That's a very interesting comment...
A bit like buying a red Porsche but at a corporate level.
A bit like buying a red Porsche but at a corporate level.
Hi @wilzy123How many ubiquitous things can you have
View attachment 20875
Edit: from an article on their LinkedIn page
Geez, I reckon that's a bit ubiquitous.
Yak, I have been reading lots of your post before and couldn’t find your post for a while then today i tried again. I noted your frustration around the shorting on brn. Wonder if it is ok to share how much shorts have been closed to date.
Your comment is offensive and your language is repugnant!!
Fwiw my previous musings about the M33 product possibility. Who knows.Just a recent article from Germany I hadn't seen so thought post as references us.
Couple of bolds about halfway down but it is the second one I highlighted that was...errr...interesting assumption or statement of fact
Is a google translate copy.
Maschinelles Lernen auf Mikrocontrollern an der Edge
(Bild: Renesas Electronics) In „intelligenten“ Edge-Anwendungen kommt bislang Inferenz zum Einsatz: Das Anwenden von vorab mit viel Rechenleistung trainierter KI-Algorithmen. Neuromorphe, also an biologische Hirne angelehnte IP-Blöcke ermöglichen erstmals selbstlernende Edge-Anwendungen.www-elektronikpraxis-de.translate.goog
Neuromorphic components for TinyML Machine learning on microcontrollers at the edge
10/28/2022 By Michael Eckstein
So far, inference has been used in "intelligent" edge applications: the application of AI algorithms that have been trained in advance with a lot of computing power. Neuromorphic IP blocks, i.e. based on biological brains, enable self-learning edge applications for the first time.
Using neuroscientific terms such as neurons and synapses is actually inadequate to describe most neural networks - after all, they are a long way from how the human brain works. There is now a new, third generation: These spiking neural networks (SNN) are based on hardware that allows processes to be carried out similar to those in the human brain - hence the term neuromorphic architecture.
Spiking Neural Networks
SNNs are artificial neural networks (ANN) that are more closely modeled on the human brain than second-generation NNs. The main difference is that SNNs are spatio-temporal NNs, ie they take time into account in their operation. SNNs work with discrete spikes, which are determined by a differential equation and represent various biological processes.
The critical process is the discharge of the neuron after reaching the membrane potential (“discharge threshold”), which occurs through the discharge of spikes in this neuron at specific times. Similarly, a brain consists of an average of 86 billion computing units, the neurons, which receive input from other neurons via dendrites. Once the inputs exceed a certain threshold, the neuron fires, sending an electrical impulse across a synapse. Synaptic weight controls the intensity of the impulse sent to the next neuron.
In contrast to other artificial neural networks, in SNNs the neurons are fired asynchronously in different layers of the network and arrive at different times, while the information traditionally propagates through the layers depending on the system clock. The spatiotemporal nature of SNNs together with the discontinuous nature of the spikes means that the models can be more sparsely distributed. The neurons only connect to relevant neurons and use time as a variable. This allows the information to be encoded more densely compared to the traditional binary encoding of ANNs. This results in SNNs being more computationally powerful and efficient. The asynchronous behavior of SNNs together with the need to run differential equations, is very computationally intensive for conventional hardware. This is where neuromorphic architecture comes into play.
Neuromorphic Architecture
The neuromorphic architecture consists of neurons and synapses and differs significantly from the von Neuman architecture. In neuromorphic computers, the processing and storage of the data takes place in the same region - and thus avoids a weakness of the von Neumann architecture: Here, the data to be processed must first be loaded from the memory into the processing units. The storage interface is a bottleneck that slows down data throughput. In addition, the neuromorphic architecture supports SNNs and accepts spikes as inputs, so the information can be encoded in terms of spike impact time, size, and shape.
Key characteristics of neuromorphic systems include their inherent scalability, event-driven computation, and stochastics. Since the neurons only trigger when their trigger threshold is exceeded, the neuromorphic architecture scores with extremely low power consumption. This is usually orders of magnitude lower than in conventional computer systems. Neuromorphic components therefore have the potential to play a major role in the coming age of edge and endpoint AI.
Sheer Analytics & Insights estimates that the global neuromorphic computing market will reach $780 million by 2028 with a compound annual growth rate of 50.3 percent [1]. Mordor Intelligence projects that the market will reach $366 million by 2026, with a compound annual growth rate of 47.4 percent [2]. Further market research results can be found on the Internet, which suggest a similar increase. Market research companies predict that various sectors such as industrial, automotive, mobile communications and medicine will use neuromorphic systems for a variety of applications.
TinyML (Tiny Machine Learning) is about running ML and NNs on memory/processor limited components such as microcontrollers (MCU). Therefore, it makes sense to integrate a neuromorphic core for TinyML use cases. Neuromorphic components are event-based processors that work with non-zero events. Event-based convolution and dot products are significantly less computationally intensive since zeros are not processed. Performance further improves as the number of zeros in the filter channels or cores increases. This, along with zero-centered wake-up features like Relu, gives the event-based processors the inherent low-power wake-up characteristic, thereby reducing the effective MAC requirements.
Neuromorphic TinyML: Learning at the Edge
Because neuromorphic systems handle spikes, 1-, 2-, and 4-bit quantizations can also be used with ANNs, compared to the conventional 8-bit quantization. Because SNNs are built into hardware, neuromorphic devices (such as Brainchip's Akida) have the unique ability of on-edge learning. This is not possible with conventional components as they only simulate a neural network with Von Neumann architecture. As a result, on-edge learning is computationally intensive and has a high memory footprint that exceeds the system budget of a TinyML system.
Additionally, integers for training an NN model do not provide enough range to train a model accurately. Therefore, training with 8-bit on traditional architectures is currently not possible. Currently, in the traditional architectures, a few on-edge learning implementations using machine learning algorithms (autocoder, decision trees) have reached a production stage for simple real-time analysis use cases, while NNs are still in the development phase. The advantages of using neuromorphic components and SNNs at the endpoint can be summarized as follows:
Recognizing the enormous potential of neuromorphic systems and SNNs, Renesas licensed a core from Brainchip [3], the world's first commercial neuromorphic IP manufacturer. At the lower end of the performance scale there is now an MCU with an M33 processor from Arm and a Spiking Neural Network with a licensed Brainchip core including the appropriate software.
- extremely low power consumption (milli- to microjoules per inference)
- Less need for MACs compared to traditional NNs
- Less use of parameter memory compared to conventional NNs
- On-Edge Learning Capabilities
Neuromorphic TinyML Use Cases
All in all, microcontrollers with neuromorphic cores can convince in use cases across the industry with their special features of on-edge learning:
The balance between the extremely low power consumption of neuromorphic endpoint systems and the improved processing power makes them suitable for applications with prolonged battery operation. Algorithms can be executed that are not possible on other components with low power consumption due to their limited computing power. Conversely, high-end applications that offer similar computing power are too energy-intensive.
- For anomaly detection applications in existing industrial plants where using the cloud to train a model is inefficient, adding an AI endpoint device at the engine and training at the edge would allow for easy scalability. Because the aging of the systems differs from machine to machine, even if it is the same model.
- In robotics, over time, the joints of robotic arms tend to wear out, misalign, and no longer function as required. Retuning controls at the edge without human intervention reduces the need to call in a professional, reduces downtime, and saves time and money.
- For facial recognition applications, a new user would need to add their face to the data set and retrain the model in the cloud. With a few snapshots of a person's face, the neuromorphic component can identify the end user through on-edge learning. In this way, the user's data can be secured on the system and an optimal user experience can be guaranteed. This can be used in cars where different drivers have different preferences in terms of seating position, climate control, etc.
- For keyword detection applications, adding additional words that you want the device to recognize at the edge is essential. This can be used in biometric applications where a person adds a 'codeword' that they wish to keep safe on the device.
Possible use cases include: smartwatches that monitor and process the data at the endpoint and send only relevant information to the cloud; smart camera sensors to detect people to execute a logical command. For example, automatic door opening when a person approaches, as current technology is based on proximity sensors; Areas without connectivity or charging facilities, e.g. B. in forests to intelligently track animals or to monitor pipelines under the sea for possible cracks using real-time vibration, image and sound data; and for infrastructure monitoring, where a neuromorphic MCU can be used to continuously monitor (via images) movement, vibration and structural changes in bridges to detect potential damage. (me)
thestockexchange.com.au
thestockexchange.com.au
It's huge.
Hi @FullmoonfeverJust a recent article from Germany I hadn't seen so thought post as references us.
Couple of bolds about halfway down but it is the second one I highlighted that was...errr...interesting assumption or statement of fact
Is a google translate copy.
Maschinelles Lernen auf Mikrocontrollern an der Edge
(Bild: Renesas Electronics) In „intelligenten“ Edge-Anwendungen kommt bislang Inferenz zum Einsatz: Das Anwenden von vorab mit viel Rechenleistung trainierter KI-Algorithmen. Neuromorphe, also an biologische Hirne angelehnte IP-Blöcke ermöglichen erstmals selbstlernende Edge-Anwendungen.
Neuromorphic components for TinyML Machine learning on microcontrollers at the edge
10/28/2022 By Michael Eckstein
So far, inference has been used in "intelligent" edge applications: the application of AI algorithms that have been trained in advance with a lot of computing power. Neuromorphic IP blocks, i.e. based on biological brains, enable self-learning edge applications for the first time.
Using neuroscientific terms such as neurons and synapses is actually inadequate to describe most neural networks - after all, they are a long way from how the human brain works. There is now a new, third generation: These spiking neural networks (SNN) are based on hardware that allows processes to be carried out similar to those in the human brain - hence the term neuromorphic architecture.
Spiking Neural Networks
SNNs are artificial neural networks (ANN) that are more closely modeled on the human brain than second-generation NNs. The main difference is that SNNs are spatio-temporal NNs, ie they take time into account in their operation. SNNs work with discrete spikes, which are determined by a differential equation and represent various biological processes.
The critical process is the discharge of the neuron after reaching the membrane potential (“discharge threshold”), which occurs through the discharge of spikes in this neuron at specific times. Similarly, a brain consists of an average of 86 billion computing units, the neurons, which receive input from other neurons via dendrites. Once the inputs exceed a certain threshold, the neuron fires, sending an electrical impulse across a synapse. Synaptic weight controls the intensity of the impulse sent to the next neuron.
In contrast to other artificial neural networks, in SNNs the neurons are fired asynchronously in different layers of the network and arrive at different times, while the information traditionally propagates through the layers depending on the system clock. The spatiotemporal nature of SNNs together with the discontinuous nature of the spikes means that the models can be more sparsely distributed. The neurons only connect to relevant neurons and use time as a variable. This allows the information to be encoded more densely compared to the traditional binary encoding of ANNs. This results in SNNs being more computationally powerful and efficient. The asynchronous behavior of SNNs together with the need to run differential equations, is very computationally intensive for conventional hardware. This is where neuromorphic architecture comes into play.
Neuromorphic Architecture
The neuromorphic architecture consists of neurons and synapses and differs significantly from the von Neuman architecture. In neuromorphic computers, the processing and storage of the data takes place in the same region - and thus avoids a weakness of the von Neumann architecture: Here, the data to be processed must first be loaded from the memory into the processing units. The storage interface is a bottleneck that slows down data throughput. In addition, the neuromorphic architecture supports SNNs and accepts spikes as inputs, so the information can be encoded in terms of spike impact time, size, and shape.
Key characteristics of neuromorphic systems include their inherent scalability, event-driven computation, and stochastics. Since the neurons only trigger when their trigger threshold is exceeded, the neuromorphic architecture scores with extremely low power consumption. This is usually orders of magnitude lower than in conventional computer systems. Neuromorphic components therefore have the potential to play a major role in the coming age of edge and endpoint AI.
Sheer Analytics & Insights estimates that the global neuromorphic computing market will reach $780 million by 2028 with a compound annual growth rate of 50.3 percent [1]. Mordor Intelligence projects that the market will reach $366 million by 2026, with a compound annual growth rate of 47.4 percent [2]. Further market research results can be found on the Internet, which suggest a similar increase. Market research companies predict that various sectors such as industrial, automotive, mobile communications and medicine will use neuromorphic systems for a variety of applications.
TinyML (Tiny Machine Learning) is about running ML and NNs on memory/processor limited components such as microcontrollers (MCU). Therefore, it makes sense to integrate a neuromorphic core for TinyML use cases. Neuromorphic components are event-based processors that work with non-zero events. Event-based convolution and dot products are significantly less computationally intensive since zeros are not processed. Performance further improves as the number of zeros in the filter channels or cores increases. This, along with zero-centered wake-up features like Relu, gives the event-based processors the inherent low-power wake-up characteristic, thereby reducing the effective MAC requirements.
Neuromorphic TinyML: Learning at the Edge
Because neuromorphic systems handle spikes, 1-, 2-, and 4-bit quantizations can also be used with ANNs, compared to the conventional 8-bit quantization. Because SNNs are built into hardware, neuromorphic devices (such as Brainchip's Akida) have the unique ability of on-edge learning. This is not possible with conventional components as they only simulate a neural network with Von Neumann architecture. As a result, on-edge learning is computationally intensive and has a high memory footprint that exceeds the system budget of a TinyML system.
Additionally, integers for training an NN model do not provide enough range to train a model accurately. Therefore, training with 8-bit on traditional architectures is currently not possible. Currently, in the traditional architectures, a few on-edge learning implementations using machine learning algorithms (autocoder, decision trees) have reached a production stage for simple real-time analysis use cases, while NNs are still in the development phase. The advantages of using neuromorphic components and SNNs at the endpoint can be summarized as follows:
Recognizing the enormous potential of neuromorphic systems and SNNs, Renesas licensed a core from Brainchip [3], the world's first commercial neuromorphic IP manufacturer. At the lower end of the performance scale there is now an MCU with an M33 processor from Arm and a Spiking Neural Network with a licensed Brainchip core including the appropriate software.
- extremely low power consumption (milli- to microjoules per inference)
- Less need for MACs compared to traditional NNs
- Less use of parameter memory compared to conventional NNs
- On-Edge Learning Capabilities
Neuromorphic TinyML Use Cases
All in all, microcontrollers with neuromorphic cores can convince in use cases across the industry with their special features of on-edge learning:
The balance between the extremely low power consumption of neuromorphic endpoint systems and the improved processing power makes them suitable for applications with prolonged battery operation. Algorithms can be executed that are not possible on other components with low power consumption due to their limited computing power. Conversely, high-end applications that offer similar computing power are too energy-intensive.
- For anomaly detection applications in existing industrial plants where using the cloud to train a model is inefficient, adding an AI endpoint device at the engine and training at the edge would allow for easy scalability. Because the aging of the systems differs from machine to machine, even if it is the same model.
- In robotics, over time, the joints of robotic arms tend to wear out, misalign, and no longer function as required. Retuning controls at the edge without human intervention reduces the need to call in a professional, reduces downtime, and saves time and money.
- For facial recognition applications, a new user would need to add their face to the data set and retrain the model in the cloud. With a few snapshots of a person's face, the neuromorphic component can identify the end user through on-edge learning. In this way, the user's data can be secured on the system and an optimal user experience can be guaranteed. This can be used in cars where different drivers have different preferences in terms of seating position, climate control, etc.
- For keyword detection applications, adding additional words that you want the device to recognize at the edge is essential. This can be used in biometric applications where a person adds a 'codeword' that they wish to keep safe on the device.
Possible use cases include: smartwatches that monitor and process the data at the endpoint and send only relevant information to the cloud; smart camera sensors to detect people to execute a logical command. For example, automatic door opening when a person approaches, as current technology is based on proximity sensors; Areas without connectivity or charging facilities, e.g. B. in forests to intelligently track animals or to monitor pipelines under the sea for possible cracks using real-time vibration, image and sound data; and for infrastructure monitoring, where a neuromorphic MCU can be used to continuously monitor (via images) movement, vibration and structural changes in bridges to detect potential damage. (me)
Not just academic brillianceI must be getting old. I got tired just reading this remarkable individuals accolades.
Why did she choose to join the Board of MF's 6th best pick of the last 10 years.
Makes absolutely no sense unless MF has been wrong all this time.
Surely not.
Honours & Awards
NACD Directorship 100
National Association of Corporate Directors (NACD)
Aug 2021Most Influential Corporate Board Directors
Women Inc
Dec 2019Asian Hall of Fame
Robert Chinn Foundation
2017
The first engineer to be inductedCastle Lectures
United States Military Academy (West Point)
2011Hall of Fame
Career Communications Group
2010Humanitarian Award
Vietnamese American Heritage Foundation
2008Outstanding Asian American & Pacific Islander: LEADERSHIP
LEAP
2008Outstanding Civic Leadership
California State Senate
2008America's Top 15 Women in Business
Forte Foundation & PINK Magazine
2007Corporate Champion Award
The University of Texas in Austin
2007Who's Who in the World
The Chronicle of Human Achievement 21st Century Edition - Marquis
2007Women Who Changed our World
Women of Colour Magazine - Special Collector's Edition
2007Women of Vision: LEADERSHIP
Anita Borg Institute for Women and Technology
2007Golden Torch Award for Exemplary Citizenship
Vietnamese American National Gala (VANG)
2006Science Spectrum Trailblazer
Science Spectrum Magazine
2006Stars Among Us
Alliance for Multicultural Community Services
2006Women Worth Watching
Profiles in Diversity Journal
2006Asian American Engineer of the Year
Chinese Institute of Engineers, USA
2005Outstanding Young Texas Exe Award
The University of Texas at Austin
2003Outstanding Young Graduate Award
The University of Texas at Austin, Cockrell College of Engineering
2003Houston's Leader
Centre for Houston's Future
2002National Technologist of the Year
Career Communications Group
2002Texas State Recognition for Leadership
Governor of Texas
2002Congressional Recognition for Civic Leadership
United States Congress
2001Times People
EE Times
2001WITI Hall of Fame
Women in Technology International
2001Women on the Move
Texas Executive Women
2001Top 20 Houston Women in Technology
The Association for Women in Computing
2000
