Tag Archives: Shriram Ramanathan

An ‘artificial brain’ and life-long learning

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Talk of artificial brains (also known as, brainlike computing or neuromorphic computing) usually turns to memory fairly quickly. This February 3, 2022 news item on ScienceDaily does too although the focus is on how memory and forgetting affect the ability to learn,

When the human brain learns something new, it adapts. But when artificial intelligence learns something new, it tends to forget information it already learned.

As companies use more and more data to improve how AI recognizes images, learns languages and carries out other complex tasks, a paper publishing in Science this week shows a way that computer chips could dynamically rewire themselves to take in new data like the brain does, helping AI to keep learning over time.

“The brains of living beings can continuously learn throughout their lifespan. We have now created an artificial platform for machines to learn throughout their lifespan,” said Shriram Ramanathan, a professor in Purdue University’s [Indiana, US] School of Materials Engineering who specializes in discovering how materials could mimic the brain to improve computing.

Unlike the brain, which constantly forms new connections between neurons to enable learning, the circuits on a computer chip don’t change. A circuit that a machine has been using for years isn’t any different than the circuit that was originally built for the machine in a factory.

This is a problem for making AI more portable, such as for autonomous vehicles or robots in space that would have to make decisions on their own in isolated environments. If AI could be embedded directly into hardware rather than just running on software as AI typically does, these machines would be able to operate more efficiently.

A February 3, 2022 Purdue University news release (also on EurekAlert), which originated the news item, provides more technical detail about the work (Note: Links have been removed),

In this study, Ramanathan and his team built a new piece of hardware that can be reprogrammed on demand through electrical pulses. Ramanathan believes that this adaptability would allow the device to take on all of the functions that are necessary to build a brain-inspired computer.

“If we want to build a computer or a machine that is inspired by the brain, then correspondingly, we want to have the ability to continuously program, reprogram and change the chip,” Ramanathan said.

Toward building a brain in chip form

The hardware is a small, rectangular device made of a material called perovskite nickelate,  which is very sensitive to hydrogen. Applying electrical pulses at different voltages allows the device to shuffle a concentration of hydrogen ions in a matter of nanoseconds, creating states that the researchers found could be mapped out to corresponding functions in the brain.

When the device has more hydrogen near its center, for example, it can act as a neuron, a single nerve cell. With less hydrogen at that location, the device serves as a synapse, a connection between neurons, which is what the brain uses to store memory in complex neural circuits.

Through simulations of the experimental data, the Purdue team’s collaborators at Santa Clara University and Portland State University showed that the internal physics of this device creates a dynamic structure for an artificial neural network that is able to more efficiently recognize electrocardiogram patterns and digits compared to static networks. This neural network uses “reservoir computing,” which explains how different parts of a brain communicate and transfer information.

Researchers from The Pennsylvania State University also demonstrated in this study that as new problems are presented, a dynamic network can “pick and choose” which circuits are the best fit for addressing those problems.

Since the team was able to build the device using standard semiconductor-compatible fabrication techniques and operate the device at room temperature, Ramanathan believes that this technique can be readily adopted by the semiconductor industry.

“We demonstrated that this device is very robust,” said Michael Park, a Purdue Ph.D. student in materials engineering. “After programming the device over a million cycles, the reconfiguration of all functions is remarkably reproducible.”

The researchers are working to demonstrate these concepts on large-scale test chips that would be used to build a brain-inspired computer.

Experiments at Purdue were conducted at the FLEX Lab and Birck Nanotechnology Center of Purdue’s Discovery Park. The team’s collaborators at Argonne National Laboratory, the University of Illinois, Brookhaven National Laboratory and the University of Georgia conducted measurements of the device’s properties.

Here’s a link to and a citation for the paper,

Reconfigurable perovskite nickelate electronics for artificial intelligence by Hai-Tian Zhang, Tae Joon Park, A. N. M. Nafiul Islam, Dat S. J. Tran, Sukriti Manna, Qi Wang, Sandip Mondal, Haoming Yu, Suvo Banik, Shaobo Cheng, Hua Zhou, Sampath Gamage, Sayantan Mahapatra, Yimei Zhu, Yohannes Abate, Nan Jiang, Subramanian K. R. S. Sankaranarayanan, Abhronil Sengupta, Christof Teuscher, Shriram Ramanathan. Science • 3 Feb 2022 • Vol 375, Issue 6580 • pp. 533-539 • DOI: 10.1126/science.abj7943

This paper is behind a paywall.

Neuromorphic (brainlike) computing inspired by sea slugs

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The sea slug has taught neuroscientists the intelligence features that any creature in the animal kingdom needs to survive. Now, the sea slug is teaching artificial intelligence how to use those strategies. Pictured: Aplysia californica. (Image by NOAA Monterey Bay National Marine Sanctuary/Chad King.)

I don’t think I’ve ever seen a picture of a sea slug before. Its appearance reminds me of its terrestrial cousin.

As for some of the latest news on brainlike computing, a December 7, 2021 news item on Nanowerk makes an announcement from the Argonne National Laboratory (a US Department of Energy laboratory; Note: Links have been removed),

A team of scientists has discovered a new material that points the way toward more efficient artificial intelligence hardware for everything from self-driving cars to surgical robots.

For artificial intelligence (AI) to get any smarter, it needs first to be as intelligent as one of the simplest creatures in the animal kingdom: the sea slug.

A new study has found that a material can mimic the sea slug’s most essential intelligence features. The discovery is a step toward building hardware that could help make AI more efficient and reliable for technology ranging from self-driving cars and surgical robots to social media algorithms.

The study, published in the Proceedings of the National Academy of Sciences [PNAS] (“Neuromorphic learning with Mott insulator NiO”), was conducted by a team of researchers from Purdue University, Rutgers University, the University of Georgia and the U.S. Department of Energy’s (DOE) Argonne National Laboratory. The team used the resources of the Advanced Photon Source (APS), a DOE Office of Science user facility at Argonne.

A December 6, 2021 Argonne National Laboratory news release (also on EurekAlert) by Kayla Wiles and Andre Salles, which originated the news item, provides more detail,

“Through studying sea slugs, neuroscientists discovered the hallmarks of intelligence that are fundamental to any organism’s survival,” said Shriram Ramanathan, a Purdue professor of Materials Engineering. ​“We want to take advantage of that mature intelligence in animals to accelerate the development of AI.”

Two main signs of intelligence that neuroscientists have learned from sea slugs are habituation and sensitization. Habituation is getting used to a stimulus over time, such as tuning out noises when driving the same route to work every day. Sensitization is the opposite — it’s reacting strongly to a new stimulus, like avoiding bad food from a restaurant.

AI has a really hard time learning and storing new information without overwriting information it has already learned and stored, a problem that researchers studying brain-inspired computing call the ​“stability-plasticity dilemma.” Habituation would allow AI to ​“forget” unneeded information (achieving more stability) while sensitization could help with retaining new and important information (enabling plasticity).

In this study, the researchers found a way to demonstrate both habituation and sensitization in nickel oxide, a quantum material. Quantum materials are engineered to take advantage of features available only at nature’s smallest scales, and useful for information processing. If a quantum material could reliably mimic these forms of learning, then it may be possible to build AI directly into hardware. And if AI could operate both through hardware and software, it might be able to perform more complex tasks using less energy.

“We basically emulated experiments done on sea slugs in quantum materials toward understanding how these materials can be of interest for AI,” Ramanathan said.

Neuroscience studies have shown that the sea slug demonstrates habituation when it stops withdrawing its gill as much in response to tapping. But an electric shock to its tail causes its gill to withdraw much more dramatically, showing sensitization.

For nickel oxide, the equivalent of a ​“gill withdrawal” is an increased change in electrical resistance. The researchers found that repeatedly exposing the material to hydrogen gas causes nickel oxide’s change in electrical resistance to decrease over time, but introducing a new stimulus like ozone greatly increases the change in electrical resistance.

Ramanathan and his colleagues used two experimental stations at the APS to test this theory, using X-ray absorption spectroscopy. A sample of nickel oxide was exposed to hydrogen and oxygen, and the ultrabright X-rays of the APS were used to see changes in the material at the atomic level over time.

“Nickel oxide is a relatively simple material,” said Argonne physicist Hua Zhou, a co-author on the paper who worked with the team at beamline 33-ID. ​“The goal was to use something easy to manufacture, and see if it would mimic this behavior. We looked at whether the material gained or lost a single electron after exposure to the gas.”

The research team also conducted scans at beamline 29-ID, which uses softer X-rays to probe different energy ranges. While the harder X-rays of 33-ID are more sensitive to the ​“core” electrons, those closer to the nucleus of the nickel oxide’s atoms, the softer X-rays can more readily observe the electrons on the outer shell. These are the electrons that define whether a material is conductive or resistive to electricity.

“We’re very sensitive to the change of resistivity in these samples,” said Argonne physicist Fanny Rodolakis, a co-author on the paper who led the work at beamline 29-ID. ​“We can directly probe how the electronic states of oxygen and nickel evolve under different treatments.”

Physicist Zhan Zhang and postdoctoral researcher Hui Cao, both of Argonne, contributed to the work, and are listed as co-authors on the paper. Zhang said the APS is well suited for research like this, due to its bright beam that can be tuned over different energy ranges.

For practical use of quantum materials as AI hardware, researchers will need to figure out how to apply habituation and sensitization in large-scale systems. They also would have to determine how a material could respond to stimuli while integrated into a computer chip.

This study is a starting place for guiding those next steps, the researchers said. Meanwhile, the APS is undergoing a massive upgrade that will not only increase the brightness of its beams by up to 500 times, but will allow for those beams to be focused much smaller than they are today. And this, Zhou said, will prove useful once this technology does find its way into electronic devices.

“If we want to test the properties of microelectronics,” he said, ​“the smaller beam that the upgraded APS will give us will be essential.”

In addition to the experiments performed at Purdue and Argonne, a team at Rutgers University performed detailed theory calculations to understand what was happening within nickel oxide at a microscopic level to mimic the sea slug’s intelligence features. The University of Georgia measured conductivity to further analyze the material’s behavior.

A version of this story was originally published by Purdue University

About the Advanced Photon Source

The U. S. Department of Energy Office of Science’s Advanced Photon Source (APS) at Argonne National Laboratory is one of the world’s most productive X-ray light source facilities. The APS provides high-brightness X-ray beams to a diverse community of researchers in materials science, chemistry, condensed matter physics, the life and environmental sciences, and applied research. These X-rays are ideally suited for explorations of materials and biological structures; elemental distribution; chemical, magnetic, electronic states; and a wide range of technologically important engineering systems from batteries to fuel injector sprays, all of which are the foundations of our nation’s economic, technological, and physical well-being. Each year, more than 5,000 researchers use the APS to produce over 2,000 publications detailing impactful discoveries, and solve more vital biological protein structures than users of any other X-ray light source research facility. APS scientists and engineers innovate technology that is at the heart of advancing accelerator and light-source operations. This includes the insertion devices that produce extreme-brightness X-rays prized by researchers, lenses that focus the X-rays down to a few nanometers, instrumentation that maximizes the way the X-rays interact with samples being studied, and software that gathers and manages the massive quantity of data resulting from discovery research at the APS.

This research used resources of the Advanced Photon Source, a U.S. DOE Office of Science User Facility operated for the DOE Office of Science by Argonne National Laboratory under Contract No. DE-AC02-06CH11357.

Argonne National Laboratory seeks solutions to pressing national problems in science and technology. The nation’s first national laboratory, Argonne conducts leading-edge basic and applied scientific research in virtually every scientific discipline. Argonne researchers work closely with researchers from hundreds of companies, universities, and federal, state and municipal agencies to help them solve their specific problems, advance America’s scientific leadership and prepare the nation for a better future. With employees from more than 60 nations, Argonne is managed by UChicago Argonne, LLC for the U.S. Department of Energy’s Office of Science.

The U.S. Department of Energy’s Office of Science is the single largest supporter of basic research in the physical sciences in the United States and is working to address some of the most pressing challenges of our time. For more information, visit https://​ener​gy​.gov/​s​c​ience.

You can find the September 24, 2021 Purdue University story, Taking lessons from a sea slug, study points to better hardware for artificial intelligence here.

Here’s a link to and a citation for the paper,

Neuromorphic learning with Mott insulator NiO by Zhen Zhang, Sandip Mondal, Subhasish Mandal, Jason M. Allred, Neda Alsadat Aghamiri, Alireza Fali, Zhan Zhang, Hua Zhou, Hui Cao, Fanny Rodolakis, Jessica L. McChesney, Qi Wang, Yifei Sun, Yohannes Abate, Kaushik Roy, Karin M. Rabe, and Shriram Ramanathan. PNAS September 28, 2021 118 (39) e2017239118 DOI: https://doi.org/10.1073/pnas.2017239118

This paper is behind a paywall.

Artificial intelligence (AI) consumes a lot of energy but tree-like memory may help conserve it

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A simulation of a quantum material’s properties reveals its ability to learn numbers, a test of artificial intelligence. (Purdue University image/Shakti Wadekar)

A May 7, 2020 Purdue University news release (also on EurekAlert) describes a new approach for energy-efficient hardware in support of artificial intelligence (AI) systems,

To just solve a puzzle or play a game, artificial intelligence can require software running on thousands of computers. That could be the energy that three nuclear plants produce in one hour.

A team of engineers has created hardware that can learn skills using a type of AI that currently runs on software platforms. Sharing intelligence features between hardware and software would offset the energy needed for using AI in more advanced applications such as self-driving cars or discovering drugs.

“Software is taking on most of the challenges in AI. If you could incorporate intelligence into the circuit components in addition to what is happening in software, you could do things that simply cannot be done today,” said Shriram Ramanathan, a professor of materials engineering at Purdue University.

AI hardware development is still in early research stages. Researchers have demonstrated AI in pieces of potential hardware, but haven’t yet addressed AI’s large energy demand.

As AI penetrates more of daily life, a heavy reliance on software with massive energy needs is not sustainable, Ramanathan said. If hardware and software could share intelligence features, an area of silicon might be able to achieve more with a given input of energy.

Ramanathan’s team is the first to demonstrate artificial “tree-like” memory in a piece of potential hardware at room temperature. Researchers in the past have only been able to observe this kind of memory in hardware at temperatures that are too low for electronic devices.

The results of this study are published in the journal Nature Communications.

The hardware that Ramanathan’s team developed is made of a so-called quantum material. These materials are known for having properties that cannot be explained by classical physics. Ramanathan’s lab has been working to better understand these materials and how they might be used to solve problems in electronics.

Software uses tree-like memory to organize information into various “branches,” making that information easier to retrieve when learning new skills or tasks.

The strategy is inspired by how the human brain categorizes information and makes decisions.

“Humans memorize things in a tree structure of categories. We memorize ‘apple’ under the category of ‘fruit’ and ‘elephant’ under the category of ‘animal,’ for example,” said Hai-Tian Zhang, a Lillian Gilbreth postdoctoral fellow in Purdue’s College of Engineering. “Mimicking these features in hardware is potentially interesting for brain-inspired computing.”

The team introduced a proton to a quantum material called neodymium nickel oxide. They discovered that applying an electric pulse to the material moves around the proton. Each new position of the proton creates a different resistance state, which creates an information storage site called a memory state. Multiple electric pulses create a branch made up of memory states.

“We can build up many thousands of memory states in the material by taking advantage of quantum mechanical effects. The material stays the same. We are simply shuffling around protons,” Ramanathan said.

Through simulations of the properties discovered in this material, the team showed that the material is capable of learning the numbers 0 through 9. The ability to learn numbers is a baseline test of artificial intelligence.

The demonstration of these trees at room temperature in a material is a step toward showing that hardware could offload tasks from software.

“This discovery opens up new frontiers for AI that have been largely ignored because implementing this kind of intelligence into electronic hardware didn’t exist,” Ramanathan said.

The material might also help create a way for humans to more naturally communicate with AI.

“Protons also are natural information transporters in human beings. A device enabled by proton transport may be a key component for eventually achieving direct communication with organisms, such as through a brain implant,” Zhang said.

Here’s a link to and a citation for the published study,

Perovskite neural trees by Hai-Tian Zhang, Tae Joon Park, Shriram Ramanathan. Nature Communications volume 11, Article number: 2245 (2020) DOI: https://doi.org/10.1038/s41467-020-16105-y Published: 07 May 2020

This paper is open access.

Organismic learning—learning to forget

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This approach to mimicking the human brain differs from the memristor. (You can find several pieces about memrisors here including this August 24, 2017 post about a derivative, a neuristor).  This approach comes from scientists at Purdue University and employs a quantum material. From an Aug. 15, 2017 news item on phys.org,

A new computing technology called “organismoids” mimics some aspects of human thought by learning how to forget unimportant memories while retaining more vital ones.

“The human brain is capable of continuous lifelong learning,” said Kaushik Roy, Purdue University’s Edward G. Tiedemann Jr. Distinguished Professor of Electrical and Computer Engineering. “And it does this partially by forgetting some information that is not critical. I learn slowly, but I keep forgetting other things along the way, so there is a graceful degradation in my accuracy of detecting things that are old. What we are trying to do is mimic that behavior of the brain to a certain extent, to create computers that not only learn new information but that also learn what to forget.”

The work was performed by researchers at Purdue, Rutgers University, the Massachusetts Institute of Technology, Brookhaven National Laboratory and Argonne National Laboratory.

Central to the research is a ceramic “quantum material” called samarium nickelate, which was used to create devices called organismoids, said Shriram Ramanathan, a Purdue professor of materials engineering.

A video describing the work has been produced,

An August 14, 2017 Purdue University news release by Emil Venere, which originated the news item,  details the work,

“These devices possess certain characteristics of living beings and enable us to advance new learning algorithms that mimic some aspects of the human brain,” Roy said. “The results have far reaching implications for the fields of quantum materials as well as brain-inspired computing.”

…

When exposed to hydrogen gas, the material undergoes a massive resistance change, as its crystal lattice is “doped” by hydrogen atoms. The material is said to breathe, expanding when hydrogen is added and contracting when the hydrogen is removed.

“The main thing about the material is that when this breathes in hydrogen there is a spectacular quantum mechanical effect that allows the resistance to change by orders of magnitude,” Ramanathan said. “This is very unusual, and the effect is reversible because this dopant can be weakly attached to the lattice, so if you remove the hydrogen from the environment you can change the electrical resistance.”

…

When hydrogen is exposed to the material, it splits into a proton and an electron, and the electron attaches to the nickel, temporarily causing the material to become an insulator.

“Then, when the hydrogen comes out, this material becomes conducting again,” Ramanathan said. “What we show in this paper is the extent of conduction and insulation can be very carefully tuned.”

This changing conductance and the “decay of that conductance over time” is similar to a key animal behavior called habituation.

“Many animals, even organisms that don’t have a brain, possess this fundamental survival skill,” Roy said. “And that’s why we call this organismic behavior. If I see certain information on a regular basis, I get habituated, retaining memory of it. But if I haven’t seen such information over a long time, then it slowly starts decaying. So, the behavior of conductance going up and down in exponential fashion can be used to create a new computing model that will incrementally learn and at same time forget things in a proper way.”

The researchers have developed a “neural learning model” they have termed adaptive synaptic plasticity.

“This could be really important because it’s one of the first examples of using quantum materials directly for solving a major problem in neural learning,” Ramanathan said.

The researchers used the organismoids to implement the new model for synaptic plasticity.

“Using this effect we are able to model something that is a real problem in neuromorphic computing,” Roy said. “For example, if I have learned your facial features I can still go out and learn someone else’s features without really forgetting yours. However, this is difficult for computing models to do. When learning your features, they can forget the features of the original person, a problem called catastrophic forgetting.”

Neuromorphic computing is not intended to replace conventional general-purpose computer hardware, based on complementary metal-oxide-semiconductor transistors, or CMOS. Instead, it is expected to work in conjunction with CMOS-based computing. Whereas CMOS technology is especially adept at performing complex mathematical computations, neuromorphic computing might be able to perform roles such as facial recognition, reasoning and human-like decision making.

Roy’s team performed the research work on the plasticity model, and other collaborators concentrated on the physics of how to explain the process of doping-driven change in conductance central to the paper. The multidisciplinary team includes experts in materials, electrical engineering, physics, and algorithms.

“It’s not often that a materials science person can talk to a circuits person like professor Roy and come up with something meaningful,” Ramanathan said.

Organismoids might have applications in the emerging field of spintronics. Conventional computers use the presence and absence of an electric charge to represent ones and zeroes in a binary code needed to carry out computations. Spintronics, however, uses the “spin state” of electrons to represent ones and zeros.

It could bring circuits that resemble biological neurons and synapses in a compact design not possible with CMOS circuits. Whereas it would take many CMOS devices to mimic a neuron or synapse, it might take only a single spintronic device.

In future work, the researchers may demonstrate how to achieve habituation in an integrated circuit instead of exposing the material to hydrogen gas.

Here’s a link to and a citation for the paper,

Habituation based synaptic plasticity and organismic learning in a quantum perovskite by Fan Zuo, Priyadarshini Panda, Michele Kotiuga, Jiarui Li, Mingu Kang, Claudio Mazzoli, Hua Zhou, Andi Barbour, Stuart Wilkins, Badri Narayanan, Mathew Cherukara, Zhen Zhang, Subramanian K. R. S. Sankaranarayanan, Riccardo Comin, Karin M. Rabe, Kaushik Roy, & Shriram Ramanathan. Nature Communications 8, Article number: 240 (2017) doi:10.1038/s41467-017-00248-6 Published online: 14 August 2017

This paper is open access.

Studying corrosion from the other side

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Corrosion can be beautiful as well as destructive,

Typically, the process of corrosion has been studied from the metal side of the equation - See more at: http://www.anl.gov/articles/core-corrosion#sthash.ZPqFF13I.dpuf. Courtesy of the Argonne National Laboratory

Typically, the process of corrosion has been studied from the metal side of the equation – See more at: http://www.anl.gov/articles/core-corrosion#sthash.ZPqFF13I.dpuf. Courtesy of the Argonne National Laboratory

A Feb. 18, 2014 news item on Nanowerk expands on the theme of corrosion as destruction (Note: Links have been removed),

Anyone who has ever owned a car in a snowy town – or a boat in a salty sea – can tell you just how expensive corrosion can be.

One of the world’s most common and costly chemical reactions, corrosion happens frequently at the boundaries between water and metal surfaces. In the past, the process of corrosion has mostly been studied from the metal side of the equation.

However, in a new study (“Chloride ions induce order-disorder transition at water-oxide interfaces”), scientists at the Center for Nanoscale Materials at the U.S. Department of Energy’s Argonne National Laboratory investigated the problem from the other side, looking at the dynamics of water containing dissolved ions located in the regions near a metal surface.

The Feb. 14, 2014 Argonne National Laboratory news release by Jared Sagoff, which originated the news item, describes how the scientists conducted their research,

A team of researchers led by Argonne materials scientist Subramanian Sankaranarayanan simulated the physical and chemical dynamics of dissolved ions in water at the atomic level as it corrodes metal oxide surfaces. “Water-based solutions behave quite differently near a metal or oxide surface than they do by themselves,” Sankaranarayanan said. “But just how the chemical ions in the water interact with a surface has been an area of intense debate.”

Under low-chlorine conditions, water tends to form two-dimensional ordered layers near solid interfaces because of the influence of its strong hydrogen bonds. However, the researchers found that increasing the proportion of chlorine ions above a certain threshold causes a change in which the solution loses its ordered nature near the surface and begins to act similar to water away from the surface. This transition, in turn, can increase the rate at which materials corrode as well as the freezing temperature of the solution.

This switch between an ordered and a disordered structure near the metal surface happens incredibly quickly, in just fractions of a nanosecond. The speed of the chemical reaction necessitates the use of high-performance computers like Argonne’s Blue/Gene Q supercomputer, Mira.

To further explore these electrochemical oxide interfaces with high-performance computers, Sankaranarayanan and his colleagues from Argonne, Harvard University and the University of Missouri have also been awarded 40 million processor-hours of time on Mira.

“Having the ability to look at these reactions in a more powerful simulation will give us the opportunity to make a more educated guess of the rates of corrosion for different scenarios,” Sankaranarayanan said. Such studies will open up for the first time fundamental studies of corrosion behavior and will allow scientists to tailor materials surfaces to improve the stability and lifetime of materials.

Here’s a link to and a citation for the paper,

Chloride ions induce order-disorder transition at water-oxide interfaces by Sanket Deshmukh, Ganesh Kamath, Shriram Ramanathan, and Subramanian K. R. S. Sankaranarayanan. Phys. Rev. E 88 (6), 062119 (2013) [5 pages]

This article is behind a paywall on both the primary site and the beta site (the American Physical Society is testing a new website for its publications).

Getting neuromorphic with a synaptic transistor

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Scientists at Harvard University (Massachusetts, US) have devised a transistor that simulates the synapses found in brains. From a Nov. 2, 2013 news item on ScienceDaily,

It doesn’t take a Watson to realize that even the world’s best supercomputers are staggeringly inefficient and energy-intensive machines.

Our brains have upwards of 86 billion neurons, connected by synapses that not only complete myriad logic circuits; they continuously adapt to stimuli, strengthening some connections while weakening others. We call that process learning, and it enables the kind of rapid, highly efficient computational processes that put Siri and Blue Gene to shame.

Materials scientists at the Harvard School of Engineering and Applied Sciences (SEAS) have now created a new type of transistor that mimics the behavior of a synapse. The novel device simultaneously modulates the flow of information in a circuit and physically adapts to changing signals.

Exploiting unusual properties in modern materials, the synaptic transistor could mark the beginning of a new kind of artificial intelligence: one embedded not in smart algorithms but in the very architecture of a computer. [emphasis mine]

There are two other projects that I know of (and I imagine there are others) focused on intelligence that’s embedded rather than algorithmic. My December 24, 2012 posting focused on a joint (National Institute for Materials Science in Japan and the University of California, Los Angeles) project where researchers developed a nanoionic device with a range of neuromorphic and electrical properties. There’s also the memristor mentioned in my Feb. 26, 2013 posting (and many other times on this blog) which features a ,proposal to create an artificial brain.

Getting back to Harvard’s synaptic transistor (from the Nov. 1, 2013 Harvard University news release which originated the news item),

The human mind, for all its phenomenal computing power, runs on roughly 20 Watts of energy (less than a household light bulb), so it offers a natural model for engineers.

“The transistor we’ve demonstrated is really an analog to the synapse in our brains,” says co-lead author Jian Shi, a postdoctoral fellow at SEAS. “Each time a neuron initiates an action and another neuron reacts, the synapse between them increases the strength of its connection. And the faster the neurons spike each time, the stronger the synaptic connection. Essentially, it memorizes the action between the neurons.”

In principle, a system integrating millions of tiny synaptic transistors and neuron terminals could take parallel computing into a new era of ultra-efficient high performance.

Here’s an image of synaptic transistors that the researchers from Harvard’s School of Engineering and Applied Science (SEAS) have supplied,

Several prototypes of the synaptic transistor are visible on this silicon chip. (Photo by Eliza Grinnell, SEAS Communications.)

Several prototypes of the synaptic transistor are visible on this silicon chip. (Photo by Eliza Grinnell, SEAS Communications.)

The news release provides a description of the synatpic transistor and how it works,

While calcium ions and receptors effect a change in a biological synapse, the artificial version achieves the same plasticity with oxygen ions. When a voltage is applied, these ions slip in and out of the crystal lattice of a very thin (80-nanometer) film of samarium nickelate, which acts as the synapse channel between two platinum “axon” and “dendrite” terminals. The varying concentration of ions in the nickelate raises or lowers its conductance—that is, its ability to carry information on an electrical current—and, just as in a natural synapse, the strength of the connection depends on the time delay in the electrical signal.

Structurally, the device consists of the nickelate semiconductor sandwiched between two platinum electrodes and adjacent to a small pocket of ionic liquid. An external circuit multiplexer converts the time delay into a magnitude of voltage which it applies to the ionic liquid, creating an electric field that either drives ions into the nickelate or removes them. The entire device, just a few hundred microns long, is embedded in a silicon chip.

The synaptic transistor offers several immediate advantages over traditional silicon transistors. For a start, it is not restricted to the binary system of ones and zeros.

“This system changes its conductance in an analog way, continuously, as the composition of the material changes,” explains Shi. “It would be rather challenging to use CMOS, the traditional circuit technology, to imitate a synapse, because real biological synapses have a practically unlimited number of possible states—not just ‘on’ or ‘off.’”

The synaptic transistor offers another advantage: non-volatile memory, which means even when power is interrupted, the device remembers its state.

Additionally, the new transistor is inherently energy efficient. The nickelate belongs to an unusual class of materials, called correlated electron systems, that can undergo an insulator-metal transition. At a certain temperature—or, in this case, when exposed to an external field—the conductance of the material suddenly changes.

“We exploit the extreme sensitivity of this material,” says Ramanathan [principal investigator and associate professor of materials science at Harvard SEAS]. “A very small excitation allows you to get a large signal, so the input energy required to drive this switching is potentially very small. That could translate into a large boost for energy efficiency.”

The nickelate system is also well positioned for seamless integration into existing silicon-based systems.

“In this paper, we demonstrate high-temperature operation, but the beauty of this type of a device is that the ‘learning’ behavior is more or less temperature insensitive, and that’s a big advantage,” says Ramanathan. “We can operate this anywhere from about room temperature up to at least 160 degrees Celsius.”

For now, the limitations relate to the challenges of synthesizing a relatively unexplored material system, and to the size of the device, which affects its speed.

“In our proof-of-concept device, the time constant is really set by our experimental geometry,” says Ramanathan. “In other words, to really make a super-fast device, all you’d have to do is confine the liquid and position the gate electrode closer to it.”

In fact, Ramanathan and his research team are already planning, with microfluidics experts at SEAS, to investigate the possibilities and limits for this “ultimate fluidic transistor.”

Here’s a link to and a citation for the researchers’ paper,

A correlated nickelate synaptic transistor by Jian Shi, Sieu D. Ha, You Zhou, Frank Schoofs, & Shriram Ramanathan. Nature Communications 4, Article number: 2676 doi:10.1038/ncomms3676 Published 31 October 2013

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Chameleon materials

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Harvard’s School of Engineering and Applied Sciences researchers discovered some unexpected properties when testing a new coating according to an Oct. 22, 2013 news item on Azonano,

Active camouflage has taken a step forward at the Harvard School of Engineering and Applied Sciences (SEAS), with a new coating that intrinsically conceals its own temperature to thermal cameras.

In a laboratory test, a team of applied physicists placed the device on a hot plate and watched it through an infrared camera as the temperature rose. Initially, it behaved as expected, giving off more infrared light as the sample was heated: at 60 degrees Celsius it appeared blue-green to the camera; by 70 degrees it was red and yellow. At 74 degrees it turned a deep red—and then something strange happened. The thermal radiation plummeted. At 80 degrees it looked blue, as if it could be 60 degrees, and at 85 it looked even colder. Moreover, the effect was reversible and repeatable, many times over.

The Oct. 21, 2013 Harvard University news release (also on EurekAlert), which originated the news item, discusses the potential for this discovery and describes the process of discovery in more detail (Note: A link has been removed),

Principal investigator Federico Capasso, Robert L. Wallace Professor of Applied Physics and Vinton Hayes Senior Research Fellow in Electrical Engineering at Harvard SEAS, predicts that with only small adjustments the coating could be used as a new type of thermal camouflage or as a kind of encrypted beacon to allow soldiers to covertly communicate their locations in the field.

The secret to the technology lies within a very thin film of vanadium oxide, an unusual material that undergoes dramatic electronic changes when it reaches a particular temperature. At room temperature, for example, pure vanadium oxide is electrically insulating, but at slightly higher temperatures it transitions to a metallic, electrically conductive state. During that transition, the optical properties change, too, which means special temperature-dependent effects—like infrared camouflage—can also be achieved.

The insulator-metal transition has been recognized in vanadium oxide since 1959. However, it is a difficult material to work with: in bulk crystals, the stress of the transition often causes cracks to develop and can shatter the sample. Recent advances in materials synthesis and characterization—especially those by coauthor Shriram Ramanathan, Associate Professor of Materials Science at Harvard SEAS—have allowed the creation of extremely pure samples of thin-film vanadium oxide, enabling a burst of new science and engineering to take off in just the last few years.

“Thanks to these very stable samples that we’re getting from Prof. Ramanathan’s lab, we now know that if we introduce small changes to the material, we can dramatically change the optical phenomena we observe,” explains lead author Mikhail Kats, a graduate student in Capasso’s group at Harvard SEAS. “By introducing impurities or defects in a controlled way via processes known as doping, modifying, or straining the material, it is possible to create a wide range of interesting, important, and predictable behaviors.”

By doping vanadium oxide with tungsten, for example, the transition temperature can be brought down to room temperature, and the range of temperatures over which the strange thermal radiation effect occurs can be widened. Tailoring the material properties like this, with specific outcomes in mind, may enable engineering to advance in new directions.

The researchers say a vehicle coated in vanadium oxide tiles could potentially mimic its environment like a chameleon, appearing invisible to an infrared camera with only very slight adjustments to the tiles’ actual temperature—a far more efficient system than the approaches in use today.

Tuned differently, the material could become a component of a secret beacon, displaying a particular thermal signature on cue to an infrared surveillance camera. Capasso’s team suggests that the material could be engineered to operate at specific wavelengths, enabling simultaneous use by many individually identifiable soldiers.

And, because thermal radiation carries heat, the researchers believe a similar effect could be employed to deliberately speed up or slow down the cooling of structures ranging from houses to satellites.

The Harvard team’s most significant contribution is the discovery that nanoscale structures that appear naturally in the transition region of vanadium oxide can be used to provide a special level of tunability, which can be used to suppress thermal radiation as the temperature rises. The researchers refer to such a spontaneously structured material as a “natural, disordered metamaterial.”

“To artificially create such a useful three-dimensional structure within a material is extremely difficult,” says Capasso. “Here, nature is giving us what we want for free. By taking these natural metamaterials and manipulating them to have all the properties we want, we are opening up a new area of research, a completely new direction of work. We can engineer new devices from the bottom up.”

Here’s an image, from the scientists, illustrating the material’s thermal camouflage (or chameleon) properties,

A new coating intrinsically conceals its own temperature to thermal cameras. (Image courtesy of Mikhail Kats.)

A new coating intrinsically conceals its own temperature to thermal cameras. (Image courtesy of Mikhail Kats.)

Here’s a link to and a citation for the research paper,

Vanadium Dioxide as a Natural Disordered Metamaterial: Perfect Thermal Emission and Large Broadband Negative Differential Thermal Emittance by Mikhail A. Kats, Romain Blanchard, Shuyan Zhang, Patrice Genevet, Changhyun Ko, Shriram Ramanathan, and Federico Capasso. Phys. Rev. X » Volume 3 » Issue 4  or Phys. Rev. X 3, 041004 (2013) DOI:10.1103/PhysRevX.3.041004

This paper is published in an open access journal according to the Harvard news release,

About Physical Review X

Launched in August 2011, PRX (http://prx.aps.org) is an open-access, peer-reviewed publication of the American Physical Society (www.aps.org), a non-profit membership organization working to advance and diffuse the knowledge of physics through its outstanding research journals, scientific meetings, and education, outreach, advocacy and international activities. APS represents 50,000 members, including physicists in academia, national laboratories and industry in the United States and throughout the world.