BRN Discussion Ongoing

MDhere

Regular
Got his shares same time last year then sold 2 days later.


View attachment 50961
Well that will be interesting and his LOSS 🙂
I guess HEir knows that he is getting free shares each time so why not sell and enjoy every min of your life. Heir heir
Next year another 2mil thanks and Sp price will be HUGE (imo) so the way I see it everthing is a balance. (Especially when they are "hard earnt" free shares 😀🤣
Santa is coming and I'm on the nice list - $2 is on the top of my Santa list, we will see how nice he thinks I've been 🤣
 
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Smart man, any buying under a dollar is going to prove to be cheap long term in my opinion, and sub 30c is just a massive bonus, we were
never, ever worth north of $2 and the market just loved reminding us so, week after week, month after month as we slide down that slippery
ice slope, BUT while all that has been going on, we have progressed as a company in numerous ways.

Edge X loves our technology, many companies are coming up with ideas that even the smartest within our company hadn't even thought of, and that's the power of an ecosystem, lots of highly creative minds working away on things that we may not even know about until the phone in Sean's office rings, and the caller says, is your pen full of ink, we wish to sign on the dotted line, "they" control that timeline, "not" us.

I can confirm that our founder is happy with the term semi-retiring, and as I and I'd assume a number of other bolted on shareholders would have thought, it was Peter who decided who his replacement (for the want of a better word) would be, and in all honesty, Peter was the only one to really know who could truly step into his shoes, Peter speaks extremely highly of Dr Tony, has worked alongside him in San Diego, he has a strong personality, knows his stuff, is on the same page as Peter and doesn't mess around....that's a win-win for us all, in real terms we actually have two CTO's if that makes sense...finally, I can say with 100% certainty despite becoming semi-retired shortly, the passion, the fire, the creativity, the dream as our number one shareholder is still well and truly alive.

My confidence levels are through the roof...God Bless all Brainchip supporters, the journey continues ;)(y)

Tech x
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Damo4

Regular
Are you good friends with the moderator? As he gives me a warning and let's you insult people
There's no befriending the Dreadbot.
It's an Ai perfectly trained to smell BS and deal swift justice.
 
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robsmark

Regular
There's no befriending the Dreadbot.
It's an Ai perfectly trained to smell BS and deal swift justice.
Clearly not powered by Akida then.
 
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Easytiger

Regular
 
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Deadpool

hyper-efficient Ai
Theirs only one word, every one needs to comprehend from Mr Mungers top 10 list and that is "CONVICTION". inter-generational wealth is just around the corner for the true believers, Mark me
 
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IloveLamp

Top 20
Worthwhile checkin out SocioNext LinkedIn page, a few nuggets to be had

Screenshot_20231129_224453_LinkedIn.jpg
 
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Diogenese

Top 20
Worthwhile checkin out SocioNext LinkedIn page, a few nuggets to be had

View attachment 50971


For those who may have missed it:

sn_pr20221223_01e.pdf (socionext.com)


[MILPITAS/Calif. and Yokohama/Japan. December 23, 2022] --- Socionext Inc., a global leader in the
design and development of innovative System-on-Chip products, will showcase its automotive custom SoC technologies at the CES Vehicle Tech and Advanced Mobility Zone, located in the Las Vegas Convention Center, North Hall, Booth 10654. CES runs from January 5-8, 2023.
...
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.

Creating a proprietary chip requires a complex, highly structured framework with a complete support system for addressing each phase of the development process. With extensive experience in custom SoC development, Socionext uses state-of-the-art process technologies, such as 7nm and 5nm , to produce automotive-grade SoCs that ensure functional safety while accelerating software development and system verification.

Socionext is committed to offering the optimal combination of IPs, design expertise, software development and support to implement large-scale, fully customizable automotive SoC solutions to meet the most demanding and rigorous automotive application performance requirements.
1701263203336.png
 
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Frangipani

Regular
Marc Kennis from Stocks Down Under being cautiously optimistic about BRN (from around 11 min) - “Things seem to be turning…” 👍🏻 (And I don’t think he had been smoking pot when he said that, even though the video’s still picture might lead to that assumption… 🤣)



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Diogenese

Top 20
For those who may have missed it:

sn_pr20221223_01e.pdf (socionext.com)


[MILPITAS/Calif. and Yokohama/Japan. December 23, 2022] --- Socionext Inc., a global leader in the
design and development of innovative System-on-Chip products, will showcase its automotive custom SoC technologies at the CES Vehicle Tech and Advanced Mobility Zone, located in the Las Vegas Convention Center, North Hall, Booth 10654. CES runs from January 5-8, 2023.
...
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.

Creating a proprietary chip requires a complex, highly structured framework with a complete support system for addressing each phase of the development process. With extensive experience in custom SoC development, Socionext uses state-of-the-art process technologies, such as 7nm and 5nm , to produce automotive-grade SoCs that ensure functional safety while accelerating software development and system verification.

Socionext is committed to offering the optimal combination of IPs, design expertise, software development and support to implement large-scale, fully customizable automotive SoC solutions to meet the most demanding and rigorous automotive application performance requirements.
View attachment 50980

The interesting thing about this is that, way back when Akida was being taped out by Socionext, they announced that Socionext would co-promote Akida with their Synquacer processor. As far as we know, nothing has come of that to date, so one could be forgiven for expecting Socionext to take a jaundiced view of Akida - once bittern?

1701266337756.png


Not a bit of it!

Less than a year ago, they announced another project to incorporate Akida with their automotive sensors.

Now my guess is that Socionext do know a little about sensors and processors, and they would not choose just any old AI processor as part of their thrust into automotive smart sensors.
 
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Frangipani

Regular
Todd Vierra’s Impact Summit 2023 presentation on Sustainable Cities Using Smart Efficient AI is now online!

While the recording of that virtual workshop is publicly accessible via the Hackster.io Impact Summit 2023 website (https://events.hackster.io/impactsummit2023), a lot of the slides Todd showed are marked “2023-11 BrainChip Presentation Confidential”, so I assume we better not share them here? How about other slides from that same presentation that are not marked confidential? 🤔


 
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Frangipani

Regular

Great find, @thelittleshort !

Since the link to X-formerly-known-as-Twitter didn‘t work for me, I googled for the info elsewhere and found this research paper on the Brainchip website:


Finding Bacteria in Blood​

A Hardware-based Neuromorphic Solution for Real-World E-Nose Applications​


Executive Summary:

Advancements in neuromorphic technology, which mimics the human brain has opened exciting possibilities in creating machines that can smell, much like our noses. This paper introduces a bio-inspired, low-power method that recognizes different patterns of scents, functioning as the “brain” of an electronic nose. This highly efficient and compact solution can be seamlessly incorporated into electronic nose systems targeted for high-precision, real-world applications. In practical tests, it has proven very effective, especially in swiftly identifying various bacteria in blood samples using electronic nose data. The proposed method achieves 181 inferences per second with a classification accuracy of 97.42% and dynamic efficiency, measured as energy consumed per inference, of 135 µJ/inference. This combination of accuracy, efficiency, and speed makes it an exceptional tool for health and safety applications that can be used in a cost-effective, portable form factor in remote regions, which may not have access to labs or even connectivity to the cloud.

Advancing Artificial Olfaction: Bridging Biology and Technology

Electronic noses (e-noses) serve a vital role in applications such as quality assurance in food production and non-invasive medical diagnostics. They are sophisticated devices designed to detect odours. They operate by mimicking the biological process of smell, where specific neurons in the nose identify volatile organic compounds and send encoded signals to the brain for odour recognition. Electronic noses are equipped with an array of chemical sensors that produce electrical signals when they encounter airborne molecules, known as Volatile Organic Compounds (VOCs). The unique pattern of these signals is akin to a fingerprint, which can be analysed and identified using various techniques to determine the specific odour present.

Despite the technological prowess demonstrated in laboratory settings, the use of electronic noses in everyday environments faces challenges. Limitations include the need for greater portability, improved power efficiency, enhanced performance, and quicker, more reliable odour classification. Advances in machine learning have increased the accuracy of electronic noses; however, these improvements often come at the cost of increased computational demand, affecting the device’s portability and energy usage. Current research in neuromorphic computing, inspired by the brain’s efficient processing, promises to overcome these limitations by emulating the accuracy and low-energy profile of the natural olfactory system. While previous studies have achieved intricate biological mimicry, such complexity is not practical for widespread application.
Our research presents a bio-inspired approach that harnesses the efficiency of neuromorphic hardware. We have developed a neuromorphic system that processes electronic nose data using a combination of an event-based data encoder and a spiking neural network classifier optimized for implementation on Akida neuromorphic hardware. This system is built to leverage the inherent strengths of neuromorphic computing, aiming to provide a robust inference engine capable of high-accuracy odour classification with low power consumption and minimal delay, ready for real-world deployment.

Overview:
In this research, we utilized the ‘bacteria in blood’ dataset, which was previously collected by the Mednose project team at Örebro University. Their project was centred on the early detection of different bacterial species in blood, by identifying the unique chemical signatures they emit during initial growth phases. The collection was carried out using the NST 3220 Emission Analyzer, a sophisticated electronic nose device developed by Applied Sensors, equipped with a combined array of 22 metal-oxide semiconductor (MOS) and MOSFET sensors. The project’s objective was to distinguish between ten types of bacteria in blood samples, and for this purpose, a comprehensive dataset of 1200 samples was created, with 120 samples representing each bacterial species. For an in-depth understanding of the electronic nose experiments and the sampling protocols, a detailed description is available in the work by Trincavelli et al. titled “Direct identification of bacteria in blood culture samples using an electronic nose,” published in the IEEE Transactions on Biomedical Engineering

Streamlining Data: From Raw Signals to Event-Driven Patterns

Pre-processing plays a pivotal role in transforming raw complex sensor data into a more manageable form for pattern recognition and analysis. We start by removing any constant background noise from the readings—a step known as baseline cancellation. Then we adjust the scale of the sensor responses to ensure consistency across all samples.

The heart of our approach is a specialized model designed to interpret these streamlined signals. Our model is fine-tuned for data that’s been transformed into a pattern of digital events, similar to the way our brain receives and processes sensory input. To convert the sensor’s continuous data into this digital format, we explored two methods. The first method, based on our previously published encoding algorithm known as Address Event Representation for Olfaction (AERO), arranges the sensor information into a structured grid, marking the presence of a signal at each moment in time. This is akin to creating a digital snapshot of the scent at every point, which helps us capture the essential characteristics of each odour.

The second method, the Step Forward (SF) algorithm, a bio-inspired data encoding method, highlighting changes in the odour data only when they pass a certain threshold. This is inspired by how our senses work, paying attention to changes rather than static information, making it efficient and effective at capturing rapid variations in odour signals.

We also considered that not all data might be necessary for accurate identification. Perhaps, after the initial burst of information when the scent is first detected, the remaining data might not add much value. So, we experimented with using different amounts of data to see if we could still identify the odours without the full 5-minute recording, focusing only on the critical moments that define each scent.

Optimising Akida Spiking Neural Networks for Effective Odour Detection
The core of our classification system is a spiking neural network (SNN), which mimics the way neurons in the brain process information and learn. It consists of two main layers: the input layer, where data comes in, and the processing layer, where the learning happens. The neurons within the processing layer are finely tuned to learn and recognize patterns in the incoming data and respond by firing when a familiar pattern is identified, thereby signalling the detection of a specific odour.

The learning mechanism of our SNN is rooted in a biological process known as Spike Time Dependent Plasticity (STDP). This natural learning mode allows the network to rapidly strengthen the synaptic connections that correspond to frequently encountered activation patterns, a fundamental aspect of how the brain learns from repetition and reinforcement. In the classification phase, the system employs a competitive process where the most activated neuron—indicating the recognition of a learned pattern—determines the odour’s classification. This is achieved through the winner-take-all approach, where the ‘winning’ neuron’s response guides the output classification.

To achieve the most accurate and efficient performance, we conducted a comprehensive optimisation of the network’s hyper-parameters. This involved adjusting the number of neurons associated with each class of odour, the number of active connections each neuron should have, and the intensity of learning competition among neurons. The configuration resulted in a robust model that could accurately classify odours based on the patterns it had learned.
The SNN model was rigorously tested and validated using MetaTF, a Python-based simulation environment for AKD1000 reference SoC, before its deployment on the Akida reference hardware. This validation ensured that our approach was sound and could be effectively transferred to the NSoC, allowing for a seamless transition from theory to application. The entire workflow, from the initial data pre-processing to the final odour classification, is designed to be fully compatible and integrated within the NSoC, showcasing the system’s readiness for deployment in electronic nose devices.

Performance Evaluation of Akida SNN Classifier

The objective of this study was to evaluate whether the spiking neural network (SNN) could accurately classify odours without needing to process the entire 5-minute sample, thus potentially reducing the time for analysis. We used a windowed approach, where the electronic nose data and then converted into an event-based format by the data-to-event encoder for the SNN to process.
Our performance assessment of the SNN classifier utilised a robust 3-fold cross-validation method, ensuring a reliable statistical evaluation. The results, summarized in the classification table, showed high mean classification accuracies for both encoding algorithms (AERO and Step Forward), particularly with the first 200 data points. This suggests that the essential features for odour differentiation are present early in the sampling process.

Screenshot-2023-11-29-at-7.11.09-AM-1024x498.png


The Step Forward encoding technique, while similar in classification accuracy to AERO, was more data-efficient, condensing the information into a smaller input size for the SNN. However, this efficiency required additional pre-processing, which could slightly increase the time to reach a classification result.
For real-world application, the classifiers were tested on the Akida Neuromorphic System-on-Chip (NSoC). The SNN classifiers performed on par with the MetaTF chip emulator used during development. The AERO-based classifier model demonstrated a dynamic power consumption of only 24.5 mW with a high throughput of 181 inferences per second, translating to an energy efficiency of 135 µJ per inference. On the other hand, the Step Forward encoded model consumed slightly more power, averaging 25 mW, with a lower throughput of 31.5 inferences per second, resulting in a dynamic efficiency of 822 µJ per inference.

These differences in performance and efficiency are attributed to the varying neural resources each model utilized. Nonetheless, the findings affirm the potential of SNN classifiers to function as low-power, real-time pattern recognition engines for electronic nose
systems, providing a balance of speed, accuracy, and energy efficiency.

Revolutionizing Diagnostic Tools: Akida’s NSoC-Powered E-Nose​


Screenshot-2023-11-29-at-7.20.02-AM-300x239.png


Figure 1: Shows the Mednose apparatus with Applied Sensor NST320 and attached computer.
The system used to develop this Mednose system is fixed, large, power-hungry and needs a great deal of compute. While there are portable systems available for the sensing, they are not capable of doing the diagnostics on device today.
The quest for a real-time, energy-efficient system for odour recognition has long stood as a critical challenge in the development of electronic nose technologies. This paper has showcased a significant leap forward in this domain: the application of an event-based neuromorphic approach for processing and classifying electronic nose data, marking a substantial innovation in pattern recognition engines for e-nose systems.

Screenshot-2023-11-29-at-7.23.25-AM.png


Fig 2: Sample diagnostic platform using NoseChip 32-array sensor and AKD1000 SoC which can enable single board, extremely compact, energy-efficient, and accurate solution.

In summary, the proposed solution integrates two key components: an innovative data-to-event encoder using AERO and Step Forward (SF) techniques for translating sensor data into a neural-compatible format and an Akida SNN classifier that employs biologically inspired algorithms to discern and categorize patterns in the data.

The performance of this system is notable—when tested on the MedNose dataset, it demonstrated an impressive ability to distinguish among ten different bacteria types, achieving a peak classification accuracy of 97.42%. This not only surpasses previous models, which needed multiple samples to reach slightly lower accuracy levels, but also does so with remarkable efficiency. Critically, the SNN model was rigorously tested on the Akida NSoC, where it exhibited exceptionally low power usage, consuming only 24.5 mW, while maintaining a high throughput of 181 inferences per second. This performance marks a significant advancement, indicating that such a system is not only possible but now can be provided with a very cost-effective and portable device that enables rapid disease diagnosis with the critical level of precision.

These results highlight the processing capabilities of the Akida SNN for complex time-varying signals and demonstrate potential impact of our company’s technology in real-world applications. When used in conjunction with such sensing systems, the implications for healthcare are particularly profound, where these advancements could drastically reduce the time and resources required to assist in disease detection and identification, ultimately leading to faster, more accurate medical responses and better patient outcomes.


Click here to read the White paper for more details.
 
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Sirod69

bavarian girl ;-)

Media AlertBrainChip Details Olfactory Capabilities of Identifying Bacteria in the Blood in New Research Report​

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 the availability of a research paper detailing how neuromorphic computing can be utilized as part of an electric nose system to detect and identify different bacteria in the blood.

With findings achieved through studies by BrainChip Research, “Finding Bacteria in the Blood: Scaling a Hardware-Driven Neuromorphic Solution for Real-World E-Nose Applications” presents how a hardware-based, low-power neuromorphic solution can be combined with electronic sensors to create compelling real-world healthcare solutions that are cost-effective, portable and accurate. These assisted devices could significantly speed up disease diagnosis in remote locations, or even outside of traditional clinical facilities.
The paper explores a blood dataset collected as part of the Mednose project at Örebro University. The classifier model developed using Akida was able to identify ten different bacteria species in blood samples with a classification accuracy of 97.42%, outperforming previous implementations.

“Leveraging neuromorphic hardware to provide portable, power-efficient solutions for use in the identification of sensory data is a game-changer for a plethora of practical applications, such as e-nose systems,” said Anup Vanarse, Research Scientist at BrainChip. “This latest research paper shows how Akida’s olfactory analysis technology allows for efficient and accurate detection of various strains of bacteria in blood to help with important disease diagnosis. Incorporating beneficial AI within sensory devices will provide the means for massive breakthroughs in the healthcare industry.”

Those interested in reading more about neuromorphic solutions utilized in e-nose applications can download the full research paper at https://brainchip.com/finding-bacteria-in-blood

 
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goodvibes

Regular
Hi Frangipani & TLS

the last summary in the article says all to me - best in class:
The performance of this system is notable—when tested on the MedNose dataset, it demonstrated an impressive ability to distinguish among ten different bacteria types, achieving a peak classification accuracy of 97.42%. This not only surpasses previous models, which needed multiple samples to reach slightly lower accuracy levels, but also does so with remarkable efficiency. Critically, the SNN model was rigorously tested on the Akida NSoC, where it exhibited exceptionally low power usage, consuming only 24.5 mW, while maintaining a high throughput of 181 inferences per second. This performance marks a significant advancement, indicating that such a system is not only possible but now can be provided with a very cost-effective and portable device that enables rapid disease diagnosis with the critical level of precision.

These results highlight the processing capabilities of the Akida SNN for complex time-varying signals and demonstrate potential impact of our company’s technology in real-world applications. When used in conjunction with such sensing systems, the implications for healthcare are particularly profound, where these advancements could drastically reduce the time and resources required to assist in disease detection and identification, ultimately leading to faster, more accurate medical responses and better patient outcomes.

 
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TECH

Regular

Media AlertBrainChip Details Olfactory Capabilities of Identifying Bacteria in the Blood in New Research Report​

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 the availability of a research paper detailing how neuromorphic computing can be utilized as part of an electric nose system to detect and identify different bacteria in the blood.

With findings achieved through studies by BrainChip Research, “Finding Bacteria in the Blood: Scaling a Hardware-Driven Neuromorphic Solution for Real-World E-Nose Applications” presents how a hardware-based, low-power neuromorphic solution can be combined with electronic sensors to create compelling real-world healthcare solutions that are cost-effective, portable and accurate. These assisted devices could significantly speed up disease diagnosis in remote locations, or even outside of traditional clinical facilities.
The paper explores a blood dataset collected as part of the Mednose project at Örebro University. The classifier model developed using Akida was able to identify ten different bacteria species in blood samples with a classification accuracy of 97.42%, outperforming previous implementations.

“Leveraging neuromorphic hardware to provide portable, power-efficient solutions for use in the identification of sensory data is a game-changer for a plethora of practical applications, such as e-nose systems,” said Anup Vanarse, Research Scientist at BrainChip. “This latest research paper shows how Akida’s olfactory analysis technology allows for efficient and accurate detection of various strains of bacteria in blood to help with important disease diagnosis. Incorporating beneficial AI within sensory devices will provide the means for massive breakthroughs in the healthcare industry.”

Those interested in reading more about neuromorphic solutions utilized in e-nose applications can download the full research paper at https://brainchip.com/finding-bacteria-in-blood


Good morning,

Nice to see that this paper has finally been released, some may remember I reported that the company was working on this project of identifying bacteria in blood, but the papers release had been held back, from memory a few years ago, Adam (Osseiran) presented at a medical conference in Europe, but nothing was ever reported until now, so once again, excellent work "behind the scenes" by Adam, Anup and the team based out of Perth, congratulations fellas !

All Australian shareholders salute your great work....regards Chris (Tech) :geek:

P.S. I hear that Alan Harvey of our Scientific Advisory Board is doing some excellent work with Peter on Cortical Columns as the Neuroscience Department keeps driving our company forward, nothing progresses unless the team of researchers research long and hard, putting in long hours for the benefit of all, thanks to all concerned, I and many others appreciate your brilliance...Tech NZ 🧐
 
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