Stable Genius
Regular
Combining Geologists and NN.
BRN Discussion Ongoing
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Shadow59
Regular
T
Tony probably, but will he tell?
Fullmoonfever
Top 20
Slowly creeping towards the tipping point.
Management better have damn well positioned us and Akida to be able to smash that "digital wall" when the cracks open
www.advsyscon.com
The volume of data being created continues to rise exponentially. Data is a critical asset for organizations, providing a foundation for digital services, AI, natural language processing (NLP), deep neural networks, and much more. These technologies are also compute-intensive. Organizations are reaching a point where their data storage and computing are unable to keep up with the growth of data and technological advancements.
This is what Gartner has termed the “digital wall”. In order to keep pace with competitors and market demands, organizations will begin to test emerging storage and compute technologies, including DNA storage, glass storage, neuromorphic computing, and extreme parallelism.
The challenge for IT teams over the next two to three years is how to integrate new technologies and reliably manage big data across disparate environments.
Management better have damn well positioned us and Akida to be able to smash that "digital wall" when the cracks open
2024: Gartner's IT Automation Trends Revisited | ActiveBatch Blog
It's 2024 and we're revisiting Gartner's IT automation Trends, learn more about hyperautomation, new data storage technologies and an expanding role for IT.
Preparing For The “Digital Wall”
- By 2025, traditional computing technologies will hit a digital wall, forcing the shift to new computing paradigms such as neuromorphic computing.
The volume of data being created continues to rise exponentially. Data is a critical asset for organizations, providing a foundation for digital services, AI, natural language processing (NLP), deep neural networks, and much more. These technologies are also compute-intensive. Organizations are reaching a point where their data storage and computing are unable to keep up with the growth of data and technological advancements.
This is what Gartner has termed the “digital wall”. In order to keep pace with competitors and market demands, organizations will begin to test emerging storage and compute technologies, including DNA storage, glass storage, neuromorphic computing, and extreme parallelism.
The challenge for IT teams over the next two to three years is how to integrate new technologies and reliably manage big data across disparate environments.
Isn't that exactly what they've been doing by expanding our ecosystem partners and product offerings?Slowly creeping towards the tipping point.
Management better have damn well positioned us and Akida to be able to smash that "digital wall" when the cracks open
2024: Gartner's IT Automation Trends Revisited | ActiveBatch Blog
It's 2024 and we're revisiting Gartner's IT automation Trends, learn more about hyperautomation, new data storage technologies and an expanding role for IT.
Preparing For The “Digital Wall”
- By 2025, traditional computing technologies will hit a digital wall, forcing the shift to new computing paradigms such as neuromorphic computing.
The volume of data being created continues to rise exponentially. Data is a critical asset for organizations, providing a foundation for digital services, AI, natural language processing (NLP), deep neural networks, and much more. These technologies are also compute-intensive. Organizations are reaching a point where their data storage and computing are unable to keep up with the growth of data and technological advancements.
This is what Gartner has termed the “digital wall”. In order to keep pace with competitors and market demands, organizations will begin to test emerging storage and compute technologies, including DNA storage, glass storage, neuromorphic computing, and extreme parallelism.
The challenge for IT teams over the next two to three years is how to integrate new technologies and reliably manage big data across disparate environments.
I think that's exactly what they've been doing by expanding our ecosystem partners and product offerings. IMO we are in the sweet spot, timing wise.Slowly creeping towards the tipping point.
Management better have damn well positioned us and Akida to be able to smash that "digital wall" when the cracks open
2024: Gartner's IT Automation Trends Revisited | ActiveBatch Blog
It's 2024 and we're revisiting Gartner's IT automation Trends, learn more about hyperautomation, new data storage technologies and an expanding role for IT.
Preparing For The “Digital Wall”
- By 2025, traditional computing technologies will hit a digital wall, forcing the shift to new computing paradigms such as neuromorphic computing.
The volume of data being created continues to rise exponentially. Data is a critical asset for organizations, providing a foundation for digital services, AI, natural language processing (NLP), deep neural networks, and much more. These technologies are also compute-intensive. Organizations are reaching a point where their data storage and computing are unable to keep up with the growth of data and technological advancements.
This is what Gartner has termed the “digital wall”. In order to keep pace with competitors and market demands, organizations will begin to test emerging storage and compute technologies, including DNA storage, glass storage, neuromorphic computing, and extreme parallelism.
The challenge for IT teams over the next two to three years is how to integrate new technologies and reliably manage big data across disparate environments.
Sirod69
bavarian girl ;-)
VVDN Technologies
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David holland
Regular
2 - I.P sales and duck egg since, we have partnerships but no I.P sales since, but it's all top secret
Fullmoonfever
Top 20
IOT for all vid from about 3 weeks ago.
Not sure if posted previously.
Watch or skip to 19 min mark.
Gil Dror, CTO at SmartSense by Digi quite aware of BRN in the neuromorphic space....nice to hear.
Not sure if posted previously.
Watch or skip to 19 min mark.
Gil Dror, CTO at SmartSense by Digi quite aware of BRN in the neuromorphic space....nice to hear.
Frangipani
Top 20
That’s your interpretation only. The way I see it, we are not led to believe that the chip itself is capable of learning - in fact, on-chip learning is simply not required for these solar-powered devices that can send and receive data via the LoRaWAN network, the leading standard for long-range IoT.
I already posted about these wildfire sensors a couple of weeks ago (https://thestockexchange.com.au/threads/brn-discussion-ongoing.1/post-305877), and this is what the company manufacturing them states on their website:
How can these sensors detect the fire? How can they know it’s a real fire but not just a burning cigarette?
The built-in artificial intelligence (AI) of the sensors is continually trained for the specific ‘smell’ of the target forest on fire. Our customers collect samples from the forest floor and send them to Germany so that we can train our AI in the lab. Over time, we will collect more and more samples from typical forests in the different parts of the world and will eventually no longer have to train the AI for new deployments as there is only a finite amount of forest types. We expect this to be the case within the first two years of operation.Doing so allows our Silvanet system to be continuously evolving and improving and greatly minimizes any risks of false positives within the platform.
———————————————————————————————————
What the Bosch article does say is:
“With the help of artificial intelligence, this sensor also analyzes the data it collects right there on the spot. If it detects a fire, it immediately sounds the alarm, sending a signal to the cloud and notifying emergency services.
(…)
The tiny “nose” that sniffs out forest fires measures just three by three millimeters. The Bosch environmental sensor installed in this forest-fire detection system is the world’s first gas sensor to feature artificial intelligence and the first to be deployed as an early wildfire warning tool. It can detect various gases, including fire gases such as hydrogen, carbon monoxide, and most hydrocarbons. The system is constantly learning and improving. Data collected from all installed sensors serves to continuously train the environmental sensors using artificial intelligence so they can detect and analyze gases with even greater accuracy. The BME AI-Studio Server software was developed specifically for this purpose.”
———————————————————————————————————-
These kind of Edge AI devices would be a perfect use case for Akida, even without utilising its on-chip learning function, especially since the Bosch sensors are collecting other environmental data as well such as temperature, humidity and air pressure, and the company is also looking at future applications, e.g. involving sensors that can detect chainsaw noise to help prevent illegal logging.
Dryad explicitly state on their website they are open-minded about collaborations: “Open for integration of 3rd party sensors and applications, no single-vendor lock-in.”
So maybe we haven’t entirely missed out on that niche just yet?
Is Dryad involved solely in wildfire detection?
While our initial focus at Dryad is wildfire detection, the Bosch BME688 sensors used in Silvanet sensor devices also collect environmental data such as temperature, humidity and air pressure which is periodically sent to the Silvanet Cloud, allowing forest owners to better understand the microclimate of the forest and its influence and development of the forest heath over time.Further planned use-cases involve sensors that can detect chainsaw noise to help prevent illegal logging.
It’s not just about forests either, our technology can also be applied to other ecosystems including lakes, rivers and oceans. At Dryad, we have ambitious plans to connect the natural world and protect our planet.
Last edited:
Learning
Learning to the Top 🕵♂️
Not sure this has been posted.
A recent patent publication from TCS.
@Diogenese is this TSC patent using Brainchip's Akida?
Abstract
State of art techniques rely of FPGA based approaches when power efficiency is of concern. However, compared to SNN on Neuromorphic hardware, ANN on FPGA requires higher power and longer design cycles to deploy neural network on hardware accelerators. Embodiments of the present disclosure provide a method and system for energy efficient hierarchical multi-stage SNN architecture for classification and segmentation of high-resolution images. Patch-to-patch-class classification approach is used, where the image is divided into smaller patches, and classified at first stage into multiple labels based on percentage coverage of a parameter of interest, for example, cloud coverage in satellite images. The image portion corresponding to the partially covered patches is divided into further smaller size patches, classified by a binary classifier at second level of classification. Labels across multiple SNN classifier levels are aggregated to identify segmentation map of the input image in accordance with the coverage parameter of interest.
Learning 🏖
A recent patent publication from TCS.
@Diogenese is this TSC patent using Brainchip's Akida?
Abstract
State of art techniques rely of FPGA based approaches when power efficiency is of concern. However, compared to SNN on Neuromorphic hardware, ANN on FPGA requires higher power and longer design cycles to deploy neural network on hardware accelerators. Embodiments of the present disclosure provide a method and system for energy efficient hierarchical multi-stage SNN architecture for classification and segmentation of high-resolution images. Patch-to-patch-class classification approach is used, where the image is divided into smaller patches, and classified at first stage into multiple labels based on percentage coverage of a parameter of interest, for example, cloud coverage in satellite images. The image portion corresponding to the partially covered patches is divided into further smaller size patches, classified by a binary classifier at second level of classification. Labels across multiple SNN classifier levels are aggregated to identify segmentation map of the input image in accordance with the coverage parameter of interest.
US20230154154A1 - Energy efficient hierarchical snn architecture for classification and segmentation of high-resolution images - Google Patents
State of art techniques rely of FPGA based approaches when power efficiency is of concern. However, compared to SNN on Neuromorphic hardware, ANN on FPGA requires higher power and longer design cycles to deploy neural network on hardware accelerators. Embodiments of the present disclosure...
patents.google.com
Learning 🏖
Kachoo
Regular
Scala 3 is an interesting one we will have to see if we get any news on this one. We are still partnered and working with Valeo as it shows on our web site. Weather its the Scala 3 or something else or a futer iteration it's been in the works for 3 years a long time. If Valeo was to punt Akida it would have done so a long time ago.
jtardif999
Regular
Yeah and as LDN (our previous CEO) said back about 3 years ago ‘we are in a sweet spot for LiDAR’.
Fullmoonfever
Top 20
For those like me who only have a rudimentary understanding on the industry supply chain, this was an interesting read.
Gives some perspective to the path to get to an end user.
Snip below and full read here...
anysilicon.com
Coming back to the semiconductor supply chain, it is extremely large and changes a lot over time as newer technologies are researched and older materials or techniques are exchanged for better ones. For context, the production of a single computer chip can require more than 1000 steps and close to 70 international border exchanges before it reaches the end customer.
Figure 2 – Semiconductor Supply Chain
First of all, there are three main sectors to the semiconductor supply chain: research and development, manufacturing, and end use. There is a new sector coming more and more into the picture, recycling, which is expected and needs to become an integral part of future semiconductor production plants if we are to meet the global green future standards.
The first sector, research and development (R&D), is the powerhouse behind the entirety of the semiconductor supply chain, as new research ideas and technologies directly impact all the other involved sectors. R&D includes both pre-competitive, exploratory studies and research on the fundamental technologies, and competitive research, which is more aimed at advancing the leading edge that the company has in the semiconductor industry.
Secondly, production or manufacturing consists of five major steps: design, fabrication, assembly, testing and packaging, with the last three usually being coupled together under the acronym ATP and often done by an OSAT company.
Companies have two possible routes that they can take: do everything in-house, i.e. being vertically integrated, in which case they become IDMs (Integrated Device Manufacturers) and sell the chip themselves; or design a certain IC and then outsource the rest, fabrication to foundries and ATP to OSAT (Outsourced Semiconductor Assembly and Testing) vendors, making them a fabless firm. For example, Intel is an IDM and AMD (their direct competition) is a fabless company, as they design products, but do not have the capabilities or facilities to manufacture them.
Regarding the inputs, the production of semiconductors has the raw materials, Semiconductor Manufacturing Equipment (SME for short), Electronic Design Automation (EDA) and the core Intellectual Property (IP).
Figure 3 – Detailed Semiconductor Supply Chain
As for end user, this involves distributing the ICs to other companies or retail customers for integration within a product (like smartphone, laptop, automobile, server, appliance etc.).
Gives some perspective to the path to get to an end user.
Snip below and full read here...
Semiconductor Supply Chain - AnySilicon
Figure 1: General Overview of the Semiconductor Supply Chain Being one of the most complex supply chains in the world, the semiconductor supply chain is worth around $0.5 trillion and is often very difficult to understand and manipulate. But before we dive deeper, we need to know what a supply...
anysilicon.com
Main Links of the Semiconductor Supply Chain
Coming back to the semiconductor supply chain, it is extremely large and changes a lot over time as newer technologies are researched and older materials or techniques are exchanged for better ones. For context, the production of a single computer chip can require more than 1000 steps and close to 70 international border exchanges before it reaches the end customer.
Figure 2 – Semiconductor Supply Chain
First of all, there are three main sectors to the semiconductor supply chain: research and development, manufacturing, and end use. There is a new sector coming more and more into the picture, recycling, which is expected and needs to become an integral part of future semiconductor production plants if we are to meet the global green future standards.
The first sector, research and development (R&D), is the powerhouse behind the entirety of the semiconductor supply chain, as new research ideas and technologies directly impact all the other involved sectors. R&D includes both pre-competitive, exploratory studies and research on the fundamental technologies, and competitive research, which is more aimed at advancing the leading edge that the company has in the semiconductor industry.
Secondly, production or manufacturing consists of five major steps: design, fabrication, assembly, testing and packaging, with the last three usually being coupled together under the acronym ATP and often done by an OSAT company.
Companies have two possible routes that they can take: do everything in-house, i.e. being vertically integrated, in which case they become IDMs (Integrated Device Manufacturers) and sell the chip themselves; or design a certain IC and then outsource the rest, fabrication to foundries and ATP to OSAT (Outsourced Semiconductor Assembly and Testing) vendors, making them a fabless firm. For example, Intel is an IDM and AMD (their direct competition) is a fabless company, as they design products, but do not have the capabilities or facilities to manufacture them.
Regarding the inputs, the production of semiconductors has the raw materials, Semiconductor Manufacturing Equipment (SME for short), Electronic Design Automation (EDA) and the core Intellectual Property (IP).
Figure 3 – Detailed Semiconductor Supply Chain
As for end user, this involves distributing the ICs to other companies or retail customers for integration within a product (like smartphone, laptop, automobile, server, appliance etc.).
Fullmoonfever
Top 20
IG tipping us
Snip below.
www.ig.com
While they involve greater risk than their large-scale peers, small-cap stocks also bring the promise of more lucrative rewards given their higher growth potential as modest-sized companies.
A small-cap stock is generally defined as a company with a market capitalisation of between several hundred million to $2 billion.
Here is a list of five of the top ASX small-cap stocks to consider, for those investors who consider them an acceptable choice given their current risk/reward preferences.
1. Temple & Webster Group Ltd (ASX: TPW)
2. Serko Ltd (ASX: SKO)
3. Adairs Ltd (ASX: ADH)
4. Universal Store Holdings (ASX: UNI)
5. Brainchip Holdings (ASX: BRN)
Brainchip's Akida neuromorphic processor has already found applications in the fields of smart car development and autonomous driving technology.
As an AI company, Brainchip's share price could receive a boost from the huge buzz surrounding the potential of generative AI technologies such as Chat GPT.
Snip below.
Five of the top ASX-listed small caps to watch
A list of five of the top small-cap stocks listed on the Australian Stock Exchange for traders and investors to consider in April 2024.
5 of the top small-cap ASX stocks in June
Small-cap stocks present the possibility of greater share price appreciation due to their growth potential. Here is a list of five of the top small-cap ASX stocks for traders to consider in June 2023.While they involve greater risk than their large-scale peers, small-cap stocks also bring the promise of more lucrative rewards given their higher growth potential as modest-sized companies.
A small-cap stock is generally defined as a company with a market capitalisation of between several hundred million to $2 billion.
Here is a list of five of the top ASX small-cap stocks to consider, for those investors who consider them an acceptable choice given their current risk/reward preferences.
1. Temple & Webster Group Ltd (ASX: TPW)
2. Serko Ltd (ASX: SKO)
3. Adairs Ltd (ASX: ADH)
4. Universal Store Holdings (ASX: UNI)
5. Brainchip Holdings (ASX: BRN)
5. Brainchip Holdings: (ASX: BRN)
Brainchip Holdings is an artificial intelligence company that claims to be the world's first commercial producer of neuromorphic processors that can mimic the way the human brain processes sensory inputs.Brainchip's Akida neuromorphic processor has already found applications in the fields of smart car development and autonomous driving technology.
As an AI company, Brainchip's share price could receive a boost from the huge buzz surrounding the potential of generative AI technologies such as Chat GPT.
Fullmoonfever
Top 20
Don't know who this is on twitter...small number followers but seems excited by Akida.
Looks like the team he mentions is something to do with Roko Legion or something?
I'm not on twitter so maybe someone who is might want to see what they up to?
Also mentions about the team thinking of buying some Prophesee EV kits at like $5.5k ea plus other AI players gear.
Liked the bit I highlighted in one of the tweets. Obviously keeping tabs on that too.
Looks like the team he mentions is something to do with Roko Legion or something?
I'm not on twitter so maybe someone who is might want to see what they up to?
Also mentions about the team thinking of buying some Prophesee EV kits at like $5.5k ea plus other AI players gear.
Liked the bit I highlighted in one of the tweets. Obviously keeping tabs on that too.
Fullmoonfever
Top 20
Apparently just released a prototype Akida robot into the Power Slap comp against a Shorter
Sirod69
bavarian girl ;-)
Luca VerreLuca Verre• 1.• 1.Co-Founder and CEO at PROPHESEE | WEF Technology Pioneer
Excited to announce that I'll be attending the World Economic Forum "Summer Davos" event in Tianjin!
Joining this prestigious gathering as PROPHESEE has been recognized as 2023 Global Innovator.
Our 6-year journey with WEF began in 2017 when we were awarded as Technology Pioneer.
Since then, it has been an incredible experience contributing to the WEF agenda of improving the state of the world.
I look forward to sharing insights on how neuromorphic technologies can enhance machine safety, intelligence, and efficiency.
Let's shape a brighter future together!
GStocks123
Regular
#tinymlemea2023 #neuromorphiccomputing #seneca #machinelearning #ai #tinyml | Guangzhi Tang
We are presenting a poster on our cutting-edge SENECA neuromorphic architecture at tinyML Foundation EMEA 2023 this week! Join us to discover how we are leveraging ultra-efficient event-driven computing technologies for AI computing. Look forward to engaging discussions and collaborations...
Has anyone been able to do a comparison between Akida & Seneca. More so, how our patents come in to play?
Risc-V-based digital neuromorphic processor
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