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Do you think the IRS etc haven’t arrested him is because of his appeals? I’m thinking that must be why. He was found guilty of fraud, embezzlement and theft in civil court. It seems to me a prosecutor should charge him criminally now. Or do they have to wait for all of his appeals to be over with?
Jedi yep. If he gets a whole new trial and looses I think he could start the appeal process all over again on some bogus technicality to Dallas court of appeals and then to Texas Supreme Court of appeals again. Wash rinse repeat . if that happens we’ve all been royally screwed. A whole bunch of us will be dead. Probably including Wade as he’s getting up there. 78 is it?But our BoD are no spring chickens either and ya got to think they want this done yesterday so they can retire too Let’s all hope this is over soon. 😩🤞
IMO. We should hear yes or no by end of this month from the Texas Supreme Court on whether or not they will give Wade a new trial. We wait.
In a recent study, a team of researchers from Hewlett-Packard Laboratories in Palo Alto, California, have fabricated and demonstrated a hybrid memristor/transistor circuit for the first time. The team demonstrated conditional programming of a nanomemristor by the hybrid circuit, showing that the same elements in a circuit can be configured to act as logic, signal routing, and memory. By routing a logic operation’s output signal back onto a memristor, the circuit could even reconfigure itself, opening the doors to a variety of self-programming circuits.
“It actually takes at least a dozen transistors to mimic the electrical properties of a single memristor,” Stan Williams of HP told PhysOrg.com. “Thus, for circuits that require some type of latching or other function performed by a memristor, it is at least conceivable for a designer to replace several active transistors with one passive memristor, which is much smaller than a single transistor. This maintains the capability of the chip while decreasing the number of transistors, which saves both silicon area and power. Thus, it may be possible to continue the equivalent of Moore's law for a couple of generations not by making transistors smaller, but by replacing some subset of them with memristors.”
All good reads
Memristor
Memristor (pronounced /'m?mr?st?r/; a portmanteau of "memory resistor") is a passive two-terminal electrical component in which there is a functional relationship between electric charge and magnetic flux linkage. When current flows in one direction through the device, the electrical resistance increases; and when current flows in the opposite direction, the resistance decreases. When the current is stopped, the component retains the last resistance that it had, and when the flow of charge starts again, the resistance of the circuit will be what it was when it was last active. It has a regime of operation with an approximately linear charge-resistance relationship as long as the time-integral of the current stays within certain bounds.
Memristor theory was formulated and named by Leon Chua in a 1971 paper. In 2008, a team at HP Labs announced the development of a switching memristor based on a thin film of titanium dioxide. These devices are being developed for application in nanoelectronic memories, computer logic, and neuromorphic computer architectures. In October 2011, the same team announced the commercial availability of memristor technology within 18 months, as a replacement for Flash, SSD, DRAM and SRAM.
This text uses material from Wikipedia, licensed under CC BY-SA
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Hybrid memristors. Hmmmm
PhysOrg.com) -- As researchers strive to increase the density and functionality of circuit elements onto computer chips, one newer option they have is a memory resistor (or “memristor”), the fourth passive circuit element. First predicted to exist in 1971 and fabricated in 2008, memristors are two-terminal devices that change their resistance in response to the total amount of current flowing through them.
By dynamically changing the doping profile inside the memristive materials, scientists can control the current-voltage relationship of the device, thus controlling the “memristance.” Since they don’t lose their state when the electrical power is turned off, memristors also have nonvolatile memory.
However, memristors are passive elements, meaning they cannot introduce energy into a circuit. In order to function, memristors need to be integrated into circuits that contain active elements, such as transistors, which can amplify or switch electronic signals. A circuit containing both memristors and transistors could have the advantage of providing enhanced functionality with fewer components, in turn minimizing chip area and power consumption.
In a recent study, a team of researchers from Hewlett-Packard Laboratories in Palo Alto, California, have fabricated and demonstrated a hybrid memristor/transistor circuit for the first time. The team demonstrated conditional programming of a nanomemristor by the hybrid circuit, showing that the same elements in a circuit can be configured to act as logic, signal routing, and memory. By routing a logic operation’s output signal back onto a memristor, the circuit could even reconfigure itself, opening the doors to a variety of self-programming circuits.
“It actually takes at least a dozen transistors to mimic the electrical properties of a single memristor,” Stan Williams of HP told PhysOrg.com. “Thus, for circuits that require some type of latching or other function performed by a memristor, it is at least conceivable for a designer to replace several active transistors with one passive memristor, which is much smaller than a single transistor. This maintains the capability of the chip while decreasing the number of transistors, which saves both silicon area and power. Thus, it may be possible to continue the equivalent of Moore's law for a couple of generations not by making transistors smaller, but by replacing some subset of them with memristors.”
The HP team’s memristor design consisted of two sets of 21 parallel 40-nm-wide wires crossing over each other to form a crossbar array, fabricated using nanoimprint lithography. A 20-nm-thick layer of the semiconductor titanium dioxide (TiO2) was sandwiched between the horizontal and vertical nanowires, forming a memristor at the intersection of each wire pair. An array of field effect transistors surrounded the memristor crossbar array, and the memristors and transistors were connected to each other through metal traces.
Then the researchers tested the device by performing a basic logic function (AB + CD) from four voltage inputs representing the four values. The operations were performed on two different rows of the memristor crossbar, and the results were routed through the transistors, which amplified the signals and fed the corresponding signal back to the memristor crossbars for programming purposes. In other words, the output signal from the simple logic function of the memristor circuits could be used to reprogram a memristor for a new operation.
“Self-programming is a form of learning,” Williams explained. “Thus, circuits with memristors may have the capacity to learn how to perform a task, rather than have to be programmed to do it.”
As the researchers explained, the basis of the memristor is that the resistance of the device can be changed and be remembered, which is physically manifested by the movement of positively charged oxygen vacancies, which are dopants in a semiconducting TiO2 film. A positive bias voltage can push the vacancies away from an electrode and increase the resistance, whereas a negative bias will attract the vacancies and decrease the resistance. If left alone, the programmed state will remain as it is for at least one year.
The researchers hope that this prototype of a hybrid memristor/transistor circuit will lead to further integrations of memristors with conventional CMOS circuits. In addition, the demonstration of a system that can alter its own programming could lead the way toward a variety of new architectures, such as adaptive synaptic circuits.
More information: Borghetti, Julien; Li, Zhiyong; Straznicky, Joseph; Li, Xuema; Ohlberg, Douglas A. A.; Wu, Wei; Stewart, Duncan R.; and Williams, R. Stanley. “A hybrid nanomemristor/transistor logic circuit capable of self-programming.” PNAS, February 10, 2009, vol. 106, no. 6, 1699-1703.
This article is part of The State of Science, a series featuring science stories from public radio stations across the United States. This story, by Jes Burns, was originally published by OPB.
A honey bear is probably one of the weirder things you’d see in a science lab, especially in a lab making computer parts.
“It’s just processed, store-bought honey,” said Ph.D. student Zoe Templin. “Off the shelf — a little cute bear so we can put it in photos.”
But for Templin and her colleagues at Washington State University, Vancouver, the honey is key.
“It is cheap and it is easily accessible to everyone,” said master’s student Md Mehedi Hassan Tanim.
The honey also has natural chemical properties that make it a promising foundation for a new kind of environmentally friendly computer component — one that could make computing faster and more energy efficient while reducing the impact on the environment.
“For me electronics in general produce so much toxic waste. It really is a devastating amount of electronic waste that occurs,” said Templin.
In fact, the world produces about 50 million tons of electronic waste per year. Only 20% is recycled, meaning the other 80% ends up in landfills.
Using honey instead of materials like silicon in computers would make recycling easier and less toxic, because honey breaks down in water.
“I would love to see this become a stepping stone to having electronically clean waste,” said Templin. “Actually getting organic materials out into your phone, into commercial use.”
A person in a full body PPE suit stands in a room with yellow lighting
In this video still, WSU Vancouver grad student Zoe Templin works in a cleanroom in March to fabricate a new kind of honey-based computer component. Credit: Dan Evans, OPB
Memristors
Templin and Tanim have spent a good deal of time in the University’s cleanroom fabricating electronic components called memristors.
“We generally fabricate devices at the start of the semester for like 2-3 weeks and then we test them later,” said Tanim. “I’ve fabricated like 40 to 50 samples, but there are hundreds of devices on each sample.”
The devices are extremely small so the dust-free space of the cleanroom and head-to-toe protective gear is necessary to create the pristine metal and honey layers that comprise the chips.
“We have to keep our devices as clean as they are because our honey film is so thin. Any particles that land on that film… [are] going to be a defect and it is going to affect the performance of our device,” Templin said.
She places a glass slide in a centrifuge and drips a few drops of a specially diluted honey mixture on the surface. She starts the machine, which will spin the honey into an even layer.
“It’s thinner than our hair,” she said.
They’ll make the memristors by sandwiching the honey between two metal electrodes.
Needles touch a small pane of glass.
In this video still, the WSU Vancouver researchers test the memory-building capacity their new honey-based memristor in March. Each small circle on the chip is one device. Credit: Dan Evans, OPB
Memristors are relatively new in the world of electronics. The term is a portmanteau of the words “memory” and “resistor” — both essential for computers.
The memristor’s function was first theorized in the 1960s, but no one managed to actually make one until 2008.
Its combination of memory and electricity resistance is unique in electronics, but there’s another place where this combo is quite common — in our brains.
Our brains are full of nerve cells called neurons, and we learn when our neurons create connections between each other. The points where the neurons connect are synapses.
When we hear a new piece of information, a small bridge is built across the synapse. The bridge is our memory. The more we hear the information repeated, the more robust that memory bridge becomes.
“So we have our neurons and the synapse, they process data and they store data at the same time,” said WSU Vancouver electrical engineering professor and project head Feng Zhao.
This dual function is a huge deal and helps make our brains the most efficient computer on the planet.
The team’s memristor works the same way. It builds bridges (microscopic filaments of metal atoms) through the honey when exposed to certain levels of electricity — that’s the data processing. When the electricity stops flowing, the bridge remains intact — that’s the memory.
If you reverse the flow of electricity, the memory is wiped clean.
“We are building systems of memory that are as efficient as how the brain stores memory,” said Templin.
Zhao’s lab at WSU Vancouver discovered that honey has the chemical characteristics needed to make memristors work.
“They have big molecule chains in the material. And we need those molecule chains in order for us to build up those bridges,” said Zhao.
The team also focused on honey because it’s stable and doesn’t spoil. And unlike other traditional computer materials, producing honey actually benefits the environment because of the key role bees play in pollination.
“Using honey as the synapse element is great. It’s very clever. It’s a nice advance as far as getting things to be biodegradable,” said Purdue University physicist Erica Carlson. Carlson’s research has similarly focused on finding new materials to create faster, more efficient computers, but she is not involved in the WSU Vancouver project.
A man looks at a computer monitor.
In this video still taken in March, WSU Vancouver professor Feng Zhao tests honey-based computer components called memristors that were developed in his lab. Credit: Dan Evans, OPB
A New Branch Of Computing
So if you have a computer component that mimics a brain cell, could you put a bunch of them together and get a computer that mimics a brain?
The answer is yes.
“Right now our device simulates a single neuron, a single synapse, but we want to integrate those devices together,” said Zhao. “And then this will behave more like our brain.”
This is the idea behind neuromorphic computing — a new revolution in computer design.
“We’re coming up against some hard technological issues to where we need fundamentally different forms of computation if we’re going to keep the world moving forward the way that we want to keep moving forward,” Carlson said.
Pretty much every computer we use is based on a design that’s 80 years old: The von Neumann architecture.
In this design, the part of the computer that processes information is separate from the part that stores it. And it takes time and energy for the information to travel in between.
“This conventional computing system consumes a tremendous amount of energy,” Zhao said.
Neuromorphic computers solve this bottleneck, in part, by having the processor and storage all in one place — the memristor.
“It’s a building block,” said Tanim. “By combining millions of devices like this, I can build a neuromorphic … chip, which can actually do the computation that a supercomputer can do — but 100 times faster and using 1000 times less energy.”
A person in full body PPE and wearing sunglasses looks under a fume hood.
In this video still taken in March, WSU Vancouver grad student Md Mehedi Hasan Tanim watches the progress of the sputter machine, which is used to fabricate metal electrodes for computer chips. Credit: Dan Evans, OPB
These neuromorphic computer systems are still in their infancy as far as development goes, but that could be changing with the seemingly overnight emergence of artificial intelligence.
“Right now, the way we’re doing AI is we’re using software to mimic the brain, and we’re putting that software on computer architectures that weren’t designed for it. So that’s why it’s so enormously energy intensive,” said Carlson.
It’s estimated that within four years, AI servers like ChatGPT will use as much electricity annually as countries the size of Argentina.
Neuromorphic computer systems could eventually reduce that energy use significantly.
“Now that we’re in the midst of the AI revolution, it would not surprise me if we started to see a lot more funding,” she said. “I mean, we’re trying to do something fundamentally different, and nobody knows which is going to be the best method.”
With honey-based devices, the WSU Vancouver team thinks they’re on track to help solve two problems at once.
By developing a biodegradable computer component made of honey, for a computer system that’s more energy efficient than any we’ve ever seen, the team is taking computers into a faster and cleaner new age.
“I’m actually extremely fortunate to have been raised and grown up in the Pacific Northwest, that cares about the environment to this level,” said Templin. “Where I can do research that could … truly help the environment in a way that really hasn’t been explored before.”
Memristor
Memristor (pronounced /'m?mr?st?r/; a portmanteau of "memory resistor") is a passive two-terminal electrical component in which there is a functional relationship between electric charge and magnetic flux linkage. When current flows in one direction through the device, the electrical resistance increases; and when current flows in the opposite direction, the resistance decreases. When the current is stopped, the component retains the last resistance that it had, and when the flow of charge starts again, the resistance of the circuit will be what it was when it was last active. It has a regime of operation with an approximately linear charge-resistance relationship as long as the time-integral of the current stays within certain bounds.
Memristor theory was formulated and named by Leon Chua in a 1971 paper. In 2008, a team at HP Labs announced the development of a switching memristor based on a thin film of titanium dioxide. These devices are being developed for application in nanoelectronic memories, computer logic, and neuromorphic computer architectures. In October 2011, the same team announced the commercial availability of memristor technology within 18 months, as a replacement for Flash, SSD, DRAM and SRAM.
They already put out a notice canceling Mills lost (cough cough) shares. Once the Texas Appeals court rules in our favor I’m sure they will put out a notice canceling all of Wade’s shares. It’s just a matter of time now.
He helped invent a a whole new category of tech called "software-as-a-service," where companies rent their software over the Internet, instead of install it on computers.
The concept almost died at birth. Shortly after Salesforce.com launched, the Internet bubble burst.
Benioff persisted, however, and today every major software company is chasing him.
Along the way, Benioff has become one of the most flamboyant, generous, larger-than-life personalities in the tech industry.
Me. Hmmm “software as a service” Remember our first p r about that concept about 12 ? years ago.? And Marc invented it. Looks like we’ve been following him ( the leader) for quite some time now. I’d be willing to bet he’s chomping at the bit to buy us. …. We wait.
Doc. I agree. Remember Wade was in a joint venture with Salesforce and it fell apart like all the other jv’s he was involved with way back when. . Who could do business with Wade? No one. Marc B lives here on the Big Island. He is an all around good guy. Buying fire trucks and even helicopters for our fire departments and donating a lot of land for the homeless and low income housing etc. I’m hoping Len and his men are talking to him about a new licensing agreement. Or maybe working on a deal for him to buy v ? It would be a good fit. And he’s a good guy.
I have always thought that adobe was infringing. jfyi
Jedi. I wonder why Dan deleted all of my “A LAUGHABLE PLACE” posts? I just started a new one.
Weird? Or? Did I touch a nerve? We shall see.
No time limit on Texas Supreme Court to decide if they will hear his appeal. He lost in district court and lost in Dallas court of appeals I think they can see right through this clown. If not. I loose all faith in our court system. THUMP! I’m thinking no more than a month? But only they know. Probably depends a lot on their case load.
Yep Doc. We’re covered til 2035 ! Relax. Go to the beach. Get a shave ice. Visit Akaka Falls and cool off in the rainforest.
On January 20, 2014, Vertical Computer Systems, Inc.. (the “Company”) filed a Current Report on Form 8-K (the “Original 8-K”) to, among other things, report events pertaining to its patents. The Company is filing this Current Report on Form 8-K/A to amend the Original 8-K to correct certain references concerning U.S. Patent No. 6,826,744 and certain continuation patents.
ITEM 8.01 OTHER EVENTS.
On January 14, 2015, Vertical Computer Systems, Inc., (the “Company”) received notice from the United States Patent and Trademark Office (the “USPTO”) that a continuation patent would be issued by the USPTO on February 3, 2015. The continuation patent will be issued as United States (“U.S.”) Patent No. 8,949,780 and is a continuation of U.S. Patent No. 7,716,629, which is a continuation patent of U.S. Patent No. 6,826,744.Collectively, they form the underlying patents (the “Patents”) under the Company’s SiteFlash™ and SiteFlash-derived products
In addition, the Company has retained the law firm of Davidoff Hutcher & Citron LLP on a contingency basis to act in connection with the licensing of the Patents,
Collectively, these Patents are a system and method for generating computer applications in an arbitrary object framework. The method separates content, form, and function of the computer application so that each may be accessed or modified separately. The method includes creating arbitrary objects, managing the arbitrary objects throughout their life cycle in an object library, and deploying the arbitrary objects in a design framework for use in complex computer applications.
Wade replied with an p d f of a letter from court with his wrong address on it. But ….. he or anyone and everyone is responsible for notifying the court of your correct current address. Imo. He’s toast. These Supreme Court justices will take 1 look at the judgements against him upheld by a majority of the Dallas court of appeals and say. Bam ! NEXT!
In the study, the team programmed a Bayesian neural network on an assembly of 75 crossbar arrays, each containing 1,024 memristors, complemented by CMOS periphery for in-memory computing. The experiment involved a two-layer Bayesian neural network trained to differentiate nine classes of heart arrhythmia from electrocardiogram (ECG) recordings.
The experimental results were compelling, with the memristor-based Bayesian neural network nearly matching the best performances of a software neural network in terms of both aleatoric (area under the curve of 0.91) and epistemic uncertainty evaluation (area under the curve of 0.99), albeit with a slight reduction in raw accuracy. This performance sharply contrasted conventional neural networks, which exhibit little ability to recognize unknown data and evaluate uncertainties.
That’s amazing stuff! Now add these new memristors to quantum computers , LLM’s and AI. Bam!
A stochastic process can have many outcomes, due to its randomness, and a single outcome of a stochastic process is called, among other names, a sample function or realization.[28
Doc. Yes. And my key takeaway from this article…
This performance sharply contrasted conventional neural networks, which exhibit little ability to recognize unknown data and evaluate uncertainties.
Hmmm The ability to recognize unknown data and. …. Wait for it… “evaluate uncertainties “. Or Arbitrary objects!
Like I said before. The Tec is finally catching up to our patent..
Chinese scientists unlock potential of memristor semiconductor building block that could boost artificial intelligence, self-driving cars and more
World’s first fully system-integrated memristor chip could make artificial intelligence smarter and up to 75 times more efficient, researchers say
Advances could lead to AI that is capable of more human-like learning, with implications for how smart devices and autonomous driving work
Topic | Science
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Victoria Bela
Victoria Bela
Published: 10:07pm, 11 Oct, 2023
The world’s first fully system-integrated memristor chip has been unveiled by a team of Chinese scientists who believe it could not only make artificial intelligence smarter, but also more time and energy efficient. While the semiconductor has yet to leave the lab setting, it could allow for the development of AI that is capable of more human-like learning, which could have implications for the way smart devices and autonomous driving work, according to the researchers. “Learning is highly important,” for edge intelligence devices, the research team from Tsinghua University said in their study released in the journal Science on September 15, referencing devices that process data internally with technology like AI. The advancement is the latest in a series of Chinese semiconductor innovations announced since US-imposed export controls and sanctions restricted the supply of advanced chips and chip-making equipment to the nation.
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The development of a new microchip announced last month by Huawei made a splash in the media, and called into question whether China had the expertise to advance semiconductors without US technology. China’s new memristor chip is a significant step forward in developing such technology, according to the researchers.
“Memristor-based computing technology has recently received considerable attention because of its potential to overcome the so-called ‘von Neumann bottleneck’ of conventional computing architecture,” Yury Suleymanov, an associate editor at Science, said in his editor’s summary of the paper, referring to the computational limitation set by the separation of memory and processing.
China At A Glance
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A resistor is an element of a circuit that is able to limit the flow of energy by providing resistance to electrons flowing through.
China downplays reports of Taiwanese firms helping Huawei’s semiconductor ambitions
11 Oct 2023
A memristor – a contraction for memory resistor – is able to remember the most recent value of current that was passed through it when it was turned on, meaning future resistance can depend on prior history, according to Nature Electronics. As such, it is capable of improvement-based learning, or maintaining pre-acquired knowledge when something new is learned.
This differs from transfer learning, which focuses on moving to a new set of data and can sacrifice the accuracy of prior data, according to the paper. To train artificial neural networks, which mimic how human neurons pass on data in the brain, conventional hardware requires a great deal of energy and time to move data between the computing and memory units.
The study is an important step towards future chips with high energy efficiency and extensive learning capabilities
Wu Huaqiang and Gao Bin
Memristor-based computing is able to reduce the energy required for a task by allowing the learning to occur on-chip with no external memory source.
Several studies have investigated memristors, but they still used additional external processors, the team said in their paper. The researchers produced a chip capable of conducting complete on-chip improvement learning, and proposed a learning architecture for it.
They said they were able to demonstrate on-chip learning with multiple tasks, including image classification, motion control, and audio recognition.
How artificial intelligence will redefine the future of work
20 Jun 2022
In one demonstration, the motion control of a model car designed to pursue a laser light was better at finding the light in a dark environment and would lose track of it in a brighter environment. But after implementing learning, it was able to find the light equally well in both environments.
The accuracy in the bright environment greatly increased, while accuracy in the dark environment did not decrease with the addition of the new ability. “The memristor-based neuro-inspired computing chip could facilitate the development of edge AI devices that could adapt to new scenes and users,” the researchers said in their paper.
With further research on the learning architecture, the team said it believed it could enable on-chip learning that is 75 times more energy efficient than current machines designed for AI processing.
Still, challenges remain in the research and development of such chips – in particular, engineering the chips for large-scale integration. It could take time for the technology to make it out of the laboratory, Wu Huaqiang and Gao Bin, professors at Tsinghua and leads of the research team, admitted to Chinese-language news site Science Times. “The study is an important step towards future chips with high energy efficiency and extensive learning capabilities,” the researchers said, adding they hoped their findings “will accelerate the development of future smart edge devices”
International Researchers Move the Needle on Memristor Technology
December 19, 2023 by Jake Hertz
Academics across the globe have made concurrent breakthroughs in a new form of non-volatile memory.
Memristors, or memory resistors, are an emerging form of non-volatile memory considered a viable alternative to traditional memory storage. These devices act as switches that can recall their electric state even after their power is turned off, allowing them to behave like human neurons—both computing and storing data.
Memristors are a hot research topic for their numerous unique properties, including scalability to lower process nodes, which enable smaller, faster, and more energy-efficient devices.
Cea-Leti's wafer-level memristor crossbar arrays
Cea-Leti's wafer-level memristor crossbar arrays using a prober. Image (modified) used courtesy of Cea-Leti
Now, as the demand for more sophisticated and power-efficient computing grows, memristors hold promise in various applications, including artificial intelligence, neuromorphic computing, and high-density data storage. In recent months, several international research teams have published findings about memristors that may help push this technology from lab to market.
The University of Rochester: Strain-Engineered Phase-Change Memristors
Researchers at the University of Rochester recently developed high-performance, bipolar phase-change memristors based on strain-engineered multilayer molybdenum ditelluride (MoTe2).
The strain-based memristor
The strain-based memristor. Image used courtesy of Nature
These devices, distinguished by their bipolar phase-change characteristics, represent a significant leap from conventional filamentary conduction mechanisms. The study successfully utilizes process-induced strain engineering to induce a semimetallic-to-semiconducting phase transition in MoTe2.
This transition is key to achieving the memristor's remarkable switching characteristics, including low voltages of 90 mV, high on/off ratios of 10^8, swift switching times of 5 ns, and prolonged retentions over 10^5 seconds. Such performance metrics indicate the potential of these memristors for applications in robust, fast, low-power, and scalable memory and neuromorphic computing systems.
The device's performance can also be finely tuned by varying a single process parameter: the contact metal film force, a product of film stress and thickness. This control over the device's switching voltage and on/off ratio underlines the memristor's adaptability and potential for diverse applications.
Cea-Leti: Using Memristors for Bayesian Neural Networks
In France, a group of Cea-Leti researchers announced that they have successfully integrated memristor hardware into artificial intelligence systems. Specifically, the team leveraged the inherent probabilistic nature of memristors to effectively execute the computationally intensive tasks of Bayesian neural networks.
Filamentary memristor
Filamentary memristor in the back end of the line of Cea-Leti's hybrid memristor/CMOS process. Image used courtesy of Nature
In the study, the team programmed a Bayesian neural network on an assembly of 75 crossbar arrays, each containing 1,024 memristors, complemented by CMOS periphery for in-memory computing. The experiment involved a two-layer Bayesian neural network trained to differentiate nine classes of heart arrhythmia from electrocardiogram (ECG) recordings.
The experimental results were compelling, with the memristor-based Bayesian neural network nearly matching the best performances of a software neural network in terms of both aleatoric (area under the curve of 0.91) and epistemic uncertainty evaluation (area under the curve of 0.99), albeit with a slight reduction in raw accuracy. This performance sharply contrasted conventional neural networks, which exhibit little ability to recognize unknown data and evaluate uncertainties.
Tsinghua University: 2D-Material-Based Memristors
Finally, in China, a team at Tsinghua University developed a new system-integrated memristor computing-in-memory chip the researchers claim is the world's first of its kind to support efficient on-chip learning. This chip, still in the laboratory phase, is expected to significantly advance developments in fields such as artificial intelligence, autonomous driving, and wearable devices, among others
The memristor-based integrated circuit
The memristor-based integrated circuit. Image used courtesy of Tsinghua University
The chip is notably energy efficient, consuming only 3% of the energy of an application-specific integrated circuit (ASIC) system for on-chip learning. According to the team, this innovation started with research on memristors for storage applications and took about 11 years to develop to this point.
Memristors Gain International Steam
Memristors converge computing and memory within a single device, opening up new frontiers for energy-efficient, high-speed, and intelligent computing solutions. As international researchers continue to explore and refine memristor technology, they pave the way for more advanced and efficient computing systems that could define the next generation of electronics.
International Researchers Move the Needle on Memristor Technology
December 19, 2023 by Jake Hertz
Academics across the globe have made concurrent breakthroughs in a new form of non-volatile memory.
Memristors, or memory resistors, are an emerging form of non-volatile memory considered a viable alternative to traditional memory storage. These devices act as switches that can recall their electric state even after their power is turned off, allowing them to behave like human neurons—both computing and storing data.
Memristors are a hot research topic for their numerous unique properties, including scalability to lower process nodes, which enable smaller, faster, and more energy-efficient devices.
Cea-Leti's wafer-level memristor crossbar arrays
Cea-Leti's wafer-level memristor crossbar arrays using a prober. Image (modified) used courtesy of Cea-Leti
Now, as the demand for more sophisticated and power-efficient computing grows, memristors hold promise in various applications, including artificial intelligence, neuromorphic computing, and high-density data storage. In recent months, several international research teams have published findings about memristors that may help push this technology from lab to market.
The University of Rochester: Strain-Engineered Phase-Change Memristors
Researchers at the University of Rochester recently developed high-performance, bipolar phase-change memristors based on strain-engineered multilayer molybdenum ditelluride (MoTe2).
The strain-based memristor
The strain-based memristor. Image used courtesy of Nature
These devices, distinguished by their bipolar phase-change characteristics, represent a significant leap from conventional filamentary conduction mechanisms. The study successfully utilizes process-induced strain engineering to induce a semimetallic-to-semiconducting phase transition in MoTe2.
This transition is key to achieving the memristor's remarkable switching characteristics, including low voltages of 90 mV, high on/off ratios of 10^8, swift switching times of 5 ns, and prolonged retentions over 10^5 seconds. Such performance metrics indicate the potential of these memristors for applications in robust, fast, low-power, and scalable memory and neuromorphic computing systems.
The device's performance can also be finely tuned by varying a single process parameter: the contact metal film force, a product of film stress and thickness. This control over the device's switching voltage and on/off ratio underlines the memristor's adaptability and potential for diverse applications.
Cea-Leti: Using Memristors for Bayesian Neural Networks
In France, a group of Cea-Leti researchers announced that they have successfully integrated memristor hardware into artificial intelligence systems. Specifically, the team leveraged the inherent probabilistic nature of memristors to effectively execute the computationally intensive tasks of Bayesian neural networks.
Filamentary memristor
Filamentary memristor in the back end of the line of Cea-Leti's hybrid memristor/CMOS process. Image used courtesy of Nature
In the study, the team programmed a Bayesian neural network on an assembly of 75 crossbar arrays, each containing 1,024 memristors, complemented by CMOS periphery for in-memory computing. The experiment involved a two-layer Bayesian neural network trained to differentiate nine classes of heart arrhythmia from electrocardiogram (ECG) recordings.
The experimental results were compelling, with the memristor-based Bayesian neural network nearly matching the best performances of a software neural network in terms of both aleatoric (area under the curve of 0.91) and epistemic uncertainty evaluation (area under the curve of 0.99), albeit with a slight reduction in raw accuracy. This performance sharply contrasted conventional neural networks, which exhibit little ability to recognize unknown data and evaluate uncertainties.
Tsinghua University: 2D-Material-Based Memristors
Finally, in China, a team at Tsinghua University developed a new system-integrated memristor computing-in-memory chip the researchers claim is the world's first of its kind to support efficient on-chip learning. This chip, still in the laboratory phase, is expected to significantly advance developments in fields such as artificial intelligence, autonomous driving, and wearable devices, among others
The memristor-based integrated circuit
The memristor-based integrated circuit. Image used courtesy of Tsinghua University
The chip is notably energy efficient, consuming only 3% of the energy of an application-specific integrated circuit (ASIC) system for on-chip learning. According to the team, this innovation started with research on memristors for storage applications and took about 11 years to develop to this point.
Memristors Gain International Steam
Memristors converge computing and memory within a single device, opening up new frontiers for energy-efficient, high-speed, and intelligent computing solutions. As international researchers continue to explore and refine memristor technology, they pave the way for more advanced and efficient computing systems that could define the next generation of electronics.
This part stuck out.
“The scientists rewrote Bayesian equations so a memristor array could perform statistical analyses that harnesses randomness—a.k.a. “stochastic computing.”
“That harness randomness”. ?
Or arbitrary objects?
744/629 is very,very valuable. Wouldn’t you agree Doc? All the pieces seem to be finally catching up to the patent. Now, the more breakthroughs with memristors, the more valuable 744/629 will be. Imo
Memristors Run AI Tasks at 1/800th Power
These brain-mimicking devices boast tiny energy budgets and hardened circuits
CHARLES Q. CHOI
04 JAN 20234 MIN READ
brain shaped computer wiring against a blue glowing background
ISTOCK
Memristive devices that mimic neuron-connecting synapses could serve as the hardware for neural networks that copy the way the brain learns. Now two new studies may help solve key problems these components face not just with yields and reliability, but with finding applications beyond neural nets.
Memristors, or memory resistors, are essentially switches that can remember which electric state they were toggled to after their power is turned off. Scientists worldwide aim to use memristors and similar components to build electronics that, like neurons, can both compute and store data. These memristive devices may greatly reduce the energy and time lost in conventional microchips shuttling data back and forth between processors and memory. Such brain-inspired neuromorphic hardware may also prove ideal for implementing neural networks—AI systems increasingly finding use in applications such as analyzing medical scans and empowering autonomous vehicles.
However, current memristive devices typically rely on emerging technologies with low production yields and unreliable electronic performance. To help overcome these challenges, researchers in Israel and China fabricated memristive devices using a standard CMOS production line. The resulting silicon synapses the team built boasted a 100 percent yield with 170- to 350-fold greater energy efficiency than a high-performance Nvidia Tesla V100 graphics processing unit when it came to multiply-accumulate operations, the most basic operation in neural networks.
“Memristors are a highly promising lead to provide low-energy-consumption artificial intelligence.”
—Damien Querlioz, Université Paris-Saclay
The scientists developed the new devices using the floating-gate transistor technology used in commercial flash memory. Whereas conventional floating-gate transistors have three terminals, the new components have only two. This greatly simplified fabrication and operation and reduced their size. In addition, memristors have only binary inputs and outputs, eliminating the need for the large, energy-hungry, analog-to-digital and digital-to-analog converters often used in neuromorphic hardware, says study senior author Shahar Kvatinsky, an associate professor of electrical and computer engineering at the Technion-Israel Institute of Technology, in Haifa.
The new devices displayed high endurance, operating past more than 100,000 cycles of programming and erasing by using voltage pulses. In addition, they showed only moderate device-to-device variation and are projected to possess long data retention times of more than 10 years.
The researchers use an array of about 150 of these components to implement a kind of neural network that operated using only binary signals. In experiments, it could recognize handwritten digits with roughly 97 percent accuracy. Kvatinsky says that this work “is just a start—a proof of concept and not a whole integrated chip or a large neural network. Integration and scaling up is a major challenge.”
In another study, a team of French researchers investigated using memristors for the statistical computing technique known as Bayesian reasoning, in which prior knowledge helps compute the chances that an uncertain choice might be correct. Its results are fully explainable—unlike many nearly inscrutable AI computations—and it can perform well when there is little available data, as it can incorporate prior expert knowledge. However, “it is just not obvious how to compute Bayesian reasoning with memristors,” says study coauthor Damien Querlioz, a research scientist at CNRS, Université Paris-Saclay.
Implementing Bayesian reasoning using conventional electronics requires complex memory patterns, “which increase exponentially with the number of observations,” says neuromorphic scientist Melika Payvand at the Institute of Neuroinformatics in Zurich, who did not participate in either study. However, Querlioz and his colleagues “found a way of simplifying this,” she notes.
Memristor AI “excels in safety-critical situations, where high uncertainty is present, little data is available, and explainable decisions are required.”
—Damien Querlioz, Université Paris-Saclay
The scientists rewrote Bayesian equations so a memristor array could perform statistical analyses that harnesses randomness—a.k.a. “stochastic computing.” Using this approach, the array generated streams of semi-random bits at each tick of the clock. These bits were often zeroes but were sometimes ones. The proportion of zeroes to ones encoded the probabilities needed for the statistical calculations the array performed. This digital strategy uses relatively simple circuitry compared to nonstochastic methods, all of which reduces the system’s size and energy demands.
The researchers fabricated a prototype circuit incorporating 2,048 hafnium oxide memristors on top of 30,080 CMOS transistors on the same chip. In experiments, they had the new circuit recognize a person’s handwritten signature from signals beamed from a device worn on the wrist.
Bayesian reasoning is often thought of as computationally expensive with conventional electronics. The new circuit performed handwriting recognition using 1/800th to 1/5,000th the energy of a conventional computer processor, suggesting “that memristors are a highly promising lead to provide low-energy-consumption artificial intelligence,” Querlioz says.
The new device also can instantly flick on and off, suggesting it can work only when needed, to conserve power. Moreover, it is also resilient to errors from random events, making it useful in extreme environments, the researchers say. All in all, the new circuit “excels in safety-critical situations, where high uncertainty is present, little data is available, and explainable decisions are required,” Querlioz says. “Examples are medical sensors or circuits for monitoring the safety of industrial facilities.”
A future direction for the Bayesian circuits might include machines that collect multiple types of sensory data, Payvand says, which might include autonomous vehicles or drones. If the machine is not confident about predictions made based off one sense, it could boost its confidence by analyzing data from a different sense, she notes.
A key obstacle Bayesian systems face “is their scalability to larger problems or networks,” Payvand cautions. Querlioz notes that the team have designed a considerably scaled-up version of their device “that is currently being fabricated.” He notes their circuit is currently specialized for certain types of Bayesian computation, and they want to create more adaptable designs in the future.
To a certain degree, both studies use randomness—Querlioz and his colleagues use it for statistical analysis, while Kvatinsky and his collaborators have their neural networks sample data at random intervals to avoid the kinds of errors that can occur when sampling data a limited number of times.
“These approaches pair very well with the inherited randomness of memristor devices,” says Giacomo Pedretti, a senior AI research scientist at Hewlett Packard Labs, who did not take part in either study. It would be “very interesting” to try to use the inherent noise in these electronics to generate controlled randomness “rather than implementing costly digital pseudorandom number generators,” he says.
Both studies appeared 19 December in the journal Nature Electronics.
This article appears in the March 2023 print issue.
Computer Science | Physics
New memristor design could be a game-changer for AI and big data
by Andrey Feldman | Feb 22, 2024
Computers based on memristors promise significant energy savings and improved accuracy in large-scale computing.
Abstract image of a computer circuit.
Advanced technologies such as neural networks have found extensive application in image recognition, big data processing, financial analysis, and many other fields. However, training them demands significant computational resources and energy consumption, posing challenges for their widespread use and further development.
The problem comes down to bottlenecks imposed by traditional computing systems built using transistors, which keep their memory and processing units separate, requiring power-intensive and performance-degrading data transfers between them. Perhaps an even greater disadvantage is that their memory requires constant power to store information, which further increases energy consumption.
To solve this, researchers propose an alternative: the memristor. “Memristors, also known as memory resistors, are switches that can ‘remember’ their previous electric state, even after power is switched off,” said Desmond Loke, professor at the Singapore University of Technology and Design, in an email.
“Memristors can be used to create devices that store data because they have the ability to drastically reduce the time and energy required for data transmission between memory and processors in traditional microchips,” he continued. “They might be perfect for constructing neural networks, artificial intelligence systems for medical scan processing, and enabling driverless vehicles.”
In a study published in Advanced Physics Research, Loke and his colleagues investigated neural network training using new memristor design that stores information in a unique material made up of germanium, tellurium, and antimony.
This substance exists in an amorphous phase, but when exposed to an electric current and a change in temperature, it exhibits ordered crystalline regions. Depending on the properties of the pulse, such as its intensity and signal shape, the number and size of these regions changes, consequently affecting the material’s electrical and optical properties, which are preserved after the current is turned off. In this way, information can be recorded in the memristor and read from it by repeatedly applying an electric current.
In total, the authors could create fifteen, clearly distinguishable stages of crystallization in the memristor, each corresponding to specific information necessary for training the neural network. “We used [the] memristors to simulate a neural network,” Loke explained. “In trials, the neural network recognized handwritten numerals with an accuracy of over 96%.”
This accuracy is an improvement on other memristor designs by more than 5%, say the team, indicating a promising trajectory for this type of memristor computing technology. But there are still significant hurdles to overcome before this technology can be implemented into real-world computing systems.
“System integration and scale pose considerable challenges,” Loke explained. Nevertheless, the researchers remain hopeful, envisioning a future where memristors might play a pivotal role in training large neural networks. They anticipate smaller, more potent, and dramatically more energy-efficient units compared to conventional computers if the technology can be developed further.
“Currently, this work is just a proof of concept,” Loke concluded. “The next step is to develop a fully integrated circuit and a big neural network.”
Reference: Kian-Guan Lim et al, Toward Memristive Phase-Change Neural Network with High-Quality Ultra-Effective Highly-Self-Adjustable Online Learning, Advanced Physics Research (2024). DOI: 10.1002/apxr.202300085
Feature image credit: AcatXIo on Pixabay
…maybe SBV/V already have it?🤞
Naw. She still has 39 k on the side from scholarships she can use for travel expenses. She got 89,475 of the 94,475 full tuition from institutional grants! Only has to pay 5 k a yr.
Good question They have sensors on everything else these days. O t. Yale, Duke, Brown all accepted ! Dartmouth waited. Receiving. 89k of the 94 k cost of Yale X 4 years ! The best deal of the 3 🎉🎉🎉🎉😎🤙Go Bulldogs! KAATN’s
Curt_Hopkins
Memristor tech helps new device store ranges instead of bits and bytes
Curt_Hopkins ?10-05-2021 08:00 AM
layerssized.jpgA group of scientists mostly from Hewlett Packard Labs have published a paper in Nature Communications that outlines a “novel analog content addressable memory (CAM) based on emerging memristor devices for fast look-up table operations.”
Tree-based machine learning performed in-memory with memristive analog CAM was authored by Giacomo Pedretti, Catherine E. Graves, Sergey Serebryakov, Xia Sheng, and Martin Foltin, all of Labs; along with Can Li and Ruibin Mao of the University of Hong Kong; and John Paul Strachan of the Peter Grunberg Insitut.
The accelerator
According to the paper “(t)ree-based machine learning techniques, such as Decision Trees and Random Forests, are top performers in several domains…(h)owever, these models are difficult to optimize for fast inference at scale without accuracy loss in von Neumann architectures due to non-uniform memory access patterns.”
To solve for this problem, lead author Pedretti and the rest of the team “propose for the first time to use the analog CAM as an in-memory computational primitive to accelerate tree-based model inference.” They used an efficient mapping algorithm leveraging the new analog CAM capabilities such that each root-to-leaf path of a Decision Tree is programmed into a row, making this process much faster.
But what the team did exactly was to design a new cell, based on the memristor.
“Thanks to this new device, instead of storing digital information we can store a continuous value,” says Pedretti. “What we store now are ranges and we give an input which is made of continuous values.”
The reason why
“Right now, explainability is the focus of our team,” says Pedretti. “Think of it as a first step towards an ‘accelerator for explainable AI.’”
According to Pedretti, tree-based algorithms are more explainable than neural networks and deep learning. Those working in artificial intelligence have been developing huge, very complex models. But when people have to use the products of those models for industry or government, they often want to know not just what, but why. Why, for example, is a given image classified as a traffic light. Knowing that in situations where proof or law is in question, can be as important as the classification itself.
“Imagine a situation in the financial or insurance industry, where you need to explain why you are not giving money to somebody,” says Pedretti. “Or in a clinical context, you may be required to explain why you gave, or withheld, a drug to a given patient.”
Labs’ CAM-accelerated, tree-based model does not just create desired product, it does so while allowing users to understand how and a specific decision was reached.
An arrival
“The way we designed the stack, from materials to circuit and architecture, based as it is on memristor technology, has allowed us to create an efficient decision-making engine,” says Pedretti. “It has also allowed us to make it in such a way that it is explainable. But in addition to that, it is good.”
What Pedretti means is that they are able to process the data in a massively parallel way, so the accelerator does not have to wait for the first-step decision in the tree to be made before moving on to the rest of the branches. They infer the likely next step, looking at the tree altogether.
The movie “Arrival” and the story it was based on, “The Story of Your Life,” are science fiction. But Fermat’s Theory of Least Time, the mathematical lynchpin of the story, is not. Nor is the idea that you may bring into practice alternate ways of looking at time, life, and certainly computer architecture, ways that do not restrict you to doing one thing as a time.
The Natives are getting restless over at Vcsy-chat site. We’re all tired. Worn out really. Pissed off to put it mildly. But what can we do other than trust in our BoD at this point? Pretty much, nothing. I want to believe Len and his men have a plan. I think everyone needs to relax. Take a chill pill and go to the beach or forest or desert or whatever relaxes you. Not much longer and we’ll all hear some thing. .
And Yes. I think Milks should be sued for whatever we can sue him for and make him suffer the penalties. All in due time.
I read the article. It’s a good thing our Markman Hearing was over with years ago now. It seems the Chinese are pulling out all the stops. SBV/V should be careful. What’s next?
THUMP! ( sound of my forehead hitting my desk)
You and over 1,000 of us feel the same way. Somethings gotta give soon. We’ve all been waiting patiently for a long long time. Our new great communicator is no better than Wade . It looks like we all may have been had , again. I hope I’m wrong. Once the TSC rules in our favor and they don’t put out a ton of good news and we don’t hear anything about all these secret nda’s Johnny quan told us were coming , that will be the last straw imo
Why would we let all of our adversaries have a secure communications app that no one could break into ? We wouldn’t. But now that the cats out of the bag after 15 years we may get paid. Finally. We shall see.
Doc. Probably. I thought about that years ago. Ploinks would never work right for average Joe. Only the military. Makes sense. Only plausible explanation imo
A lot of talk on Vcsy chat site about reverse splits etc. No reverse split and no sale of Corp to anyone other than a well know established huge corporation for no less than half stock and half cash. No way a sale to some newbie Corp. That would be a huge red flag. Just sayin. We should all be on guard for anything and everything. Very little communication from your corporation is never good. A little “ hang in there just a little longer shareholder's “ would go a long way. But we get crickets. Pathetic. Sad. Disgusting really.
I think Wade will try to file a sur reply to ours after the fifteen days . April 8 if working days. Then we’ll have 15 ? Days to reply? But let’s hope if Wade tries that they deny his motion and end this b s fiasco once and for all and the J D and the IRS finally cuff him. I’m old and tired. I want justice. And I will not be screwed again.
Won’t let me be post the link. Our Lawyer submitted proof to court In black and white clearing up some lies. Wade has 15 days to respond. Then they will rule if they will even hear the case. I seriously doubt it. It’s easy to see what a crook he is/was. Not much longer now imo. I doubt there will be any more delays.
By George! I think you got it! Or at least I hope you do. Isn’t today Wade’s last day to respond? Hard to fight against court transcripts. lol. He’s toast imo o t . Lehua won District and State American Legion Constitutional Speech contest again this year! On to Nationals again.At Hillsdale College this year. She will represent Hawaii ! 25k. 22.5 20k third place ! 🎉🎉🎉🎉
lol. Thanks for that Doc. It would be a great Grad present. She also just won, last night, the 86 annual American Legion Constitutional Speech contest for The Big Island again this year! Going to State comp again which she won last year and competed in Nationals in Indy and came within 1 point of being in finals. She knows the ropes and is ready to win it this year! 25 k first 22.5 and 20 k third. Everything falling into place. Waiting sucks. The silence sucks. But I hope it will all pay off soon. If not? Oh how many torches will be lit! lol We wait.
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