Humble Genius
Regular
Nothing has changed? Check the sp
Yep, nothing has changed other than the people who shit themselves yesterday and sold their shares (and now wishing they didn't)
BRN Discussion Ongoing
- Thread starter TechGirl
- Start date
Yep, nothing has changed other than the people who shit themselves yesterday and sold their shares (and now wishing they didn't)
Xhosa12345
Regular
LOL or the opposite.....
Bravo
If ARM was an arm, BRN would be its biceps💪!
Arm's bigger brethren? Founder of Softbank seeks $100bn war chest to build AI chip behemoth to rival Nvidia, Intel — but is it too little, too late?
By Wayne Williams
published 2 days ago
Project Izanagi will focus on artificial general intelligence
(Image credit: Shutterstock / glen photo)
The founder of Softbank Group has reportedly set his sights on a new venture: a $100 billion AI chip company named Project Izanagi which could one day rival the dominance of AI leaders such as Nvidia, Intel and AMD.
According to Bloomberg, Masayoshi Son says $30 billion of the required funding for Project Izanagi, which will focus on artificial general intelligence (AGI), is expected to come from Softbank, with the remaining $70 billion potentially sourced from Middle Eastern investors. However, the specifics of the funding and execution are still under wraps, and the project may undergo further evolution.
Project Izanagi is envisioned to work in synergy with Arm, a chip design business in which Softbank holds a 90% stake. However, exactly how the two businesses will interact remains a mystery for now.
An impossible dream?
Despite facing various setbacks in his startup investments, Son's fervor for AGI is palpable. He was quoted by Bloomberg as saying, “AGI is what every AI expert is after. But when you ask them about a detailed definition, a number, the timing, how much computing power, how much smarter AGI is than the human intelligence, most of them don’t have an answer. I have my own answer: I am convinced AGI will be real in 10 years.”
The task of building a company to compete successfully against Nvidia is a formidable one. Nvidia boasts a wealth of talented hardware engineers, highly competitive hardware, and a ubiquitous CUDA software stack, which has been evolving for over 16 years.
It makes sense for Son's Project Izanagi's AI processors to employ technologies such as instruction set architecture (ISA) developed by Arm to give it a head start, but the new venture may also choose to look elsewhere. Softbank is one of the companies rumored to be interested in buying cash-strapped UK chip designer Graphcore.
Arm's bigger brethren? Founder of Softbank seeks $100bn war chest to build AI chip behemoth to rival Nvidia, Intel — but is it too little, too late?
Project Izanagi will focus on artificial general intelligencewww.techradar.com
Softbank's Founder Masayoshi Son says "I am convinced AGI will be real in 10 years.”
I reckon Son must have been talking to Peter Van Der Made because I remember there was a podcast (I believe it was in or around February 2023) in which Peter said that AKIDA 10, which is due in 2030 will be the “Holy Grail” of general artificial intelligence.
MDhere
Top 20
Nice average. U just need 400 more to get it to a pretty figureCould not help myself, although I didn't get them at 30 cents, I am still happy.
And just for people that think I am full of sh#t, this is what I have brought in
the last month, ( Ok I am in debt ) but just showing because this is how much.
confidence I have in the Company and I am ready to buy more if the opportunity
arises.
As I said before, it is going to make me or break me, only time will tell.
View attachment 58033
Terroni2105
Founding Member
Yesterdays investor presentation is now available here:
Afternoon Cassip & Diogenese ,
Just a quick thankyou for the above ...... and a favour if i may.
Having logged onto their site ...cant seem to get the english translation.
If any savey Chipper is fluent in german.........
1, The 7.09% of their holding ( Biggest in their portfolio ) ....... HOW MANY SHARES DO THAY CONTROL.
The reason being ....... Should they have been included in the latest top 20 holders & more so are they potentially holding over 5% of the floated stock ....... and hence is a substantial holder notice warranted to the market ???
Note: my understanding is this fund has 500 million euro's to play with
500,000,000.00 divided by 100 = 5 million Euros (1 percent )
5,000,000.00 euros X 7.09 % ( BrainChip Holding in their fund assuming the fund is fully invested with no cash )
=35,450,000.00 million euros divided by say 0.30 euro per BRN stock bought would mean they potentially hold well over 5% of floated stock.
If their holding is over 83,050,000 BRN units ( Give or take a couple ) = SUBSTANTIAL HOLDER
Thankyou in advance.
Regards,
Esq.
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DingoBorat
Slim
I agree, but maybe they will offer exclusivity, in certain sectors?..
For example an exclusive deal with Apple, on mobile and Edge devices (phones, watches, rings, etc).
But would that make shareholders happy, if such a deal, cut out say Samsung products in the same area (although things like Smart Home, could still be available).
Something like that, could lead to a bit of a royalties bidding war, if 2 such Company's, both wanted the "AKIDA" advantage.
Is AKIDA that desirable?
Sean's comments on the increased seriousness of engagements, suggests it is.
DingoBorat
Slim
"Since there are severe restrictions on heat generation, particularly for embedded devices, both higher performance and lower power consumption are required in AI chips"Renesas Develops New AI Accelerator for Lightweight AI Models and Embedded Processor Technology to Enable Real-Time Processing
ByBusinesswire
February 26, 2024
Results of Operation Verification Using an Embedded AI-MPU Prototype Announced at ISSCC 2024
Renesas Electronics Corporation, a premier supplier of advanced semiconductor solutions, today announced the development of embedded processor technology that enables higher speeds and lower power consumption in microprocessor units (MPUs) that realize advanced vision AI. The newly developed technologies are as follows: (1) A dynamically reconfigurable processor (DRP)-based AI accelerator that efficiently processes lightweight AI models and (2) Heterogeneous architecture technology that enables real-time processing by cooperatively operating processor IPs, such as the CPU. Renesas produced a prototype of an embedded AI-MPU with these technologies and confirmed its high-speed and low-power-consumption operation. It achieved up to 16 times faster processing (130 TOPS) than before the introduction of these new technologies, and world-class power efficiency (up to 23.9 TOPS/W at 0.8 V supply).
Amid the recent spread of robots into factories, logistics, medical services, and stores, there is a growing need for systems that can autonomously run in real time by detecting surroundings using advanced vision AI. Since there are severe restrictions on heat generation, particularly for embedded devices, both higher performance and lower power consumption are required in AI chips. Renesas developed new technologies to meet these requirements and presented these achievements on February 21, at the International Solid-State Circuits Conference 2024 (ISSCC 2024), held between February 18 and 22, 2024 in San Francisco.
The technologies developed by Renesas are as follows:
(1) An AI accelerator that efficiently processes lightweight AI models
As a typical technology for improving AI processing efficiency, pruning is available to omit calculations that do not significantly affect recognition accuracy. However, it is common that calculations that do not affect recognition accuracy randomly exist in AI models. This causes a difference between the parallelism of hardware processing and the randomness of pruning, which makes processing inefficient.
To solve this issue, Renesas optimized its unique DRP-based AI accelerator (DRP-AI) for pruning. By analyzing how pruning pattern characteristics and a pruning method are related to recognition accuracy in typical image recognition AI models (CNN models), we identified the hardware structure of an AI accelerator that can achieve both high recognition accuracy and an efficient pruning rate, and applied it to the DRP-AI design. In addition, software was developed to reduce the weight of AI models optimized for this DRP-AI. This software converts the random pruning model configuration into highly efficient parallel computing, resulting in higher-speed AI processing. In particular, Renesas’ highly flexible pruning support technology (flexible N:M pruning technology), which can dynamically change the number of cycles in response to changes in the local pruning rate in AI models, allows for fine control of the pruning rate according to the power consumption, operating speed, and recognition accuracy required by users.
This technology reduces the number of AI model processing cycles to as little as one-sixteenth of pruning incompatible models and consumes less than one-eighth of the power.
(2) Heterogeneous architecture technology that enables real-time processing for robot control
Robot applications require advanced vision AI processing for recognition of the surrounding environment. Meanwhile, robot motion judgment and control require detailed condition programming in response to changes in the surrounding environment, so CPU-based software processing is more suitable than AI-based processing. The challenge has been that CPUs with current embedded processors are not fully capable of controlling robots in real time. That is why Renesas introduced a dynamically reconfigurable processor (DRP), which handles complex processing, in addition to the CPU and AI accelerator (DRP-AI). This led to the development of heterogeneous architecture technology that enables higher speeds and lower power consumption in AI-MPUs by distributing and parallelizing processes appropriately.
A DRP runs an application while dynamically changing the circuit connection configuration between the arithmetic units inside the chip for each operation clock according to the processing details. Since only the necessary arithmetic circuits operate even for complex processing, lower power consumption and higher speeds are possible. For example, SLAM (Simultaneously Localization and Mapping), one of the typical robot applications, is a complex configuration that requires multiple programming processes for robot position recognition in parallel with environment recognition by vision AI processing. Renesas demonstrated operating this SLAM through instantaneous program switching with the DRP and parallel operation of the AI accelerator and CPU, resulting in about 17 times faster operation speeds and about 12 times higher operating power efficiency than the embedded CPU alone.
Operation Verification
Renesas created a prototype of a test chip with these technologies and confirmed that it achieved the world-class, highest power efficiency of 23.9 TOPS per watt at a normal power voltage of 0.8 V for the AI accelerator and operating power efficiency of 10 TOPS per watt for major AI models. It also proved that AI processing is possible without a fan or heat sink.
Utilizing these results helps solve heat generation due to increased power consumption, which has been one of the challenges associated with the implementation of AI chips in a variety of embedded devices such as service robots and automated guided vehicles. Significantly reducing heat generation will contribute to the spread of automation into various industries, such as the robotics and smart technology markets. These technologies will be applied to Renesas’ RZ/V series—MPUs for vision AI applications.
All things that AKIDA easily solves.
My eyes widened though, when I saw this..
(flexible N:M pruning technology)
Is this a direct reference to Simon's N of M coding??
Hey @Diogenese, what do you think
..
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db1969oz
Regular
You have 5 minutes!
DingoBorat
Slim
Description
Announcing The KnowU™, Know Labs' Wearable Non-Invasive CGM
Announcing the KnowU™, Know Labs' wearable non-invasive continuous glucose monitor (CGM). The KnowU incorporates the sensor the Company plans to submit to the FDA for clearance. This proprietary radio-frequency (RF) dielectric sensor has been tested and proven stable and accurate in a lab setting and is now miniaturized and wearable. The KnowU can be worn with an adhesive, allowing the user to clip the sensor on and off or on the wrist or forearm with a strap. The device, which is significantly smaller and lighter than the prototype, includes on-board computing power and built-in machine learning capabilities. The KnowU is designed to optimize the customer experience – expected to last for years, eliminate costly disposables, have a rechargeable battery, and connect with an easy-to-use companion mobile app.
View attachment 58012
Know Labs, Inc. on LinkedIn: Announcing The KnowU™, Know Labs' Wearable Non-Invasive CGM | 13 comments
Today, we announce the KnowU™, our non-invasive wearable continuous glucose monitor that incorporates the sensor we plan to submit to the FDA for… | 13 comments on LinkedInwww.linkedin.com
I was actually surprised, when the Investor Presentation had that, the Healthcare and Wellness sector, was "only" a 800 million dollar market.
I'd think, it would be much larger than that?
But maybe more burger, face stuffing individuals on the planet, than I thought
..
Pom down under
Top 20
Tothemoon24
Top 20
Spiking Neural Networks 101 | Yadnyesh Chakane
As the dimensions of models, particularly Large Language Models (LLMs), expand over time, so too do their computational requirements, resulting in a subsequent rise in energy costs for their training. One viable solution to mitigate these challenges involves the adoption of biologically inspired...
As the dimensions of models, particularly Large Language Models (LLMs), expand over time, so too do their computational requirements, resulting in a subsequent rise in energy costs for their training. One viable solution to mitigate these challenges involves the adoption of biologically inspired neural networks, specifically Spiking Neural Networks (SNNs). Renowned for their energy efficiency, SNNs can be effectively employed across a spectrum of fields including robotics, neuromorphic hardware, reinforcement learning, and brain-computer interfaces. Below is a post that delves into the intricacies of SNNs.
To know more about SNNs you can check out the following resources:
Training Spiking Neural Networks Using Lessons From Deep Learning - https://lnkd.in/gpsuk9US
S4NN: temporal backpropagation for spiking neural networks with one spike per neuron - https://lnkd.in/gDNGpp2e
#machinelearning #artificialintelligence #llms #research #deeplearning
mkm109
Regular
I was thinking the same.. what has changed that necessitated a more polite approach?
Could they be working on integrating our IP? 
DingoBorat
Slim
I don't think you know what a dead cat bounce is, otherwise you wouldn't be putting a rocket ship after itLooks like a dead cat bouncehopefully a close in the 40s to close the gap at .44
"In finance, a dead cat bounce is a small, brief recovery in the price of a declining stock. Derived from the idea that "even a dead cat will bounce if it falls from a great height""
And anyway I've tried it and they definitely don't bounce.
HopalongPetrovski
I'm Spartacus!
Hey Esqui old friend.
Do we have any jiggery pokery witching hour stuff coming up with the end of Feb leap year thingamabob?
Somebody somewhere mentioned ASX300 rebalancing???
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