BRN Discussion Ongoing

[manny100]

Hi All
There has been some discussion about the cross trade.

The theory of the cross trade is that if you hold a large number of shares and need to dispose of a large proportion quickly but you do not want to crash the price of your remaining holding you go to a broker, any broker, and say I have 4 million shares can you match me off market with a buyer. I will take the closing price today. The broker will then attempt to match your sale with a buyer or buyers who is/are happy to pay the closing price.

As a retail share holder at a time when there is no interest in your company a large sale being attended to off market in this way it could be argued as being a good thing rather than a large number of at market need the money today fire sale on market dump.

When a companies shares are galloping along however the advantage of off market sales like this become less clear. It has removed 4 million from the buy side and this pressure of a genuine buyer is never shared with ordinary retail holders.

From the sellers perspective if forced to sell into the rising market they may well have on Friday set a minimum price higher than the 49 cents say 51 cents and that pressure may have been enough to have closed the price for retail higher than 49 cents.

There is no doubt that the above distorts normal considerations of a free market that determines price.

(Yes some cross trades are between related entities for a range of purposes but the annoying thing is the reason behind cross trades is not required to be disclosed so the market is always left blind.)

The final thing I would say is this if a seller tells his broker he will take the closing price and the broker tells the buyer he can have them for the closing price there is a temptation in play for someone to manipulate the closing price either up or down.

Anyone watching the drop from 54.5 cents to 49 cents could get the wrong idea just as this wealthier ordinary retail investor did in the UK only to discover he was right to feel hard done by and to challenge what happened???

https://www.fca.org.uk/news/press-releases/barclays-fined-£26m-failings-surrounding-london-gold-fixing-and-former-barclays

Clicking on the FCA links gets you the full story.

And this was not done on the Pink Sheets by no name fly by night brokers it was Barclays a member of the Golden Circle.

In short the retail client had bet on a closing price on a particular day and right up to the last he was a winner then out of the blue no not today Josephine. He thought this is suspicious. He complained Barclays who investigated and said all good nothing to see here. Unhappy he complains to FCA and guess what the evidence of what had been done was in the traders file. He had been horribly ripped off.

Don’t worry though if it happened in Australia you have the ASX and ASIC to look after you.

My opinion only DYOR
Fact Finder

The savings in time for Law Enforcement viewing event based CCTV footage would be huge. You only need to know if an event takes place.
Pics etc are clearer which also is a great plus.
All security applications would benefit greatly.
Military applications for sure. Latency savings of AKIDA is like being quicker on the draw.

 

[Fullmoonfever]

I see on Git that they've been doing some upgrades, updates & bug fixes in Dec.

Appears a fair bit of the work revolved around AkD1500 and V2...nice.



Dec 13, 2023

ktsiknos-brainchip
2.6.0-doc-1
363c1f2
Upgrade to QuantizeML 0.7.3, Akida/CNN2SNN 2.6.0 and Akida models 1.3.1
Latest

Update QuantizeML to version 0.7.3​

New features​

• Quantization support for ONNX graph
• ONNX quantization 'custom_pattern' feature
• Allow padding value per-channel on first convolution
• Proper support of dilation rate parameter
• Removal of support of activation parameter in layers
• Added a check on last-channel format
• Quantization now supports per-axis offset and scale in Rescaling layer
• Multiple reshape/flatten removal transformation
• Added a pretty print on QuantizationParams class

Bug fixes:​

• Fixed a saturation that could happen when small amount of random samples are used for calibration
• Configuration parameter will not wrongly be updated when used to create a quantized layer
• Silenced a division by zero warning
• Mean and variance are now non-trainable in QuantizedBatchNormalization
• QuantizedDense buffer bitwidth limited to 28
• FixedPoint.quantize no longer build incoherent frac_bits tensor and will default to a per-tensor quantization

Update Akida and CNN2SNN to version 2.6.0​

New features​

• [Akida] Added support of SPI driver over FTDI to support AKD1500
• [Akida] Added support for latest version of PCIe driver, focusing on AKD1500 support
• [Akida] Added initial support for Conv2D/CNP programming and inference on Akida V2
• [Akida] Added initial support for Dense1D/FNP3 programming and inference on Akida V2
• [Akida] Added initial support for DepthwiseConv2D/CNP programming and inference on Akida V2
• [Akida] Added initial support for InputConv2D/HRC programming and inference on Akida V2
• [Akida] Implemented per-axis padding value support in InputConv2D
• [Akida] Added support to perform multipass programming from AKD1500 flash
• [Akida] Refactored PoolingCalculatedParams and the way it is built
• [Akida] Dropped HwVersion.Latest support
• [Akida] Updated engine deployed README to latest version
• [CNN2SNN] Integrated support to convert ONNX graphs quantized with QuantizeML
• [CNN2SNN] Raise a warning if input_scaling is used when converting to Akida V2.
• [CNN2SNN] Added __version__ attribute to module.
• [CNN2SNN] Removed input-shape dependency on ONNX graph
• [CNN2SNN] Clarified support for dilation rate argument in Keras layers
• [CNN2SNN] Added model equalization to avoid extreme variable values when converting QuantizeML V1 models
• [CNN2SNN] Refactored check_model_compatibility that now takes a float model as input and checks quantization, conversion and optionally mapping
• [CNN2SNN] Improved the block matching displayed error during conversion

Bug fixes:​

• [Akida] Fixed left shift overflow when programming some models
• [Akida] Fixed on conv2d_map on windows
• [Akida] Fixed DMA-related error when using raspberry PI and AKD1500 via PCIe
• [Akida] Fixed error on program_info retrieval causing crash
• [CNN2SNN] Allowed sequential model conversion
• [CNN2SNN] Reject QuantizeConv2D using the groups parameter with a value different than 1 (unsupported)
• [CNN2SNN] Correctly reject unbounded ReLU and ReLU without OutputQuantizer when converting towards V1

Update Akida models to 1.3.1​

• Updated CNN2SNN minimal required version to 2.6.0 and QuantizeML to 0.7.3

New features​

• Added a version attribute to the package
• Improved helpers import path in all submodules
• Removed default path to datasets in training scripts, they are now mandatory
• Reworked ModelNet40/PointNet++ model so that it is hardware compatible
• Added a ‘quantized’ parameter to pretrained helpers that will fetch float model when set to False
• Removed apply_weights_to_model API that is already available in QuantizeML
• Improved several docstrings
• Improved transfer learning on PlantVillage
• Improved fetch_file management of ConnectionResetError

Bug fixes:​

• Allowed non-square input for ImageNet preprocessing
• Ill-formed URL path for pretrained model on Windows
• Fixed AkidaNet edge and VWW pretrained checksums

 

[cosors]

That's not my cup of tea, but maybe for you. I saw that you were the last who posted this URL. Can you do anything with it? I couldn't find anything here using the keyword search for the Mentium Technologies or sub orbital either.
Maybe it is interesting. I'm closing the tab as this will take until 2026 anyway.

"Testing neuromorphic architectures for high capacity/low-power AI in Sub Orbital flight​

Project Description​

The technology is a radiation-hardened Artificial Intelligence (AI) inference accelerator. The system consists of a co-processor that is able to expand the AI capabilities of existing systems by orders of magnitudes while consuming less than 0.4W of power.

Anticipated Benefits​

Artificial Intelligence systems in space are significantly impacted by hardware limitations such as power, mass, and radiation protection. This technology has the potential to bring world-class efficiency of 50 TOPS/W efficiency, at 0.4W power consumption, revolutionizing onboard data analysis, sensor enhancement, and system autonomy, all with a radiation-hardened design."
https://techport.nasa.gov/view/155249



View attachment 57751
But then it will probably come from Synopsys? I have no clue. That's really not my field. I can't get my head around who is doing what with whom for whom and so on; whomwhom. But ARM? I couldn't find them in Neuromorphia's ecosystem thread, which she has thankfully just updated. So I'll leave it here before I close the dab.

View attachment 57752

Addendum
They have developed their own chip. That's how I understand it in a quick glance. They just won the SBIR-STTR award and got $5M for it. So it seems to be more of a competitor.

Award last edited on: 5/11/2023

Low-Power, ultra-Fast Deep Learning Neuromorphic Chip for Unmanned Aircraft Systems​

The chip will use 1/100th of the energy while reaching 100x in speed compared to state of the art. The team already had demonstrated 1000x and 1/1000th energy consumption in a smaller scale experimental demo. From this experience UCSB has a patented technology licensed by Mentium Technologies Inc. thanks to this technology and its develpment within this project, the Neuromorphic Chip will empower the UAS with Cognitive functions enabling autonomous guidance, decision making and complex image processing, while keeping the power consumption low.

more to find here:

NASA 2017 Low-power, ultra-fast deep learning neuromorphic chip for unmanned aircraft systems | www.inknowvation.com

www.inknowvation.com

 

[7für7]

This weekend feels like a whole week 🫠

 

[Fact Finder]

Addendum
They have developed their own chip. That's how I understand it in a quick glance. They just won the SBIR-STTR award and got $5M for it. So it seems to be more of a competitor.

Low-Power, ultra-Fast Deep Learning Neuromorphic Chip for Unmanned Aircraft Systems​

The chip will use 1/100th of the energy while reaching 100x in speed compared to state of the art. The team already had demonstrated 1000x and 1/1000th energy consumption in a smaller scale experimental demo. From this experience UCSB has a patented technology licensed by Mentium Technologies Inc. thanks to this technology and its develpment within this project, the Neuromorphic Chip will empower the UAS with Cognitive functions enabling autonomous guidance, decision making and complex image processing, while keeping the power consumption low.

more to find here:

NASA 2017 Low-power, ultra-fast deep learning neuromorphic chip for unmanned aircraft systems | www.inknowvation.com

www.inknowvation.com

They sound pretty good I wonder why with such amazing technology already in their hands NASA commenced trialling Loihi 1 in 2019 and then paid to become a Brainchip EAP on 23.12.20 and remain engaged with both Brainchip and Intel in 2024.

My opinion only DYOR
Fact Finder

 

[Guzzi62]

Mentium Tech:
Their homepage only gives very limited info, 4 partners, NASA the most prominent but Synopsys is a very big company in silicon & IP.

I don't know what to think of them, over my pay grade.

Home - Mentium Tech

EVOLVING EDGE AI FOR MISSION-CRITICAL APPLICATIONS WHY A long time ago, to move faster and to help relieve themselves from heavy jobs, Humans invented the wheel. In the last decade, we have developed AI to complement our brains, to go further, and open new horizons. And yet, to conquer the most...

mentium.tech

 

[Diogenese]

Hi SG,

From the look of this patent application which is your third patent, as of March 2020, Nintendo thought NNs were software:

US2021304355A1
SYSTEMS AND METHODS FOR MACHINE LEARNED IMAGE CONVERSION 20200325
View attachment 57813

[0044] Returning to FIG. 1, game device 100 stores and executes a video game application program 108 . Included in the video game application program are a game engine 110 and a neural network 112.


This does not exclude the possibility that Nintendo has adopted Akida after 2020, but it certainly does not support that idea.

Addendum
They have developed their own chip. That's how I understand it in a quick glance. They just won the SBIR-STTR award and got $5M for it. So it seems to be more of a competitor.

Award last edited on: 5/11/2023

Low-Power, ultra-Fast Deep Learning Neuromorphic Chip for Unmanned Aircraft Systems​

The chip will use 1/100th of the energy while reaching 100x in speed compared to state of the art. The team already had demonstrated 1000x and 1/1000th energy consumption in a smaller scale experimental demo. From this experience UCSB has a patented technology licensed by Mentium Technologies Inc. thanks to this technology and its develpment within this project, the Neuromorphic Chip will empower the UAS with Cognitive functions enabling autonomous guidance, decision making and complex image processing, while keeping the power consumption low.

more to find here:

NASA 2017 Low-power, ultra-fast deep learning neuromorphic chip for unmanned aircraft systems | www.inknowvation.com

www.inknowvation.com

Hi cosors,

Mentium has licenced an analog neuromorphic chip from UC-SB.


UNIV CALIFORNIA

US2023124085A1 HYBRID TRANSISTOR AND MEMORY CELL 20211014

A hybrid switch and memory cell includes a transistor device that has an atomically-thin semiconductor material channel, source/drain electrodes, and gate dielectric. The cell includes a resistive-random-access-memory having a thin conductive edge and a 2D insulator layer over the thin conductive edge, wherein the 2D insulator layer extends over the semiconductor channel and serves as the gate dielectric in the transistor device.


WO2022119631A1 NEURAL NETWORK SYSTEM WITH NEURONS INCLUDING CHARGE-TRAP TRANSISTORS AND NEURAL INTEGRATORS AND METHODS 20201202

Present implementations can include a system with a transistor array including a plurality of charge-trap transistors, the charge-trap transistors being operatively coupled with corresponding input nodes, and a neural integrator including a first integrator node and a second integrator node operatively coupled with the transistor array, and generating an output corresponding to a neuron of a neural network system. Present implementations can include a neural integrator with a first integrator node operatively coupled with a first charge-trap transistor of a transistor array, a second integrator node operatively coupled with a second charge-trap transistor of the transistor array, the second charge-trap transistor being operatively coupled with the first charge-trap transistor, and a capacitor operatively coupled with the first integrator node and the second integrator node, and operable to generate an output based on a first voltage at the first integrator node and a second voltage at the second integrator node.

 

[Diogenese]

They sound pretty good I wonder why with such amazing technology already in their hands NASA commencing trialling Loihi 1 in 2019 and then paid to become a Brainchip EAP on 23.12.20 and remain engaged with both Brainchip and Intel in 2024.

My opinion only DYOR
Fact Finder

This is interesting - NASA RadNeuro report for 2021:

https://ntrs.nasa.gov/api/citations/20210021885/downloads/GCD%20APR%20-%20RadNeuroSept2021AmesReview.pptx.pdf

https://ntrs.nasa.gov/api/citations...GCD APR - RadNeuroSept2021AmesReview.pptx.pdf

Slide 12

Slide 15

 

[Fullmoonfever]

Would appear our friends over at Quantum Ventura finally got their hands on an in the wild AKD1000 chip for CyberNeuroRT

As they note they were running originally on Akida simulation and now with the chip are getting better results as expected.

I see they were using datasets from UNSW and this second round was much more challenging as well.

Original one page summary HERE

 

[charles2]

The answer is Brainchip is not trying to change the world.

The answer is compatibility as a result of incorporating CNN and CNN2SNN conversion Brainchip allows the whole World to adopt AKIDA technology into their existing platforms without changing or throwing out their existing technology or models.

By adding AKIDA technology suddenly the World can off load Ai inference at the Edge to AKIDA and only deal with the relevant events dramatically reducing needed bandwidth by only sending valuable relevant meta data to the Cloud where it can be processed further, action taken and then stored more economically and efficiently ready for use.

The best real world example is a security camera monitoring a commuter carpark.

A normal security camera connected to the cloud can have its frame rate slowed down to reduce power to say 10 fps yet even though it is slowed down it is still every second sending ten frames or photographs of the carpark with nothing happening to the cloud needing the same bandwidth every second, every minute, every hour.

This means if my maths is correct 36,000 images of nothing happening is being sent to the cloud for processing to find and report 36,000 times nothing happening every hour on the hour 24 hours a day.

This results in masses of probably useless data being stored in case it is later needed.

Enter AKIDA technology because it is compatible with every sensor and every process the carpark owner can slot it in between the camera and the existing processor and it can monitor the 10 fps for events and only when an event occurs in a frame does it pass this frame/ photo on to the processor as meta data to send on to the cloud for further processing.

What this allows is the camera to run at higher frame rates (Nviso runs at over 1,000 fps with AKIDA) and resolution (even colour) while still dramatically lowering energy consumption and with dramatically reduced bandwidth and cloud storage saving running costs at every stage while reducing latency and improving accuracy.

The reality of what Brainchip offers is that it makes the existing technology magnitudes better and does not require all existing technology to be made redundant and thrown on the scrap heap before it can be adopted.

Beyond this Brainchip AKIDA technology offers the option to start from scratch and build out whole systems based on its technology so that as existing technology ages and has to be replaced it can step in and immediately or over time completely replace that which went before.

This is why you can read on the ARM website that AKIDA dramatically improves the efficiency of ARM processes.

My opinion only DYOR
Fact Finder

If you choose to bookmark just one post to remind yourself why you bought Brainchip and perhaps why you are contemplating buying more, this is it. Friends and relatives that you have influenced to buy shares (who may have eliminated you from their Xmas card/gift list....wives and ex-wives and future ex-wives included).....send them a copy. A get out of the doghouse free pass.

And not emphasized were the security benefits of having far less proprietary/personal data sloshing around in the cloud. The carbon footprint is reduced while patents continue to lengthen and strengthen the AKIDA advantage. I like the sound of that...AKIDA ADVANTAGE.

As an aside...ugh... I woke up to this FF post and felt slightly belittled as I sold 3.5% of my BRCHF yesterday after this very riveting rocket launch. Surely one can understand having a little spare change to invest/play with other "compelling" ideas....yes, to rationalize is to be alive.....

Prediction: It is likely to be an extended period before I cut loose another 3.5% especially if FF (and others) continue to remind me/us why we are invested.

Cheers for the AKIDA ADVANTAGE

charles2

 

[charles2]

Gives quantitative meaning to the phrase "take it in the shorts"

Nvidia Stock Surge Causes $3 Billion Loss for Short Sellers

(Bloomberg) -- The surge in Nvidia Corp. shares on Thursday has left short sellers with about $3 billion in paper losses, according to an analysis by S3 Partners LLC, which called it an “AI generated nightmare” for bearish traders.Most Read from BloombergUS Unveils Biggest Sanctions Package on...

finance.yahoo.com

 

[IloveLamp]

Check out this job at Tenstorrent: https://www.linkedin.com/jobs/view/3836122072

 

[IloveLamp]

Simplify Your AI Journey – Intel

Deliver AI at scale across cloud, data center, edge, and client with comprehensive hardware and software solutions.

intel.ly

 

[IloveLamp]

For faster emergency response, New York’s fire department looks to AI

The New York City Fire Department (FDNY) is tapping technology from the smart transportation-focused C2SMARTER research center to speed up emergency

bit.ly

 

[Frangipani]

Adam Osseiran will be one of the presenters at the upcoming AI Innovation Summit Australia-Poland in Perth​

​

AI Innovation Summit Australia-Poland

Showcasing Poland & Australia's AI Achievements - Independent Innovations, Converging Visions for Tomorrow!

www.eventbrite.com.au

​

​

AI Innovation Summit: Australia-Poland​

Get ready to embark on an international AI journey at the AI Innovation Summit: Australia-Poland. We are excited to bring an international dimension to this summit, bridging connections between AI enthusiasts, professionals, and experts from both Australia and Poland. This complimentary public event is your gateway to exploring the unique AI developments emerging from these countries, promoting an exchange of knowledge and showcasing the forefront of AI innovations. Look forward to dynamic discussions, invaluable networking opportunities, and insights from leading figures in the industry. Moreover, we will delve into the ethical and societal impacts of AI across different cultural landscapes. Join us in celebrating the synergy of AI innovation from the Land Down Under and the heart of Central Europe.

​

Our MC/Moderator:​

​

Dr. Andrzej Gwizdalski, WAWEB3 Co-founder and Managing Director, Researcher and Lecturer at UWA​

Dr. Andrzej Gwizdalski, a distinguished Senior Fellow of the Higher Education Academy, co-founder of the Western Australia Web3 Association, and the orchestrator behind the International WAWEB3 Conference. An esteemed university educator and researcher, Dr. Gwizdalski has garnered multiple awards for his work examining digital transformation's effects on society, economy, employment, and culture. With a rich, interdisciplinary expertise spanning digital blockchain technologies, business, economics, and anthropology, he pioneered WA’s inaugural Master's Level Blockchain in Business Unit at UWA. His commitment to the WEB3 ecosystem engages governments, industry, startups and academia to harness blockchain, AI, and IoT for a dynamic digital economy in WA and beyond.

Our Presenters:​

​

Paul Bitdorf, Executive Chairman, Nicheliving; Honorary Consul of Poland in Perth​

Paul Bitdorf stands as a distinguished figure in both the business and diplomatic landscapes, serving as the Executive Chairman of Nicheliving, a forefront innovator in affordable housing solutions in Australia, and as the Honorary Consul of Poland in Perth, Australia. His dual roles exemplify his dedication to fostering strong economic ties and cultural exchanges between Australia and Poland. Paul's leadership at Nicheliving has been pivotal in driving the company's growth and success, leveraging his vast experience to navigate the complex intersections of real estate development and international diplomacy. His work as Honorary Consul underscores his commitment to enhancing bilateral relationships, offering a bridge for collaboration and mutual understanding between the two nations.

Tym Pieglowski, President at Polish Australian Business Forum (PABF)​

Tym Pieglowski is an advisor and entrepreneur, passionate about simple solutions to complex problems. He has expertiese in public and private sector clients on transport infrastructure solutions. He is a civil engineer specialising in travel demand forecasting for highway, public transport, freight and aviation. Untill recently Tim was a Director of the PwC Financial Advisory team. Prior to joining PwC, Tym was the founding Director of TMK Consult providing project management, transport planning and travel demand forecasting services for the NSW Government transport infrastructure projects. He has over 15 years of experience working in Australia, the UK and Poland.

Professor Piotr Skrzypczyński, Deputy Leader - Robotics and Artificial Intelligence, Centre for Artificial Intelligence and Cybersecurity, Poznań University of Technology in Poland​

Professor Piotr Skrzypczyński serves as the Deputy Leader of Robotics and Artificial Intelligence at the Centre for Artificial Intelligence and Cybersecurity, Poznań University of Technology, Poland. With a distinguished academic and research career, Skrzypczyński has contributed significantly to the fields of robotics and computer science, authoring over 160 technical papers. His expertise spans AI-based robotics, robot navigation, SLAM (Simultaneous Localization and Mapping), computer vision, and machine learning. Holding a Ph.D. and D.Sc. in robotics from the Poznań University of Technology, his work is pivotal in advancing the understanding and application of AI and robotics. Skrzypczyński's leadership at the Institute of Robotics and Machine Intelligence underscores his commitment to driving innovation and excellence in AI research and development.

Dr. Ryszard Kowalczyk, Chair in Artificial Intelligence at SmartSat CRC at the University of South Australia​

Dr. Richard Kowalczyk holds the esteemed position of SmartSat CRC Chair in Artificial Intelligence at the University of South Australia in Adelaide. With a prolific background that includes serving as the Director of Swinburne Key Lab for Intelligent Software Systems and leading the Distributed AI Systems Research Group, as well as being the inaugural Wipro Chair of Artificial Intelligence at Swinburne University of Technology in Melbourne, his contributions to AI are profound. Before his current roles, he spearheaded AI research at CSIRO and various international R&D centers. Professor Kowalczyk's research spans AI, autonomous software agents, and collective intelligence, focusing on their integration into cyber-physical-social ecosystems for enhanced decision-making. His work facilitates advancements in smart cities, industry 4.0, and, notably, AI applications for space systems autonomy and resilience.

​

Bartosz Ziolko, AI Expert specialising in Automatic Speech Recognition (ASR) and Natural Language Processing (NLP) in Poland​

Dr. Bartosz Ziółko is a distinguished computer scientist, entrepreneur, and an expert in Automatic Speech Recognition (ASR) and Natural Language Processing (NLP). He served as a professor at the University of Science and Technology (AGH) in Krakow, and was a co-founder and CEO of Techmo. Ziółko completed his education in Krakow, earning his doctorate from the University of York. He achieved his habilitation in 2017, focusing on speech and natural language processing methods. He specialises in speech processing and AI. Bartosz has authored over 100 scientific papers, holds patents in the U.S. and Europe, and has led projects that developed Polish speech recognition systems. He was the organiser of the XXII Annual Pacific Voice Conference and has collaborated with Hokkaido University as a National Institute of Information and Communications Technology (NICT) fellow from December 2020 till end of 2021.

Dr. Fedja Hadzic, Chief Scientist and Managing Director at September AI Labs, in Perth.​

Dr. Fedja Hadzic, is the Chief Scientist and Managing Director at September AI Labs. He has been at the forefront of machine learning and data science innovation since the company's inception in September 2018. Under his leadership, September AI Labs has developed groundbreaking medical applications in partnership with local universities and delivered novel AI solutions that have significantly automated and enhanced critical business processes across various industries. With a career that began in 2002, developing simulations for AI and cognitive science, Fedja's passion for machine learning was further ignited by his involvement with the September AI Labs in September 2018. His academic contributions include over 50 peer-reviewed publications and a pioneering book on data mining with complex structures published by Springer.

Dr. Adam Osseiran, Chairperson of the Scientific Advisory Board at BrainChip Holdings
Dr. Adam Osseiran is a pivotal figure in neuromorphic computing. He is the Chairperson of the Scientific Advisory Board at BrainChip Holdings. Adam is an Adjunct Professor at the University of Western Australia. Dr. Osseiran is a past and current supervisor of several neuromorphic PhD students using BrainChip’s very advanced neuromorphic Edge technology, that learn autonomously, enabling the development of energy-efficient, intelligent systems in industrial applications such as olfaction, gustation, tactile and other ag-tech technologies. Besides his role at BrainChip, Dr. Osseiran is President of the Hydrogen Society of Australia and Co-Founder of Innovate Australia, founder and technical advisor of several advanced AI technology start-ups, further showcasing his commitment to innovation.

The presentations will be followed by a panel discussion moderated by MC/Moderator Dr Andrzej Gwizdalski

Summit Highlights:​

This summit aims to add an international flair, creating a bridge between AI aficionados, professionals, and thought leaders from both Australia and Poland. As a free event open to the public, it serves as a portal to discover the distinctive AI advancements from these nations, fostering knowledge sharing and highlighting cutting-edge AI breakthroughs. Anticipate engaging conversations, priceless networking chances, and wisdom from industry pioneers. Additionally, the summit will address the ethical and societal implications of AI within varied cultural contexts. Embrace the fusion of AI ingenuity from Australia and Poland's vibrant centers.

 

[Frank Zappa]

Hi Cgc

One of the things to keep in mind with Brainchip is that it’s AKIDA technology is about much, much more than just the glamorous and sexy autonomous driving end of the technology market.

If you personally are a newbie you may have missed this partnership:

“BrainChip’s Neuromorphic Technology Enables Intellisense Systems to Address Needs for Next-Generation Cognitive Radio Solutions​

Laguna Hills, Calif. – March 21, 2023 – BrainChip Holdings Ltd(ASX: BRN, OTCQX: BRCHF, ADR: BCHPY), the world’s first commercial producer of ultra-low power, fully digital, event-based, neuromorphic AI IP, today announced that Intellisense Systems Inc. has selected its neuromorphic technology to improve the cognitive communication capabilities on size, weight and power (SWaP) constrained platforms (such as spacecraft and robotics) for commercial and government markets.

Intellisense’s intelligent radio frequency (RF) system solutions enable wireless devices and platforms to sense and learn the characteristics of the communications environment in real time, providing enhanced communication quality, reliability and security. By integrating BrainChip’s Akida™ neuromorphic processor, Intellisense can deliver even more advanced, yet energy efficient, cognitive capabilities to its RF system solutions.

One such project is the development of a new Neuromorphic Enhanced Cognitive Radio (NECR) device to enable autonomous space operations on platforms constrained by size, weight and power (SWaP). Intellisense’s NECR technology provides NASA numerous applications and can be used to enhance the robustness and reliability of space communication and networking, especially cognitive radio devices. Smart sensing algorithms will be implemented on neuromorphic computing hardware, including Akida, and then integrated with radio frequency modules as part of a Phase II prototype.

“We are excited to partner with BrainChip and leverage their state-of-the-art neuromorphic technology,” said Frank T. Willis, President and CEO of Intellisense. “By integrating BrainChip’s Akida processor into our cognitive radio solutions, we will be able to provide our customers with an unparalleled level of performance, adaptability and reliability.”

BrainChip’s Akida processor is a revolutionary computing architecture that is designed to process neural networks and machine learning algorithms at ultra-low power consumption, making it ideal for edge computing applications. By utilizing this cutting-edge technology, Intellisense will be able to deliver cognitive radio solutions that are faster, more efficient and more reliable than ever before.

“Intellisense provides advanced sensing and display solutions and we are thrilled to be partnering with them to deliver the next generation of cognitive radio capabilities,” said Sean Hehir, CEO of BrainChip. “Our Akida processor is uniquely suited to address the demanding requirements of cognitive radio applications and we look forward to continue partnering with Intellisense to deliver cutting-edge embedded processing with AI on-chip to their customers.”

In my opinion Cognitive Communications has huge potential.

Just think about going for a drive and you see standing in the middle of the road a policeman.

You obey his signal to stop. He walks up to your car window and advises you that a bushfire has closed the road 40 kilometres ahead and tells you that you need to use the alternate route to get to your destination.

In today’s world the Police Officer is often replaced by traffic Apps that tell you the best route to take.

This is what Cognitive Communications are all about. Choosing the best route for digital communications to be routed so that too many messages do not go the same way causing congestion or log jams.

In space radio communications are routed to Earth via satellites and at each satellite AKIDA will allow decisions to be made as to which is the best route to take.

On Earth you will know about 3G, 4G and 5G networks. You may even have heard they are working on another upgrade a 6G network.

In this technology age as more and more devices are made needing connection to the Cloud networks are quickly reaching capacity.

A way to increase the capacity of existing networks is to apply intelligence at every node and make smart decisions about which way to send a given communication to its destination so as to avoid contesting bandwidth.

There are millions and millions of these nodes that will need to be made smart to create cognitive communications.

But to do this the solution will need to be smart and run at ridiculously low power capable but still capable of real time decision making at very low cost.

AKIDA technology stands head and shoulders above all other technology when you factor size, power and cost into the equation.

It is predicted that in the not to distant future 75% of all data will be created at the Edge.

Even if AKIDA is adopted by say 30% of all these Edge applications the other 70% will still be there trying to send this collected date over networks to have it processed and stored in the cloud.

This is where even if it is not adopted at the Edge AKIDA will be needed to make networks as smart or cognitive as possible to handle the avalanche of demand that is going to crush existing and future networks.

Not as sexy as autonomous Tesla vehicles but still a massive market.

My opinion only DYOR
Fact Finder

Brainchip now has over 12,000 followers on LinkedIn.

 

[Fact Finder]

Hi cosors,

Mentium has licenced an analog neuromorphic chip from US-SB.


UNIV CALIFORNIA

US2023124085A1 HYBRID TRANSISTOR AND MEMORY CELL 20211014

A hybrid switch and memory cell includes a transistor device that has an atomically-thin semiconductor material channel, source/drain electrodes, and gate dielectric. The cell includes a resistive-random-access-memory having a thin conductive edge and a 2D insulator layer over the thin conductive edge, wherein the 2D insulator layer extends over the semiconductor channel and serves as the gate dielectric in the transistor device.


WO2022119631A1 NEURAL NETWORK SYSTEM WITH NEURONS INCLUDING CHARGE-TRAP TRANSISTORS AND NEURAL INTEGRATORS AND METHODS 20201202

Present implementations can include a system with a transistor array including a plurality of charge-trap transistors, the charge-trap transistors being operatively coupled with corresponding input nodes, and a neural integrator including a first integrator node and a second integrator node operatively coupled with the transistor array, and generating an output corresponding to a neuron of a neural network system. Present implementations can include a neural integrator with a first integrator node operatively coupled with a first charge-trap transistor of a transistor array, a second integrator node operatively coupled with a second charge-trap transistor of the transistor array, the second charge-trap transistor being operatively coupled with the first charge-trap transistor, and a capacitor operatively coupled with the first integrator node and the second integrator node, and operable to generate an output based on a first voltage at the first integrator node and a second voltage at the second integrator node.

I wonder when they will start shouting they have helped one of the USA DARPA approved foundries IFS & Global Foundries to overcome the production variabilities that plague analogue and that stopped Rain Ai in its tracks and forced Innatera to go tiny rather than scaling up to AKIDA’s digital heights?

I wonder when they will announce that like AKIDA technology they were found compatible with the NASA approved Vorago Hardsil process for hostile deep space missions?

I wonder when they will prove out compatibility with all major foundry process like digital AKIDA technology?

I wonder when they will announce what it costs to implement a new production method that removes the variability issue, what yields they achieve over what nodes and what the increased costs of this production method will be against standard digital production costs used for AKIDA?

Quite a few questions to answer before I will actually start to worry.

My opinion only DYOR
Fact Finder

 

[cosors]

https://www.reuters.com/business/autos-transportation/mercedes-benz-hits-cars-returns-forecast-inflation-supply-chain-costs-bite-2024-02-22/

MB has delivered (very) good figures and at the same time announced that, contrary to previous plans, it will continue to produce ICEs beyond 2030.

___
It's just stupid that they have already sold their combustion engine division to China, as far as I know

 

[cosors]

Let's not forget it was Bernie Madoff who supported the introduction of computer trading on the NYSE.

The one who virtually invented the NASDAQ, the greatest financial criminal of all time, who made his money with dealing chocolate in prison ... alongside his (criminal) genius? May he rest in peace too. Completely value-neutral. I appreciate his work for computer-based trading and other regulations, but damn.

 

[Stable Genius]

Hi SG,

From the look of this patent application which is your third patent, as of March 2020, Nintendo thought NNs were software:

US2021304355A1
SYSTEMS AND METHODS FOR MACHINE LEARNED IMAGE CONVERSION 20200325
View attachment 57813

[0044] Returning to FIG. 1, game device 100 stores and executes a video game application program 108 . Included in the video game application program are a game engine 110 and a neural network 112.


This does not exclude the possibility that Nintendo has adopted Akida after 2020, but it certainly does not support that idea.

Thanks @Diogenese

We’re very fortunate to have you kindly sharing your knowledge and expertise on this forum.

Your patience, humour and intelligent commentary is very much appreciated.

Cheers