BRN Discussion Ongoing

FJ-215

Regular
Isnt that just the license fee Megachips and Renesas paid us?
We don't know the answer to this.

BRN has not wanted to disclose what each license is worth and have managed to disguise this very well, even when pushed by the ASX.

MegaChips for example is a 4 year agreement where the license fee would be paid in tranches over a 2 year period. (from 21/11/21)
Have they paid the full amount or is there more to come?

Have to wait for the Full Year results released Feb 2024
 
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FJ-215

Regular
More like Mick Jagger's 🤣
I might need to revisit some of his books while listening to Mick's music.

Might start with "The trouble with lichen"

Come to think of it, he did do a short story on fungus but they weren't exactly magic mushrooms

Anyway,

Far Away Eyes,

Sounds like a good title for a Wyndham novel, just lacking the human apocalypse.

Also, I just mentioned it because I really like this song......

 
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FJ-215

Regular
I might need to revisit some of his books while listening to Mick's music.

Might start with "The trouble with lichen"

Come to think of it, he did do a short story on fungus but they weren't exactly magic mushrooms

Anyway,

Far Away Eyes,

Sounds like a good title for a Wyndham novel, just lacking the human apocalypse.

Also, I just mentioned it because I really like this song......


LOL,,,,,,

Charlie doesn't look happy.
 
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HopalongPetrovski

I'm Spartacus!
I might need to revisit some of his books while listening to Mick's music.

Might start with "The trouble with lichen"

Come to think of it, he did do a short story on fungus but they weren't exactly magic mushrooms

Anyway,

Far Away Eyes,

Sounds like a good title for a Wyndham novel, just lacking the human apocalypse.

Also, I just mentioned it because I really like this song......


The Kraken Wakes is probably my favourite although Chocky and The Midwich Cuckoos still hold up really well.
One of my all time favourite authors. :)
 
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Boab

I wish I could paint like Vincent
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schuey

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Isnt that just the license fee Megachips and Renesas paid us?
License fees yeah- Megachips & Megachips sub licensees.. In 6 months post them signing up.

$4.8mill USD based on Akida 1000.

Looking at the expanding ecosystem of partners eagerly awaiting the Gen 2 IP suite, what sort of figures for license sales could be achieved before the next AGM?

I wouldn’t be surprised if it was multiples of $4.8mill.

“Highest level of engagement and sales activity”
Or to that effect.

100 employees.. Aggressive all in push to commercialise..

Something FF I’m sure would be pushing..
 
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Tothemoon24

Top 20
The software is described as “an accessory for point of care ultrasound systems”. When paired with a compatible device, the underlying AI neural network can predict the position of the ultrasound probe relative to the heart, and guides the user to manoeuvre the probe in order to capture diagnostic quality cardiac images.


FDA grants clearance for AI-powered cardiac tech from UltraSight​

August 30, 2023 6:00 am



UltraSight has announced that the FDA has granted clearance for its AI-powered ultrasound guidance technology, which is designed to “assist medical professionals without sonography experience in acquiring cardiac ultrasound images at the point of care in multiple settings”, with the aim of supporting detection of heart disease and providing patients with easier access to cardiac monitoring.
The software is described as “an accessory for point of care ultrasound systems”. When paired with a compatible device, the underlying AI neural network can predict the position of the ultrasound probe relative to the heart, and guides the user to manoeuvre the probe in order to capture diagnostic quality cardiac images.
It is hoped that the solution will support patient triage by enabling “increased efficiency and clinical confidence” along with increasing patient access by bringing cardiac ultrasound into local communities.
The FDA’s clearance follows a study from UltraSight which “demonstrated that with real-time guidance of the ultrasound probe and feedback on the quality of the ultrasound image, medical professionals without prior ultrasound experience can acquire diagnostic quality images.”
Roberto Lang, director of cardiovascular imaging at the University of Chicago, says: “UltraSight’s real-time AI guidance is a game-changer for diagnostic efficiency and experience. Now, with regulatory clearance, medical professionals and patients alike can benefit from this transformative cardiac imaging solution.”
 
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TheFunkMachine

seeds have the potential to become trees.
Afternoon FabricatedLunacy ,

My understanding is BrainChip has the PHYSICAL silicone in hand ,......Let the games commence.

Exciting times.

Regards,
Esq.
View attachment 43318
If brainchips road map to vibration analysis market penetration was a picture?
 
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Cgc516

Regular
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FJ-215

Regular
Well, nearly bedtime here in Melbourne,

Been a great day, SP heading in the right direction and more than a few good laughs on The Exchange. Tomorrow morning teases with the promise of a special announcement.

May there be more green on the screen!!
 
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IloveLamp

Top 20

Intel has reported that its new chip, "Sierra Forest," will have over double the efficiency for the same power consumption of other microchips. Designed as a new data center chip, Intel's new double-efficiency chip is scheduled for release sometime in 2024, Reuters report.
 
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stockduck

Regular
What is about that news....

no financial advice and always do your own reseach because its only a layman wording...!

Could someone leave a comment who is in the know....;)

What is Privat AI from VMware, and how does it run? Is it only software or also specialized hardware needed?





New VMware Private AI Foundation With NVIDIA Enables Enterprises to Ready Their Businesses for Generative AI; Platform to Further Support Data Privacy, Security and Control

August 22, 2023

.....


  • Privacy — Will enable customers to easily run AI services adjacent to wherever they have data with an architecture that preserves data privacy and enables secure access.
  • Choice — Enterprises will have a wide choice in where to build and run their models — from NVIDIA NeMo™ to Llama 2 and beyond — including leading OEM hardware configurations and, in the future, on public cloud and service provider offerings.
  • Performance — Running on NVIDIA accelerated infrastructure will deliver performance equal to and even exceeding bare metal in some use cases, as proven in recent industry benchmarks.
  • Data-Center Scale — GPU scaling optimizations in virtualized environments will enable AI workloads to scale across up to 16 vGPUs/GPUs in a single virtual machine and across multiple nodes to speed generative AI model fine-tuning and deployment.
  • Lower Cost — Will maximize usage of all compute resources across, GPUs, DPUs and CPUs to lower overall costs, and create a pooled resource environment that can be shared efficiently across teams.
  • Accelerated Storage — VMware vSAN Express Storage Architecture will provide performance-optimized NVMe storage and supports GPUDirect® storage over RDMA, allowing for direct I/O transfer from storage to GPUs without CPU involvement.
  • Accelerated Networking — Deep integration between vSphere and NVIDIA NVSwitch™ technology will further enable multi-GPU models to execute without inter-GPU bottlenecks.
  • Rapid Deployment and Time to Value — vSphere Deep Learning VM images and image repository will enable fast prototyping capabilities by offering a stable turnkey solution image that includes frameworks and performance-optimized libraries pre-installed.

    ......

  • Availability

    VMware intends to release VMware Private AI Foundation with NVIDIA in early 2024.


PS.:
🤔
 
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Nice little acknowledgement of Akida innovation with IBM and Intel.


Why We Need to Re-Engineer AI to Work Like the Brain to Save on Energy​

tehseen_zia-42x42.jpg

Dr. Tehseen Zia

Editor

Last updated: 17 August, 2023

NEUROMORPHIC COMPUTING: REDEFINING AI FOR A SUSTAINABLE FUTURE

Recent AI progress poses energy challenges. Balancing advancements with sustainability is crucial. Traditional computing faces limitations, while the brain's efficiency inspires Neuromorphic Computing (NC) for energy-efficient AI. Leading companies drive innovative NC technologies, reflecting the fast-growing demand for energy-saving AI.

A digitized human brain.


As artificial intelligence (AI) progresses, its accomplishments bring energy-intensive challenges.

One study predicts that if data growth persists, cumulative energy usage from binary operations could surpass 10^27 Joules by 2040 – more than what the world can generate.

So let’s explore AI’s environmental impact, the constraints of conventional computing models, and how Neuromorphic Computing (NC) draws inspiration from the energy-efficient human brain, leading to sustainable AI advancements.

Artificial Intelligence: The Dilemma​

In recent years, artificial intelligence (AI) has achieved remarkable landmarks, exemplified by the evolution of language models like ChatGPT and advancements in computer vision that empower autonomous technology and elevate medical imaging.

Moreover, AI’s astonishing proficiency in reinforcement learning, as seen in its victories over human champions in games like Chess and Go, highlights its remarkable capabilities.

While these developments have enabled AI to transform industries, foster business innovation, uncover scientific breakthroughs and make a strong impression on society, they are not without consequences.

Alongside that alarming forecast for 2040, even today the storage of extensive data and training AI models using these datasets requires significant energy and computational resources, with research showing that:
Hence, it becomes vital to achieve a balance between advancements and energy requirements, considering their environmental effects, as AI continues to develop.

Von Neumann architecture: The Bottleneck​

AI models operate within the framework of Von Neumann architecture, a computer design that essentially separates processing and memory, requiring constant communication between them.

As AI models grow complex and datasets expand, this architecture faces significant hurdles.

Firstly, the processing and memory units shared a communication bus, slowing AI computations and hampering training speed.

Secondly, the processing unit of the architecture lacks parallel processing capabilities which impacts the training.
While GPUs alleviate the issue by allowing parallel processing, they introduce data transfer overhead.

The frequent data movement faces additional overhead due to memory hierarchy which impacts the performance.

Large datasets cause extended memory access times, and limited memory bandwidth, resulting in performance bottlenecks.

Complex AI models strain Von Neumann systems, limiting memory and processing capacities. These limitations have given rise to high energy demands and carbon emissions in AI systems.

Addressing these challenges is crucial for optimizing AI performance and minimizing environmental impact.

Biological Brain: The Inspiration​

The human brain is more powerful than any AI machines when it comes to cognitive abilities.

Despite its immense power, the brain is incredibly light and operates on just 10W of power, in contrast to the energy-hungry machines we use today.

According to an estimate, even this modest power budget allows the brain to achieve an astonishing 1 exaflop, equivalent to 1000 petaflops—a feat that the world’s fastest supercomputer with its 30 megawatts of power struggles to match at 200 petaflops.

The brain’s secret lies in its neurons, which integrate processing and memory, unlike the Von Neumann architecture.

The brain processes information in a massively parallel manner, with billions of neurons and trillions of synapses working simultaneously. Despite its remarkable intricacy, the brain remains compact and economical in its energy usage.

What is Neuromorphic Computing?​

Neuromorphic computing (NC) is a branch of computing technology inspired by the structure and functioning of the human brain’s neural networks.

It seeks to design and develop computer architectures and systems that mimic the parallel and distributed processing capabilities of the brain, enabling efficient and energy-effective processing of complex tasks.

This approach aims to overcome the limitations posed by the Von Neumann architecture for AI tasks especially by co-locating memory and processing at single location.

To comprehend NC, it is vital to understand how the brain works. Neurons, the building blocks of brain, communicate via electrical signals for information processing.

Upon receiving signals from interconnected neurons, they process and emit impulses.

These impulses travel along pathways formed by neurons, with synapses – gaps between neurons – facilitating the transmission.

Within the framework of NC, analog memristors are utilized to replicate the function of the synapses, achieving memory by adjusting resistance.

The rapid communication between neurons is typically achieved through the utilization of Spiking Neural Networks (SNNs).

These SNNs link spiking neurons using artificial synaptic devices, such as memristors, which employ analog circuits to mimic brain-like electrical signals.

These analog circuits offer significantly higher energy efficiency compared to the conventional Von Neumann architecture.

Neuromorphic Technologies​

The rise of AI is boosting the demand for neuromorphic computing.

The global neuromorphic computing market is expected to grow from USD 31.2 million in 2021 to around USD 8,275.9 million by 2030, with an impressive CAGR of 85.73%. In response, companies are advancing neuromorphic technologies, such as:

IBM’s TrueNorth: Introduced in 2014, it’s a neuromorphic CMOS integrated circuit with 4096 cores, over a million neurons, and 268 million synapses. TrueNorth overcomes von Neumann bottlenecks, consuming only 70 milliwatts.
Intel’s Loihi: Unveiled in 2017, Loihi is 1000 times more energy-efficient than typical neural network training. It features 131,072 simulated neurons and shows energy efficiency 30-1000 times greater than CPUs/GPUs.
BrainChip’s Akida NSoC: Using spiking neural network architecture, it integrates 1.2 million neurons and 10 billion synapses. Akida supports real-time, low-power AI applications like video object detection and speech recognition.

These innovations signal the rapid evolution of neuromorphic computing to meet AI demands.

Challenges of Neuromorphic Computing​

Realizing the potential of NC in AI demands addressing specific challenges.

Firstly, the development of efficient algorithms compatible with neuromorphic hardware is crucial. This requires a deep understanding of hardware operations and tailored adaptations.

Secondly, the need to handle larger, intricate datasets is crucial. The present NC experiments involve relatively modest datasets, necessitating exploration of its performance with more substantial and complex problems.

As dataset size and complexity expand, NC’s computational demands increase.

The challenge lies in designing NC systems capable of meeting these demands while delivering precise and effective solutions.

Despite encouraging outcomes from smaller-scale tests, NC’s performance with larger and more intricate datasets remains untested.

Further research and development are essential to optimize the technology for practical applications.

The Bottom Line​

Neuromorphic Computing (NC) draws inspiration from the brain’s neural networks to revolutionize AI with energy efficiency.

As AI advances bring environmental concerns, NC offers an alternative by mimicking the brain’s parallel processing.
Unlike the Von Neumann architecture, which hampers efficiency, NC co-locates memory and processing, overcoming bottlenecks.

Innovations like IBM’s TrueNorth, Intel’s Loihi, and BrainChip’s Akida NSoC showcase the potential of neuromorphic technologies.

Challenges persist, including algorithm adaptation and scalability to larger datasets. As NC evolves, it promises energy-effective AI solutions with sustainable growth potential.
 
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This combined with Akida the mind boggles🤔
Article has nothing to do with akida just had a thought..... And yes it 🤕

Good times ahead👇

Screenshot_20230831-015253-193.png
 
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Tothemoon24

Top 20
Socionext !
Sounds very saucy 🔥

 
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Socionext !
Sounds very saucy 🔥


What flavour is the sauce ?😉
I like the power consumption
Also without any CPU
Without firmware has me puzzled
 
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