Package / Size	FC-BGA / 6mm x 9mm x 1.2mm

Related Links
SC1260AR3 Product Page
Radar Sensor Product Lineup

 

[miaeffect]

I think they screenshotted the original Brainchip/Socionext article and then provided the link for this new press release.
It doesn't specifically say it's us, but looking at the "SC1260" naming and the links we already have, it's 99.99% us.
This release is as follows:

Socionext Introduces Ultra-compact, Ultra-low-power 60GHz Radio-wave Ranging Sensors for Automotive Applications​

Share this post
Share TweetShare

New SC1260 Series Detects Position, Movement of Vehicle Passengers with Maximum Accuracy​

Yokohama, Japan and Milpitas, Calif., May 30, 2023 --- Socionext Inc., a global leader in high-precision sensor technology, today introduced the SC1260 Series radio-wave ranging sensors for automotive applications. This new series utilizes the 60GHz frequency band and time-division multiplexing (TDM-MIMO) processing with multiple transmitting and receiving antennas that can detect the position and movement of passengers in a vehicle with maximum accuracy.

SC1260AR3
This SC1260 has an all-in-one configuration incorporating antennas and radar signal processing circuits to achieve maximum detection accuracy in an ultra-low-power, ultra-compact package. The high-precision sensors use a wide band of 6.8GHz (57.1 to 63.9GHz) with an expanded number of receiving antennas by TDM-MIMO processing and built-in radar signal processing circuits for range-finding and angle calculation. The devices enable easy acquisition of three-dimensional (3D) position information without requiring advanced expertise in high-frequency devices and signal processing. combines antennas, wireless circuits, AD converters, FIFO memory, SPI interface, and intelligent power control sequencer for flexible duty cycle control. The power required is only 0.72mW at 0.1% duty cycle operation.
These capabilities make the series optimal for applications such as tracking the position and movements of passengers in a vehicle while suppressing the load on the vehicle battery when the engine is off.
The SC1260 Series complies with the global broadband 60GHz radio equipment standard. Sample and evaluation kit shipments are scheduled for June, with production volumes to be available in Q1 2024.
“By leveraging our extensive experience and knowledge accumulated through the development of millimeter-wave wireless communication ICS and 24GHz radio-wave ranging sensors, Socionext has become the first company worldwide to develop ultra-compact, ultra-low power 60GHz radio-wave ranging sensors for automotive applications with built-in radar signal processing circuits for range-finding and angle calculations in automotive applications,” said Teruaki Hasegawa, Head of Socionext's IoT & Radar Sensor Business Unit.
Moreover, TDM-MIMO processing enables high-precision sensing, such as the detection of passengers when three people are sitting in a row inside the vehicle.
Following the debut of its SC1260AR3, Socionext will continue to develop products that meet customer needs and applications.
Socionext is a leader in the advancement of sensing applications through its unique lineup of radio-wave ranging sensor devices and will continue to be a world leader in this field by championing new product development for enhancing user experience.
SC1260 Series (SC1260AR3) Specifications

Key Features	TDM-MIMO operation, 3D position detection (X, Y, Z coordinate output), 3D presence/absence detection, range FFT output, automatic intermittent measurement, high-performance power supply noise filter, 11-bit oversampling ADC, advanced sequencer
Average Power Consumption	0.72mW at 0.1% duty cycle operation
Transmission Frequency	57.1 to 63.9GHz
Package / Size	FC-BGA / 6mm x 9mm x 1.2mm

Related Links
SC1260AR3 Product Page
Radar Sensor Product Lineup

Yes, they are using same video clip.

 

[Diogenese]

Is this us?
View attachment 38955 View attachment 38956 View attachment 38957

Socionext | 60GHz Radar Sensor | Automotive Applications

60GHz sensors with (TDM-MIMO) processing for detecting the position and movement of vehicle passengers with maximum accuracy

socionextus.com

Hi Mia,

Believe!

This is all on the one page:

Top right: NNA = neuromorphic network accelerator.

Automotive Custom SoC Technologies and Solutions (socionextus.com)

Automotive Custom SoC Technologies and Solutions

Socionext’s advanced automotive custom SoC solutions are designed to help OEMs and tier-one automakers achieve differentiating technologies

socionextus.com

These custom SoCs enable a wide range of applications, including ADAS sensors, central computing, networking, in-cabin monitoring, satellite connectivity, and infotainment.

…

Advanced AI Solutions for Automotive

Socionext has partnered with artificial intelligence provider BrainChip to develop optimized, intelligent sensor data solutions based on Brainchip’s Akida® processor IP.

BrainChip’s flexible AI processing fabric IP delivers neuromorphic, event-based computation, enabling ultimate performance while minimizing silicon footprint and power consumption. Sensor data can be analyzed in real-time with distributed, high-performance and low-power edge inferencing, resulting in improved response time and reduced energy consumption.

This video is also on the same page:

 

[Esq.111]

Hi Mia,

Believe!

This is all on the one page:

Top right: NNA = neuromorphic network accelerator.

Automotive Custom SoC Technologies and Solutions (socionextus.com)

Automotive Custom SoC Technologies and Solutions

Socionext’s advanced automotive custom SoC solutions are designed to help OEMs and tier-one automakers achieve differentiating technologies

socionextus.com

View attachment 38959

These custom SoCs enable a wide range of applications, including ADAS sensors, central computing, networking, in-cabin monitoring, satellite connectivity, and infotainment.

…

Advanced AI Solutions for Automotive

Socionext has partnered with artificial intelligence provider BrainChip to develop optimized, intelligent sensor data solutions based on Brainchip’s Akida® processor IP.

BrainChip’s flexible AI processing fabric IP delivers neuromorphic, event-based computation, enabling ultimate performance while minimizing silicon footprint and power consumption. Sensor data can be analyzed in real-time with distributed, high-performance and low-power edge inferencing, resulting in improved response time and reduced energy consumption.

This video is also on the same page:
View attachment 38960
View attachment 38961


View attachment 38958

Good Afternoon Diogenese, Fellow Chippers,

Think we should ask Rocket to post the Giff featuring the six or seven Japanese businessmen dressed in Amani suite's drinking beer & whilst rythmically swaying their hips.

WOOOooo HOOOooo.

Regards,
Esq

 

[Xray1]

Do you remember his early statements? They ahve not rung true. I believe he has probably been reined in by the board from making such statements again...........remember this is his first CEO gig. His history is that of a great salesman so he's having to learn a new craft I think..........

I hope he will be turning things around quickly over the next few months given the Co has it's first AGM "Strike" action against it's name !!! ........... imo, probably something he wouldn't want recorded / disclosed on his CV.

 

[IloveLamp]

Good Afternoon Diogenese, Fellow Chippers,

Think we should ask Rocket to post the Giff featuring the six or seven Japanese businessmen dressed in Amani suite's drinking beer & whilst rythmically swaying their hips.

WOOOooo HOOOooo.

Regards,
Esq

I gotchyu

 

[Sirod69]

AiLabs Inc


AiLabs Service Offerings:

1. AI Strategy and Roadmap Development: We work closely with clients to understand their business goals and develop an AI strategy that aligns with their objectives. Our consultants identify areas where AI can drive innovation, streamline processes, and enhance decision-making, ultimately creating a roadmap for successful AI implementation.

2. AI Solution Design and Development: Our team designs and develop sAI solutions tailored to meet specific business needs. This includes creating machine learning models, building intelligent systems, and integrating AI capabilities into existing workflows. We leverage industry best practices and ensure the scalability and reliability of our solutions.

3. AI Staffing: Recognizing the shortage of AI talent in the market, we offer AI staffing services to help businesses find skilled professionals for their in-house AI teams. We rigorously screen and assess candidates to ensure they possess the necessary technical skills and domain knowledge required by our clients.

4. AI Training and Education: To bridge the AI skills gap and empower organizations, we provide training programs and workshops on AI fundamentals, advanced AI techniques, and AI ethics. These programs will cater to both technical and non-technical professionals, equipping them with the knowledge to understand and leverage AI effectively.

 

[Thebask27]

Shortman indicates otherwise

Sorry, but your attachment is from 19.6.

 

[cassip]

In German Handelsblatt today, articel behind paywall:

"The hardware of the future for AI could come from Europe
The future of artificial intelligence could lie in a kind of artificial brain. The magic word: neuromorphic computer chips. Leading the way in their development are companies from Paris and Dresden." (translated with deepl.com)

https://www.handelsblatt.com/technik/forschung-innovation/neuro-chips-die-hardware-der-zukunft-fuer-ki-koennte-aus-europa-kommen/29223908.html

- some information about advantages of neuromorphic computing and that it is "revolutionary"
- no mention of Brainchip
- hint to scientific center in France (Thales and CNRS)
- Startup "Spincloud" and its chip "Spinnaker 2" (--> TU Dresden); Steve Furber, Professor of Manchester, participates, he has a former connection to Arm

...when you go to the Spinnaker website you read GlobalFoundries as well (dated 2021):

SpiNNaker2: TU Dresden, University of Manchester und Globalfoundries gelingt Durchbruch bei KI-Cloud-Systemen

Die Technische Universität Dresden, die University of Manchester, die Racyics GmbH und Globalfoundries (GF) gaben heute das so genannte Tapeout des SpiNNaker2-Chips bekannt – eines neuen Chips für künstliche Intelligenz, der vom menschlichen Gehirn inspiriert ist.

tu-dresden.de

"SpiNNaker2: TU Dresden, University of Manchester and Globalfoundries achieve breakthrough in AI cloud systems"

Second Generation SpiNNaker Neuromorphic Supercomputer to be Built at TU Dresden

None

www.humanbrainproject.eu

This is their website:

Home - SpiNNcloud Systems

spinncloud.com

BrainChip Tapes Out AKD1500 Chip in GlobalFoundries 22nm FD SOI Process - (2023)​

Could there be a connection or is it competition?

Spinnaker was mentioned here before, maybe anybody knows more about them? Interesting comments on the homepage.

Regards
Cassip

 

[Diogenese]

Good Afternoon Diogenese, Fellow Chippers,

Think we should ask Rocket to post the Giff featuring the six or seven Japanese businessmen dressed in Amani suite's drinking beer & whilst rythmically swaying their hips.

WOOOooo HOOOooo.

Regards,
Esq

I can just see MF's headline tomorrow:

"Major fabless chipmaker Socionext partners with BrainChip for Automotive AI Processor"

" ... but before you buy BrainChip, our shaman Bengt P Nuss, has 200 other shares you should buy instead"

 

[cassip]

Interesting article from Fraunhofer (2022), mention of Loihi, Spinnaker and Akida:

Künstliche neuronale Netze – Maschinelles Sehen mit weniger Daten will gelernt sein – Magazin des Fraunhofer-Instituts für Kognitive Systeme IKS

Gegenüber dem menschlichen Gehirn verbrauchen künstliche neuronale Netze selbst für einfache Unterscheidungsaufgaben immens mehr Strom. Hier kommt Event-based Vision ins Spiel.

safe-intelligence.fraunhofer.de

"Artificial neural networks
Machine vision with less data needs to be learned

Compared to the human brain, artificial neural networks consume immensely more power even for simple discrimination tasks. Event-based vision provides a promising approach to solve this problem. However, there are still some challenges to overcome."

..."A major problem is also the lack of hardware availability. Although there are already some suppliers for event cameras (e.g., Prophesee, iniVation, Samsung) and hardware implementations of neuromorphic chips (e.g., Loihi/Loihi2, SpiNNaker, Akida), the acquisition is either not even possible or the purchase price exceeds that of conventional hardware by a multiple. Required data sets for learning various tasks can thus only be created with difficulty. Some approaches deal with the conversion of images to event data, but the high temporal resolution is lost. However, first comprehensive datasets in the field of autonomous driving [5, 6] show a positive trend."

(translated with deepl)

 

[hotty4040]

I hope he will be turning things around quickly over the next few months given the Co has it's first AGM "Strike" action against it's name !!! ........... imo, probably something he wouldn't want recorded / disclosed on his CV.

Pardon my ignorance Xray 1, but what do you mean by " strike action " and in what form is this mentioned.

I'm not familiar with this term from the AGM As one parliamentarian with some other type of form would say....

" Please explain "

Alida Ballista

hotty...

 

[Diogenese]

I gotchyu

View attachment 38962

Kurosawa's Seven Saki with Toshiro Mifumi

 

[hotty4040]

Pardon my ignorance Xray 1, but what do you mean by " strike action " and in what form is this mentioned.

I'm not familiar with this term from the AGM As one parliamentarian with some other type of form would say....

" Please explain "

Alida Ballista

hotty...

Sorry bout the above typo, I meant ( A leader ) Ballista of course

Hotty...

 

[Slade]

Hi Mia,

Believe!

This is all on the one page:

Top right: NNA = neuromorphic network accelerator.

Automotive Custom SoC Technologies and Solutions (socionextus.com)

Automotive Custom SoC Technologies and Solutions

Socionext’s advanced automotive custom SoC solutions are designed to help OEMs and tier-one automakers achieve differentiating technologies

socionextus.com

View attachment 38959

These custom SoCs enable a wide range of applications, including ADAS sensors, central computing, networking, in-cabin monitoring, satellite connectivity, and infotainment.

…

Advanced AI Solutions for Automotive

Socionext has partnered with artificial intelligence provider BrainChip to develop optimized, intelligent sensor data solutions based on Brainchip’s Akida® processor IP.

BrainChip’s flexible AI processing fabric IP delivers neuromorphic, event-based computation, enabling ultimate performance while minimizing silicon footprint and power consumption. Sensor data can be analyzed in real-time with distributed, high-performance and low-power edge inferencing, resulting in improved response time and reduced energy consumption.

This video is also on the same page:
View attachment 38960
View attachment 38961


View attachment 38958

I most certainly do believe. A change is coming!!! Love it.

 

[Quatrojos]

Is this us?
View attachment 38955 View attachment 38956 View attachment 38957

Socionext | 60GHz Radar Sensor | Automotive Applications

60GHz sensors with (TDM-MIMO) processing for detecting the position and movement of vehicle passengers with maximum accuracy

socionextus.com

 

[robsmark]

Is this us?
View attachment 38955 View attachment 38956 View attachment 38957

Socionext | 60GHz Radar Sensor | Automotive Applications

60GHz sensors with (TDM-MIMO) processing for detecting the position and movement of vehicle passengers with maximum accuracy

socionextus.com

Assuming this is us, it leaves us with the million dollar question; Should we expect a commercial announcement here, or will Socionext go through Renesas or Megachips?

 

[Diogenese]

Hi Mia,

Believe!

This is all on the one page:

Top right: NNA = neuromorphic network accelerator.

Automotive Custom SoC Technologies and Solutions (socionextus.com)

Automotive Custom SoC Technologies and Solutions

Socionext’s advanced automotive custom SoC solutions are designed to help OEMs and tier-one automakers achieve differentiating technologies

socionextus.com

View attachment 38959

These custom SoCs enable a wide range of applications, including ADAS sensors, central computing, networking, in-cabin monitoring, satellite connectivity, and infotainment.

…

Advanced AI Solutions for Automotive

Socionext has partnered with artificial intelligence provider BrainChip to develop optimized, intelligent sensor data solutions based on Brainchip’s Akida® processor IP.

BrainChip’s flexible AI processing fabric IP delivers neuromorphic, event-based computation, enabling ultimate performance while minimizing silicon footprint and power consumption. Sensor data can be analyzed in real-time with distributed, high-performance and low-power edge inferencing, resulting in improved response time and reduced energy consumption.

This video is also on the same page:
View attachment 38960
View attachment 38961


View attachment 38958

Right!

Now I've had a cold shower, it should be noted that Socionext has had an NNA since at least 2018:

https://socionextus.com/pressreleas...ically for deep learning inference processing.

Socionext Develops AI Accelerator Engine Optimized for Edge Computing​

Socionext Develops AI Accelerator Engine Optimized for Edge Computing​

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Small-sized and Low Power Engine Supports Broad Range of Applications​

SUNNYVALE, Calif., May 11, 2018 –Socionext Inc., a leading provider of SoC-based solutions, has developed a new Neural Network Accelerator (NNA) engine, optimized for AI processing on edge computing devices. The compact, low power engine has been designed specifically for deep learning inference processing. When implemented, it can achieve 100x performance boost compared with conventional processors for computer vision processing such as image recognition. Socionext will start delivering the Software Development Kit for the FPGA implementation of the NNA in the third quarter of 2018. The company is also planning to develop its SoC products with the NNA.
Socionext currently provides graphics SoC "SC1810" with a built-in proprietary Vision Processor Unit compatible with the computer vision API "OpenVX" developed by the Khronos Group, a standardization organization. The NNA has been designed to work as an accelerator to extend the capability of the VPU. It performs various computer vision processing functions with deep learning, as well as conventional image recognition, for applications including automotive and digital signage, delivering higher performance and lower power consumption.
The NNA incorporates the company's proprietary architecture using the quantization technology that reduces the bits for parameters and activations required for deep learning. The quantization technology is capable of carrying out massive amounts of computing tasks with less resource, greatly reducing the data size, and significantly lowering the system memory bandwidth. In addition, the newly developed on-chip memory circuit design improves the efficiency of computing resource required for deep learning, enabling optimum performance in a very small package. A VPU equipped with the new NNA combined with the latest technologies will be able to achieve 100 times faster processing speed in image recognition compared with a conventional VPU.

Now the interesting thing is that Socionext have a patent application dating from mid-2018 whose purpose is to reduce the calculations required for large MAC loads.

US2021081489A1 ARITHMETIC METHOD 20180604

[0010] An arithmetic method according to the present disclosure is an arithmetic method of performing convolution operation in convolutional layers of a neural network by calculating matrix products, using an arithmetic unit and an internal memory included in a LSI. The arithmetic method includes: determining, for each of the convolutional layers, whether an amount of input data to be inputted to the convolutional layer is smaller than or equal to a predetermined amount of data; selecting a first arithmetic mode and performing convolution operation in the first arithmetic mode, when the amount of input data is determined to be smaller than or equal to the predetermined amount of data in the determining; selecting a second arithmetic mode and performing convolution operation in the second arithmetic mode, when the amount of input data is determined to be larger than the predetermined amount of data in the determining; and outputting output data which is a result obtained by performing convolution operation, in which the performing of convolution operation in the first arithmetic mode includes: storing weight data for the convolutional layer in external memory located outside the LSI; storing the input data for the convolutional layer in the internal memory; and reading the weight data from the external memory into the internal memory part by part as first data of at least one row vector or column vector, and causing the arithmetic unit to calculate a matrix product of the first data and a matrix of the input data stored in the internal memory, the weight data is read, as a whole, from the external memory into the internal memory only once, the performing of convolution operation in the second arithmetic mode includes: storing the input data for the convolutional layer in the external memory located outside the LSI; storing a matrix of the weight data for the convolutional layer in the internal memory; and reading the input data from the external memory into the internal memory part by part as second data of at least one column vector or row vector, and causing the arithmetic unit to calculate a matrix product of the second data and the matrix of the weight data stored in the internal memory, and the input data is read, as a whole, from the external memory into the internal memory only once.

Now it was about 2018 that BrainChip and Socionext began their cooperation, so their original NNA was developed in advance of their association with Akida.

If we assume that this patent is their description of their original NNA, Akida would wipe the floor with it. Akida could perform the functions of the VPU with NNA above in a trice. Given that SocioNext have undoubtedly seen Akida in action, bearing in mind their initial enthusiasm for a Synquacer/Akida engagement, would they persist with their clunky NNA from last millennium?