Jumpchooks
Regular
Voice recognition in Scotland
BRN Discussion Ongoing
- Thread starter TechGirl
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Voice recognition in Scotland
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Socionext and Sony catch up regularly as part of the following with the last meeting being 25.10.22:I have no idea about Magic-Eye giving Brainchip exposure to Sony but the following definitely suggests Socionext has that capacity.
When you open the following link click on Partners:
Socionext Cameras
Socionext is the ISP partner of e-con Systems. Powered by Socionext's ISP, e-con's cameras are integrated with advanced features to perform demosaicing, denoising, and auto functions on the captured image to help deliver a superior image.www.e-consystems.com
My opinion only DYOR
FF
AKIDA BALLISTA
My opinion only DYOR
FF
AKIDA BALLISTA
Rise from the ashes
Regular
Just for interest Christy Aerne is one of Peter's connections on LinkedIn.
Ubiquitous problem....coordinated stock manipulation by short entities
KNOW Labs (KNW) has been mentioned here before speculating that they could be using AKIDA. Their goal is to develop a non invasive blood glucose sensor/monitor.
Here is an excerpt from a news release addressing this issue.
KNOW Labs (KNW) has been mentioned here before speculating that they could be using AKIDA. Their goal is to develop a non invasive blood glucose sensor/monitor.
Here is an excerpt from a news release addressing this issue.
Know Labs, Inc. Addresses False and Incomplete Short Seller’s Claims
- KNW
+4.35% -
Wed, December 21, 2022, 8:29 AM GMT+13
In this article:
- KNW
+4.35%
SEATTLE, December 20, 2022--(BUSINESS WIRE)--Know Labs, Inc. (NYSE American: KNW), an emerging developer of non-invasive medical diagnostic technology, today addressed recent claims by a purported research firm known for fueling and participating in short-selling, which has distributed false and incomplete information about the company in an apparent effort to profit by driving down the company’s stock price.
"Our team at Know Labs remains highly confident in the ability of our Bio-RFID™ platform technology to detect and measure glucose and other analytes in the body non-invasively, at extremely high levels of accuracy," said Ron Erickson, Know Labs founder and chairman. "We continue to test and refine our technology while working toward the FDA clearance process for medical-grade diagnostic devices."
Know Labs will report its fourth quarter and fiscal year 2022 results during an audio webcast today, Dec. 20, 2022. This follows a recent presentation to institutional investors at the Bernstein CGM Disruptors Conference last month.
"Attempts to manipulate the capital markets by self-interested actors seeking short-term profits by spreading lies disguised as equities research are reprehensible," Erickson continued. "White Diamond Research is notorious for its smear campaigns. Their required legal disclosure makes its intentions clear: ‘You should assume that as of the publication date of our reports and research, White Diamond (possibly along with or through our members, partners, affiliates, employees, and/or consultants) along with our clients and/or investors and/or their clients and/or investors has a short position in all stocks (and/or options, swaps, and other derivatives related to the stock) and bonds covered herein, and therefore stands to realize significant gains in the event that the price of either declines.’ That tells you all you need to know."
"The purported research report about Know Labs got most things wrong but did get one thing right," Erickson added. "Detecting and measuring blood glucose non-invasively has never been done, and we remain confident Know Labs will offer the first devices to do it, improving the lives of millions of people with diabetes and pre-diabetes around the world."
About Know Labs, Inc.
Know Labs, Inc. is a public company whose shares trade on the NYSE American Exchange under the stock symbol "KNW." The Company’s technology uses spectroscopy to direct electromagnetic energy through a substance or material to capture a unique molecular signature. The Company refers to its technology as Bio-RFID™. The Bio-RFID technology can be integrated into a variety of wearable, mobile or bench-top form factors. This patented and patent-pending technology makes it possible to effectively identify and monitor analytes that could only previously be performed by invasive and/or expensive and time-consuming lab-based tests. The first application of our Bio-RFID technology will be in a product marketed as a non-invasive glucose monitor. It will provide the user with real-time information on blood glucose levels. This product will require U.S. Food and Drug Administration clearance prior to its introduction to the market.
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The good thing is after the whole video she talks about brainchip in the end. I would think Mike davies would have seen the whole video before it was published considering his position, he would like to know what is going to be published . He let her mention brainchip akida in the end. Seems like hmmmmm.
Fact Finder
Top 20
Thanks @charles2Ubiquitous problem....coordinated stock manipulation by short entities
KNOW Labs (KNW) has been mentioned here before speculating that they could be using AKIDA. Their goal is to develop a non invasive blood glucose sensor/monitor.
Here is an excerpt from a news release addressing this issue.
Know Labs, Inc. Addresses False and Incomplete Short Seller’s Claims
- KNW
+4.35%
Wed, December 21, 2022, 8:29 AM GMT+13
In this article:
- KNW
+4.35%
SEATTLE, December 20, 2022--(BUSINESS WIRE)--Know Labs, Inc. (NYSE American: KNW), an emerging developer of non-invasive medical diagnostic technology, today addressed recent claims by a purported research firm known for fueling and participating in short-selling, which has distributed false and incomplete information about the company in an apparent effort to profit by driving down the company’s stock price.
"Our team at Know Labs remains highly confident in the ability of our Bio-RFID™ platform technology to detect and measure glucose and other analytes in the body non-invasively, at extremely high levels of accuracy," said Ron Erickson, Know Labs founder and chairman. "We continue to test and refine our technology while working toward the FDA clearance process for medical-grade diagnostic devices."
Know Labs will report its fourth quarter and fiscal year 2022 results during an audio webcast today, Dec. 20, 2022. This follows a recent presentation to institutional investors at the Bernstein CGM Disruptors Conference last month.
"Attempts to manipulate the capital markets by self-interested actors seeking short-term profits by spreading lies disguised as equities research are reprehensible," Erickson continued. "White Diamond Research is notorious for its smear campaigns. Their required legal disclosure makes its intentions clear: ‘You should assume that as of the publication date of our reports and research, White Diamond (possibly along with or through our members, partners, affiliates, employees, and/or consultants) along with our clients and/or investors and/or their clients and/or investors has a short position in all stocks (and/or options, swaps, and other derivatives related to the stock) and bonds covered herein, and therefore stands to realize significant gains in the event that the price of either declines.’ That tells you all you need to know."
"The purported research report about Know Labs got most things wrong but did get one thing right," Erickson added. "Detecting and measuring blood glucose non-invasively has never been done, and we remain confident Know Labs will offer the first devices to do it, improving the lives of millions of people with diabetes and pre-diabetes around the world."
About Know Labs, Inc.
Know Labs, Inc. is a public company whose shares trade on the NYSE American Exchange under the stock symbol "KNW." The Company’s technology uses spectroscopy to direct electromagnetic energy through a substance or material to capture a unique molecular signature. The Company refers to its technology as Bio-RFID™. The Bio-RFID technology can be integrated into a variety of wearable, mobile or bench-top form factors. This patented and patent-pending technology makes it possible to effectively identify and monitor analytes that could only previously be performed by invasive and/or expensive and time-consuming lab-based tests. The first application of our Bio-RFID technology will be in a product marketed as a non-invasive glucose monitor. It will provide the user with real-time information on blood glucose levels. This product will require U.S. Food and Drug Administration clearance prior to its introduction to the market.
A timely warning to every investor manipulation never stops it will be with us every step up the ladder and as Brainchip goes further up the ladder the manipulation will become more sophisticated and the funds deployed to manipulate will increase.
Even after the bell is rung on a Nasdaq listing it will continue and some might say that this is when it will move up a gear or two.
It cannot be stopped. Your only defence is to do your own research and have a plan.
My opinion only DYOR
FF
AKIDA BALLISTA
Fact Finder
Top 20
Something to read over Christmas:
mobidev.biz
I have read this report and it certainly confirms the right time, right place nature of the Brainchip AKIDA technology and the developed partner ecosystems to take full advantage of these emerging trends.
I am now wondering if Rob Telson has a lot more still to disclose at CES 2023 than I originally thought.
Regards
FF
AKIDA BALLISTA
Artificial Intelligence Technology Trends for Business Owners in 2025
Discover the top AI trends that are increasing in 2025 and will determine how companies can leverage AI technology in the future.
I have read this report and it certainly confirms the right time, right place nature of the Brainchip AKIDA technology and the developed partner ecosystems to take full advantage of these emerging trends.
I am now wondering if Rob Telson has a lot more still to disclose at CES 2023 than I originally thought.
Regards
FF
AKIDA BALLISTA
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I just posted a question to her and her reply below
Baisyet
1 hour agoDidnt Intel just announced Brainchip Akida Ip Foundry ? what does that mean
3
Highlighted reply
Anastasi In Tech
1 hour ago
Intel will manufacture Brainchip's chips
mrgds
Regular
AWESOME ..................... thanks @Baisyet
Why doesn"t MD just say that Intel is adopting Akida SNN technology ......................
@Diogenese ........................ any thoughts much appreciated
AKIDA BALLISTA
This video provides (for me) the key to truly get a handle on how neuromorphic processing works, how it is able to save so much power and how its future applications seem so limitless. And as Intel continues to be so publicly committed to neuromorphic computing this fact alone gives credence to our relatively unknown company, Brainchip, to introduce their improved version (AKIDA) to a slightly less reluctant/circumspect commercial audience. What is perceived as Science Fiction can prove to be a difficult sell without influential advocates.
Thus being embraced by IFS may prove to be a big break for Brainchip's commercial acceptance.
Thank you to Peter van der Made, Anastasi and Mike D for sharing your brilliance and visions.
And of course all those that contribute so much here. We all know who they are.
Edit: Just read the above.....Intel to produce Brainchip's chips. We need a new emoji for WOW
Baron Von Ricta
Regular
Kachoo
Regular
Its a very delicate situation.AWESOME ..................... thanks @Baisyet
Why doesn"t MD just say that Intel is adopting Akida SNN technology ......................
@Diogenese ........................ any thoughts much appreciated
AKIDA BALLISTA
Imagine your supervisors and bosses Investers spent billions to develop a less superior product!
You don't just go oh we were wrong and let's go this way. It takes a plan and time to steer that giant vessel a different direction. You know how many jobs and manager roles and friends would have been booted with a sharp reaction. It's a big political game also.
Know Intel will make money and be in the chip race for edge AI. It's only time that the rest join.
I know we would love to hear them sing AKIDA but they won't not yet.
This partnership was the last DD I needed to really feel confident in BRN succeeding. As Arm, Merc, Renesas, Mega Chips and more weren't
This is a sign how quick others will adopt.
IMO
Merry Christmas Happy Holidays folks.
buena suerte :-)
BOB Bank of Brainchip
Nice work @Baisyet ..... WOW!I just posted a question to her and her reply below
Baisyet
1 hour ago
Didnt Intel just announced Brainchip Akida Ip Foundry ? what does that mean
3
Highlighted reply
Anastasi In Tech
1 hour ago
Intel will manufacture Brainchip's chips
Intel will manufacture Brainchip's chips
TheUnfairAdvantage
Regular
I can only guess that this video was produced prior to the public announcement of the engagement between Intel and Brainchip.AWESOME ..................... thanks @Baisyet
Why doesn"t MD just say that Intel is adopting Akida SNN technology ......................
@Diogenese ........................ any thoughts much appreciated
AKIDA BALLISTA
Schnitzel lover
Regular
Questions that are obvious in relation to this adoption..This is a positive view of where Brainchip is going in automotive. Mercedes Benz sells around an average of 3 million passenger vehicles and Jerome Nadel places 70 AKIDA chips pre processing sensor inputs before passing on as meta data. If Blind Freddie's mental arithmetic is correct that is 210 million AKIDA smart sensors.
"BrainChip Akida
Mercedes-Benz's EQXX concept car, which debuted at CES earlier this year, uses BrainChip's Akida neuromorphic processor for in-vehicle keyword recognition. Billed as "the most efficient car Mercedes has ever made," the car utilizes neuromorphic technology that consumes less power than a deep learning-based keyword spotting system. That's crucial for a car with a range of 620 miles, or 167 miles more than Mercedes' flagship electric car, the EQS.
Mercedes said at the time that BrainChip's solution was five to 10 times more efficient than traditional voice controls at recognizing the wake word "Hey Mercedes."
Mercedes said, “Although neuromorphic computing is still in its infancy, such systems will soon be on the market within a few years. When applied at scale throughout vehicles, they have the potential to radically reduce the amount of effort required to run the latest AI technologies. power consumption."
BrainChip's CMO Jerome Nadel said: "Mercedes is focused on big issues like battery management and transmission, but every milliwatt counts, and when you think about energy efficiency, even the most basic reasoning, like finding keywords, matters. important."
A typical car could have as many as 70 different sensors by 2022, Nadel said. For cockpit applications, these sensors can enable face detection, gaze assessment, emotion classification, and more.
He said: “From a system architecture perspective, we can do a 1:1 approach where there is a sensor that will do some preprocessing and then the data will be forwarded. The AI will do inference near the sensor...it will Instead of the full array of data from sensors, the inference metadata is passed forward.”
The idea is to minimize the size and complexity of packets sent to AI accelerators, while reducing latency and minimizing power consumption. Each vehicle will likely have 70 Akida chips or sensors with Akida technology, each of which will be "low-cost parts that won't notice them at all," Nadel said. He noted that attention needs to be paid to the BOM of all these sensors.
BrainChip expects to have its neuromorphic processor next to every sensor on the vehicle
Going forward, Nadel said, neuromorphic processing will also be used in ADAS and autonomous driving systems. This has the potential to reduce the need for other types of power-hungry AI accelerators.
"If every sensor could have Akida configured on one or two nodes, it would do adequate inference, and the data passed would be an order of magnitude less, because that would be inference metadata...that would affect the servers you need," he said. power."
BrainChip's Akida chip accelerates SNNs (spike neural networks) and CNNs (by converting to SNNs). It's not tailored for any specific use case or sensor, so it can be paired with visual sensing for face recognition or people detection, or other audio applications like speaker ID. BrainChip also demonstrated Akida's smell and taste sensors, although it's hard to imagine how these could be used in cars (perhaps to detect air pollution or fuel quality through smell and taste).
Akida is set up to handle SNNs or deep learning CNNs that have been transformed into SNNs. Unlike the native spike network, the transformed CNN preserves some spike-level information, so it may require 2 or 4 bits of computation. However, this approach allows exploiting the properties of CNNs, including their ability to extract features from large datasets. Both types of networks can be updated at the edge using STDP. In the case of Mercedes-Benz, this might mean retraining the network after deployment to discover more or different keywords.
According to Autocar, Mercedes-Benz confirmed that "many innovations" from the EQXX concept car, including "specific components and technologies," will be used in the production model. There's no word yet on whether new Mercedes-Benz models will feature artificial brains."
I do hope you read the whole post and not just the orange text- (
x1000)
My opinion only DYOR
FF
AKIDA BALLISTA
- What’s royalty R&D revenue likely to be assuming test chips are built, tested and refined with BRN IP along the way? Assuming 2 years before commercial car sales and in mass production.
- If there are existing customers that are testing Akida IP now, wouldn’t there be some amount of royalty revenue for their R&D phase?
Info on licensing deals structure would be beneficial with the view we all see the Akida tech infiltration into the edge industry, however no royalty revenue. Would I be wrong in assuming that royalty revenue does not happen until there’s a commercial sale of a product?
Dozzaman1977
Regular
Another year just about done and dusted .
Wishing all the contributors on here, the Brainchip Management and employees, and my favourite IT girl Anastasi a happy and safe Christmas and a prosperous new year. Looking forward to a successful 2023.Just had a look at the ARM website. Its been 7 months since the Brainchip/ARM AI partnership was announced, and Brainchip is still the first company that comes up when you click on relevance and the industries/ tech listed below.
Industry and Technologies
- Internet of Things (IoT)
- Artificial intelligence
- Automotive
- Industrial
- Smart Homes
- Wearables
- Healthcare
- Mobile computing
- Functional Safety
- Security
- Smart Cities
- Sustainability
- Avionics
- Industrial IoT (IIOT)
- Mobile
They also have a new case study article on their site which links back to Brainchip site, i dont know if this is new but its below, a good read!!!!
What is the Akida Event Domain Neural Processor?
By Brien M. Posey
Previous generations of artificial intelligence and machine learning chips were useful, but their time is rapidly running out. The constraints on power and bandwidth imposed by edge devices mean that it’s time for a new paradigm, a new design that fulfills the promise of AI and ML at the edge. It’s time for the BrainChip Akida processor.
Although machine learning (ML) has existed for some time, the technology is still evolving. The BrainChip Akida processor overcomes many of the challenges that have long been associated with ML, particularly regarding deep learning neural networks.
The Evolving Artificial Intelligence Model
Before we jump into the guts of how BrainChip’s Akida Neural Processor works, it’s important to understand what it does and how it will transform artificial intelligence (AI).
BrainChip has focused the past 15 years on evolving the art of AI to overcome the shortcomings of today’s deep learning technologies.
In utilizing AI, corporations are processing exabytes of data to extract information for a wide range of purposes, including surveillance and security, consumer behavior, advertising, language processing, video analysis, financial predictions, and many more.
These applications have spawned a monumental market for both software and hardware and have transformed nearly every industry. There can be no argument that the breakthroughs have been extraordinary, and the growth rate of applications has been explosive. Yet, this has represented, to date, only the tip of the iceberg for AI capabilities. With the expansion of the Internet of Things (IoT) comes a parallel expansion of AI into everyday appliances in the home, office, and industry.
Today’s systems, although impressive, are merely first- and second-generation solutions relying on over-simplified and limited representations of how nature’s intelligence—the brain—really functions. Today’s systems have limited to no ability to learn without huge amounts of labeled data and many repetitions of deep learning and training cycles.
Deep learning systems recognize an object by statistically determining the number of features that match an im- age—features that were extracted from millions of images that it was previously trained on. These systems use several orders of magnitude more power than the brain.
Currently, ML systems rely on power-hungry CPUs and GPUs physically located in large data centers to ingest, process, and retrain data which is generated in a highly distributed fashion all over the globe. This drives an ever-growing and insatiable need for communication bandwidth to move the data to the data center.
BrainChip deems this model ripe for a revolution, and that AI needs to evolve to support intelligence at the location where the data is generated or sensed. It believes that the future of AI lies in the ability to achieve ultra-low power processing as data is being interpreted and transformed into information, and that continuous learning needs to be autonomous and continuous.
BrainChip has developed the Akida Neural Processor to solve the problems inherent in moving AI out of the data center and to the location where data is created: the edge, of which a large segment is often referred to as IoT.
This has several advantages. The most important one is privacy and a sharp reduction of dependency on the Internet. You would not want a device in your home that shoots images up to the internet, where they can be hacked and viewed by anyone—but a warning sent over the internet to your phone that an intruder or other unrecognized person enters your home would be an advantage.
What Is a Neural Network?
A neural network lies at the core of all AI. As its name implies, a neural network is modeled on the principles of neural processing—the cells that make up the brain network. However, today’s technology (deep learning) is, at best, only loosely related to how the brain functions.
Neuromorphic computing is a field of computer science based on the study of the brain, and how the function of neural brain cells can be utilized in silicon to perform cognitive computing.
BrainChip has developed the Akida neural processor utilizing the fundamental concepts of Neuromorphic computing, in combination with the advances made in deep learning.
The Akida neural processor is a flexible, self-contained, event-based processor that can run today’s most common neural networks, Convolutional Neural Networks in event-based hardware, as well as the next-generation Spiking Neural Networks.
The Akida neural processor is ultra-low power, requires only internal memory, and can perform inference and instantaneous learning within an AI solution. It represents the third generation of neural networking, and the next step in the evolution of AI.
What Is the Akida Neural Processor?
What makes Akida so different from first and second-generation neural processors? Unlike those legacy processors, the Akida processor is event-based, which means it processes data in the form of events.
Events are the occurrences where things happen, such as a change of contrast in a picture, or a change of color. The human visual system encodes images in the same way.
An event is expressed as a short burst of energy. In Akida, the burst can have a value that indicates neural behavior. No events are generated where zero values occur in the network—for instance, where blank areas occur in a picture—making Akida’s processing scheme intrinsically sparse. In other words, if no events exist or are generated, no processing needs to occur.
The Akida processor uses an encoding scheme called “rank coding,” in which information is expressed as the time and place it occurs. Akida is not programmed in the traditional sense—it consists of physical neuron and synapse circuits configured for a specific task, defining the dimensions and types of network layers.
The entire network is mapped to physical neuron and synapse circuits on the chip. Synapses store weight values and are connected to neurons, which integrate the weight
values when they’re released by an incoming event. Each neuron can have thousands of synapses. Each reconfigurable core can contain the equivalent of tens of thousands of neurons.
Power is consumed only when inputs to a neuron exceed the predetermined threshold and generate an action potential to be processed by subsequent layers in the network.
No output event is generated when the sum of synaptic inputs is zero or negative, significantly reducing the processing requirements in all the following layers. The neural and synapse functions in the Akida neural fabric are entirely implemented in digital hardware. Therefore, no computer code is running within any of the neural cores, resulting in a very low overall power consumption of approximately 3 pico-Joules per synaptic operation (in 28nm technology).
As stated previously, the Akida neural processor is a complete, self-contained, purpose-built neural processor. This is in stark contrast with traditional solutions, which utilize a CPU to run the neural network algorithm, a deep learning accelerator (such as a GPU) to perform, multiply, and ac- cumulate mathematical operations (MACs), and memory to store network parameters (see Figure 1).
Figure 1: A traditional Neural Processing solution using a CPU, Deep Learning Accelerator, and external memory vs. the Akida solution as a fully integrated, purpose-built neural processor
By integrating all the required elements into a consolidated, purpose-built neural processor, the Akida processor eliminates the excess power consumption associated with the interaction and communication between the three separate elements, as well as minimizing the physical footprint.
How Does the Akida Event-Based Neural Processor Work at Ultra-Low Power?
As described earlier, the Akida neural processor is differentiated from other solutions by two major factors:
- It is a complete, fully integrated, purpose-built neural processor
- It is an event-based processor
By fully integrating the neural network control, the parameter memory, and the neuronal mathematics, the Akida neural processor eliminates significant compute and data I/O power overhead. This factor alone can save multiple watts of unnecessary power consumption.
The Akida event processor is constructed from event-based neurons, which work in a manner much more like the way the brain operates than the “perceptron” style neurons used in today’s deep learning neural network hardware solutions.
In the Akida event domain processor, “events” or “spikes” indicate the presence of information, eliminating wasted effort. This is a core principle.
All neural networks consist of some form of simulation or emulation of “neural cells” and the weighted connections between those cells. The connections between neural cells have memory, store a value, and are called “synapses” (see Figure 2).
In the end, only information is processed and consumes energy. In the Akida event domain processor, “events” or “spikes” indicate useful information, eliminating wasted effort. This is a core principle.
Figure 2: Biological neurons are cells that communicate with one another and store information in synapses. A neuron can have hundreds of thousands of synapses, the content of which is recalled by sensory input action potentials. The neuron integrates the values of active synapses and generates an action potential output when the integrated value reaches or exceeds a threshold value. Artificial Neural Networks model similar behavior.
This is fundamentally different from the function of artificial neurons in Deep Learning Convolutional Neural Network hardware implementations, which process all information without discerning whether it contains valuable information or not.
Every pixel in an image is converted to data and processed, whether it contains any information or not.
To illustrate how this works, consider an extreme case. You could give a “standard” Convolutional Neural Network a blank page to process, and it will take every pixel and process it through millions of multiply-accumulate instructions to find out that the page is blank.
The Akida event-based processing method works like how the human brain would process a blank page: since there are no lines or colors on the page, it receives no events, so it does not need to process anything. It is this reduction of data that must be processed, known as “sparsity,” that leads to significant power savings.
Combined with state-of-the-art circuit architecture and implementation, the Akida neural processor has demonstrated power reduction of up to 10x over the most power-efficient alternatives. In addition, power savings are up to 1,000x compared with standard data center architectures. For AI applications at the edge, where information is created, power budgets can be limited to micro-watts or milli-watts. The Akida platform, with its ultra-low power consumption, meets the power budget requirements for these applications.
Figure 3: the evolution of training and learning
How Does Akida Learn?
Training is an extremely time- and energy-consuming process in today’s deep learning solutions, as it requires a tremendous amount of hand-labeled input data (datasets) and extremely powerful compute infrastructures to train a neural network.
All this has resulted in very useful and powerful solutions, but one with a significant drawback—once an AI solution is trained, it’s not easy for the system to learn new things without going through the entire training process again, this time including the new information.
The Akida processor offers a solution that can take a deep learned neural network, run inference on that network, and then can learn things without going through retraining. Figure 3 shows the evolution of training and learning. Akida represents the third generation of AI; whereby instantaneous learning is enabled.
In native learning mode, event domain neurons learn quickly through a biological process known as Spike Time Dependent Plasticity (STDP), in which synapses that match an activation pattern are reinforced. BrainChip is utilizing a naturally homeostatic form of STDP learning in which neurons don’t saturate or switch off completely.
STDP is possible because of the event-based processing method used by the Akida processor and can be applied to incremental learning and one-shot or multi-shot learning.
The next generation of AI solutions will evolve by utilizing the concepts learned from studying the biological brain. The BrainChip Akida neural processor embodies this evolution by incorporating event domain neural processors in a practical and commercially viable way.
The ability to move AI to the edge depends upon a fundamental shift in how the core of AI solutions is built. The Akida neural processor provides the means. It’s a self-contained, efficient neural processor that’s event-based for maximum efficiency, ultra-low power consumption, and real-time learning. Instantaneous learning reduces the need for retraining, and its processing capabilities eliminate the need for constant internet connectivity.
The BrainChip Akida Neural processor is the next generation in AI that will enable the edge. It overcomes the limitations of legacy AI chips that require too much power and bandwidth to handle the needs of today’s applications, and moves the technology forward in a significant leap, allowing AI to do more with less. Akida’s time has come.
Top 10 Edge Computing Stories of 2022 at EdgeIR | Edge Industry Review
The world of edge computing is rapidly evolving, with new companies and technologies emerging each year to enable the latest developments in this field.
Kachoo
Regular
I will add another story for some to understand the working of big corporations and how size dies not mean they are efficient well run or waste money.
I was given a work vehicle and fuel card to do my job. I lived 1500 km from my place of origin. Any way I was doing a job close to my old home 250 km away. I had to fuel up the truck so I did. Finished the job then drove back over a few days.
About a week later my manager calls me in and ask As if I was using the company fuel card to drive home shocked I said no way why. He then says the country manager 3 levels above him called to ask As he saw me put back then 80 dollar of fuel in the company truck and saw my file that that was close to my family home.
I said no look I was doing a job near there he checked what jobs I did and said okay good. That was it. Now this same country manager ordered over 10 million dollars of equipment thinking it was a deal back then from China only to later learn that it was not compliant in NA but hey that's swept under the rug.
So in the end big money will waste and throw money on there ideas until it's exhausted only to conform and steam ahead later on.
Relevance to BRN
If Akida is the best technology to achieve the purpose it's set to do you will see it adopted by most of manufactures they will have no choice but to buy in and tweak things their way all go out to the pasture in that market. We say Apple is huge right well if they don't keep racing the will fall and fall quick being a leader does not give you time to relax.
So like many say it's great to be a share holder!
Looking forward to 2023!
Bear markets eventually end and there are stars that shine even in a down market don't forget. The world has not stopped spinning.
I was given a work vehicle and fuel card to do my job. I lived 1500 km from my place of origin. Any way I was doing a job close to my old home 250 km away. I had to fuel up the truck so I did. Finished the job then drove back over a few days.
About a week later my manager calls me in and ask As if I was using the company fuel card to drive home shocked I said no way why. He then says the country manager 3 levels above him called to ask As he saw me put back then 80 dollar of fuel in the company truck and saw my file that that was close to my family home.
I said no look I was doing a job near there he checked what jobs I did and said okay good. That was it. Now this same country manager ordered over 10 million dollars of equipment thinking it was a deal back then from China only to later learn that it was not compliant in NA but hey that's swept under the rug.
So in the end big money will waste and throw money on there ideas until it's exhausted only to conform and steam ahead later on.
Relevance to BRN
If Akida is the best technology to achieve the purpose it's set to do you will see it adopted by most of manufactures they will have no choice but to buy in and tweak things their way all go out to the pasture in that market. We say Apple is huge right well if they don't keep racing the will fall and fall quick being a leader does not give you time to relax.
So like many say it's great to be a share holder!
Looking forward to 2023!
Bear markets eventually end and there are stars that shine even in a down market don't forget. The world has not stopped spinning.
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