In trying to bring brain-like (neuromorphic) computing closer to reality, researchers have been working on the development of memory resistors, or memristors, which are resistors in a circuit that ‘remember’ their state even if you lose power.
Today, most computers use random access memory (RAM), which moves very quickly as a user works but does not retain unsaved data if power is lost. Flash drives, on the other hand, store information when they are not powered but work much slower. Memristors could provide a memory that is the best of both worlds: fast and reliable.
He goes on to discuss a team at the University of Texas at Austin’s work on creating an extraordinarily thin memristor: an atomristor,
he team’s work features the thinnest memory devices and it appears to be a universal effect available in all semiconducting 2D monolayers.
The scientists explain that the unexpected discovery of nonvolatile resistance switching (NVRS) in monolayer transitional metal dichalcogenides (MoS2, MoSe2, WS2, WSe2) is likely due to the inherent layered crystalline nature that produces sharp interfaces and clean tunnel barriers. This prevents excessive leakage and affords stable phenomenon so that NVRS can be used for existing memory and computing applications.
“Our work opens up a new field of research in exploiting defects at the atomic scale, and can advance existing applications such as future generation high density storage, and 3D cross-bar networks for neuromorphic memory computing,” notes Akinwande [Deji Akinwande, an Associate Professor at the University of Texas at Austin]. “We also discovered a completely new application, which is non-volatile switching for radio-frequency (RF) communication systems. This is a rapidly emerging field because of the massive growth in wireless technologies and the need for very low-power switches. Our devices consume no static power, an important feature for battery life in mobile communication systems.”
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Here’s a link to and a citation for the Akinwande team’s paper,
ETA January 23, 2018: There’s another account of the atomristor in Samuel K. Moore’s January 23, 2018 posting on the Nanoclast blog (on the IEEE [Institute of Electrical and Electronics Engineers] website).
i have two bits of news, one from this October 2017 about using light to control a memristor’s learning properties and one from December 2017 about memristors and neural networks.
Memristors – or resistive memory – are nanoelectronic devices that are very promising components for next generation memory and computing devices. They are two-terminal electric elements similar to a conventional resistor – however, the electric resistance in a memristor is dependent on the charge passing through it; which means that its conductance can be precisely modulated by charge or flux through it. Its special property is that its resistance can be programmed (resistor function) and subsequently remains stored (memory function).
In this sense, a memristor is similar to a synapse in the human brain because it exhibits the same switching characteristics, i.e. it is able, with a high level of plasticity, to modify the efficiency of signal transfer between neurons under the influence of the transfer itself. That’s why researchers are hopeful to use memristors for the fabrication of electronic synapses for neuromorphic (i.e. brain-like) computing that mimics some of the aspects of learning and computation in human brains.
Human brains may be slow at pure number crunching but they are excellent at handling fast dynamic sensory information such as image and voice recognition. Walking is something that we take for granted but this is quite challenging for robots, especially over uneven terrain.
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“Memristors present an opportunity to make new types of computers that are different from existing von Neumann architectures, which traditional computers are based upon,” Dr Neil T. Kemp, a Lecturer in Physics at the University of Hull [UK], tells Nanowerk. “Our team at the University of Hull is focussed on making memristor devices dynamically reconfigurable and adaptive – we believe this is the route to making a new generation of artificial intelligence systems that are smarter and can exhibit complex behavior. Such systems would also have the advantage of memristors, high density integration and lower power usage, so these systems would be more lightweight, portable and not need re-charging so often – which is something really needed for robots etc.”
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In their new paper in Nanoscale (“Reversible Optical Switching Memristors with Tunable STDP Synaptic Plasticity: A Route to Hierarchical Control in Artificial Intelligent Systems”), Kemp and his team demonstrate the ability to reversibly control the learning properties of memristors via optical means.
The reversibility is achieved by changing the polarization of light. The researchers have used this effect to demonstrate tuneable learning in a memristor. One way this is achieved is through something called Spike Timing Dependent Plasticity (STDP), which is an effect known to occur in human brains and is linked with sensory perception, spatial reasoning, language and conscious thought in the neocortex.
STDP learning is based upon differences in the arrival time of signals from two adjacent neurons. The University of Hull team has shown that they can modulate the synaptic plasticity via optical means which enables the devices to have tuneable learning.
“Our research findings are important because it demonstrates that light can be used to control the learning properties of a memristor,” Kemp points out. “We have shown that light can be used in a reversible manner to change the connection strength (or conductivity) of artificial memristor synapses and as well control their ability to forget i.e. we can dynamically change device to have short-term or long-term memory.”
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According to the team, there are many potential applications, such as adaptive electronic circuits controllable via light, or in more complex systems, such as neuromorphic computing, the development of optically reconfigurable neural networks.
Having optically controllable memristors can also facilitate the implementation of hierarchical control in larger artificial-brain like systems, whereby some of the key processes that are carried out by biological molecules in human brains can be emulated in solid-state devices through patterning with light.
Some of these processes include synaptic pruning, conversion of short term memory to long term memory, erasing of certain memories that are no longer needed or changing the sensitivity of synapses to be more adept at learning new information.
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“The ability to control this dynamically, both spatially and temporally, is particularly interesting since it would allow neural networks to be reconfigurable on the fly through either spatial patterning or by adjusting the intensity of the light source,” notes Kemp.
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In their new paper in Nanoscale Currently, the devices are more suited to neuromorphic computing applications, which do not need to be as fast. Optical control of memristors opens the route to dynamically tuneable and reprogrammable synaptic circuits as well the ability (via optical patterning) to have hierarchical control in larger and more complex artificial intelligent systems.
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“Artificial Intelligence is really starting to come on strong in many areas, especially in the areas of voice/image recognition and autonomous systems – we could even say that this is the next revolution, similarly to what the industrial revolution was to farming and production processes,” concludes Kemp. “There are many challenges to overcome though. …
That excerpt should give you the gist of Berger’s article and, for those who need more information, there’s Berger’s article and, also, a link to and a citation for the paper,
It would seem machine learning could experience a significant upgrade if the work in Wei Lu’s University of Michigan laboratory can be scaled for general use. From a December 22, 2017 news item on ScienceDaily,
A new type of neural network made with memristors can dramatically improve the efficiency of teaching machines to think like humans.
The network, called a reservoir computing system, could predict words before they are said during conversation, and help predict future outcomes based on the present.
The research team that created the reservoir computing system, led by Wei Lu, professor of electrical engineering and computer science at the University of Michigan, recently published their work in Nature Communications.
Reservoir computing systems, which improve on a typical neural network’s capacity and reduce the required training time, have been created in the past with larger optical components. However, the U-M group created their system using memristors, which require less space and can be integrated more easily into existing silicon-based electronics.
Memristors are a special type of resistive device that can both perform logic and store data. This contrasts with typical computer systems, where processors perform logic separate from memory modules. In this study, Lu’s team used a special memristor that memorizes events only in the near history.
Inspired by brains, neural networks are composed of neurons, or nodes, and synapses, the connections between nodes.
To train a neural network for a task, a neural network takes in a large set of questions and the answers to those questions. In this process of what’s called supervised learning, the connections between nodes are weighted more heavily or lightly to minimize the amount of error in achieving the correct answer.
Once trained, a neural network can then be tested without knowing the answer. For example, a system can process a new photo and correctly identify a human face, because it has learned the features of human faces from other photos in its training set.
“A lot of times, it takes days or months to train a network,” says Lu. “It is very expensive.”
Image recognition is also a relatively simple problem, as it doesn’t require any information apart from a static image. More complex tasks, such as speech recognition, can depend highly on context and require neural networks to have knowledge of what has just occurred, or what has just been said.
“When transcribing speech to text or translating languages, a word’s meaning and even pronunciation will differ depending on the previous syllables,” says Lu.
This requires a recurrent neural network, which incorporates loops within the network that give the network a memory effect. However, training these recurrent neural networks is especially expensive, Lu says.
Reservoir computing systems built with memristors, however, can skip most of the expensive training process and still provide the network the capability to remember. This is because the most critical component of the system – the reservoir – does not require training.
When a set of data is inputted into the reservoir, the reservoir identifies important time-related features of the data, and hands it off in a simpler format to a second network. This second network then only needs training like simpler neural networks, changing weights of the features and outputs that the first network passed on until it achieves an acceptable level of error.
IMAGE: Schematic of a reservoir computing system, showing the reservoir with internal dynamics and the simpler output. Only the simpler output needs to be trained, allowing for quicker and lower-cost training. Courtesy Wei Lu.
“The beauty of reservoir computing is that while we design it, we don’t have to train it,” says Lu.
The team proved the reservoir computing concept using a test of handwriting recognition, a common benchmark among neural networks. Numerals were broken up into rows of pixels, and fed into the computer with voltages like Morse code, with zero volts for a dark pixel and a little over one volt for a white pixel.
Using only 88 memristors as nodes to identify handwritten versions of numerals, compared to a conventional network that would require thousands of nodes for the task, the reservoir achieved 91% accuracy.
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Reservoir computing systems are especially adept at handling data that varies with time, like a stream of data or words, or a function depending on past results.
To demonstrate this, the team tested a complex function that depended on multiple past results, which is common in engineering fields. The reservoir computing system was able to model the complex function with minimal error.
Lu plans on exploring two future paths with this research: speech recognition and predictive analysis.
“We can make predictions on natural spoken language, so you don’t even have to say the full word,” explains Lu.
“We could actually predict what you plan to say next.”
In predictive analysis, Lu hopes to use the system to take in signals with noise, like static from far-off radio stations, and produce a cleaner stream of data. “It could also predict and generate an output signal even if the input stopped,” he says.
IMAGE: Wei Lu, Professor of Electrical Engineering & Computer Science at the University of Michigan holds a memristor he created. Photo: Marcin Szczepanski.
The work was published in Nature Communications in the article, “Reservoir computing using dynamic memristors for temporal information processing”, with authors Chao Du, Fuxi Cai, Mohammed Zidan, Wen Ma, Seung Hwan Lee, and Prof. Wei Lu.
The research is part of a $6.9 million DARPA [US Defense Advanced Research Projects Agency] project, called “Sparse Adaptive Local Learning for Sensing and Analytics [also known as SALLSA],” that aims to build a computer chip based on self-organizing, adaptive neural networks. The memristor networks are fabricated at Michigan’s Lurie Nanofabrication Facility.
Lu and his team previously used memristors in implementing “sparse coding,” which used a 32-by-32 array of memristors to efficiently analyze and recreate images.
Mott memristors (mentioned in my Aug. 24, 2017 posting about neuristors and brainlike computing) gets more fulsome treatment in an Oct. 9, 2017 posting by Samuel K. Moore on the Nanoclast blog (found on the IEEE [Institute of Electrical and Electronics Engineers] website) Note: 1: Links have been removed; Note 2 : I quite like Moore’s writing style but he’s not for the impatient reader,
When you’re really harried, you probably feel like your head is brimful of chaos. You’re pretty close. Neuroscientists say your brain operates in a regime termed the “edge of chaos,” and it’s actually a good thing. It’s a state that allows for fast, efficient analog computation of the kind that can solve problems that grow vastly more difficult as they become bigger in size.
The trouble is, if you’re trying to replicate that kind of chaotic computation with electronics, you need an element that both acts chaotically—how and when you want it to—and could scale up to form a big system.
“No one had been able to show chaotic dynamics in a single scalable electronic device,” says Suhas Kumar, a researcher at Hewlett Packard Labs, in Palo Alto, Calif. Until now, that is.
He, John Paul Strachan, and R. Stanley Williams recently reported in the journal Nature that a particular configuration of a certain type of memristor contains that seed of controlled chaos. What’s more, when they simulated wiring these up into a type of circuit called a Hopfield neural network, the circuit was capable of solving a ridiculously difficult problem—1,000 instances of the traveling salesman problem—at a rate of 10 trillion operations per second per watt.
(It’s not an apples-to-apples comparison, but the world’s most powerful supercomputer as of June 2017 managed 93,015 trillion floating point operations per second but consumed 15 megawatts doing it. So about 6 billion operations per second per watt.)
The device in question is called a Mott memristor. Memristors generally are devices that hold a memory, in the form of resistance, of the current that has flowed through them. The most familiar type is called resistive RAM (or ReRAM or RRAM, depending on who’s asking). Mott memristors have an added ability in that they can also reflect a temperature-driven change in resistance.
The HP Labs team made their memristor from an 8-nanometer-thick layer of niobium dioxide (NbO2) sandwiched between two layers of titanium nitride. The bottom titanium nitride layer was in the form of a 70-nanometer wide pillar. “We showed that this type of memristor can generate chaotic and nonchaotic signals,” says Williams, who invented the memristor based on theory by Leon Chua.
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(The traveling salesman problem is one of these. In it, the salesman must find the shortest route that lets him visit all of his customers’ cities, without going through any of them twice. It’s a difficult problem because it becomes exponentially more difficult to solve with each city you add.)
Here’s what the niobium dioxide-based Mott memristor looks like,
Photo: Suhas Kumar/Hewlett Packard Labs A micrograph shows the construction of a Mott memristor composed of an 8-nanometer-thick layer of niobium dioxide between two layers of titanium nitride.
S.NET once stood for Society for the Study of Nanoscience and Emerging Technologies and then the name started changing with the most recent being, Society of the Studies of New and Emerging Technologies. As I noted in my 2017 end-of-year comments (Dec. 30, 2017 posting), the nano blogosphere is also shifting as nanotechnology is being absorbed into and enables other scientific and technical efforts.
S.NET is celebrating its 10th year at their annual meeting, which will be held in Maastricht (Netherlands). Here’s their call for papers,
2018 Annual S.NET Meeting
Image: Geert Budenaerts, Wikimedia
CALL for PAPERS.
The 10th annual S.NET meeting will take place June 25-27, 2018 at the Faculty of Arts and Social Science, Maastricht University, The Netherlands. The theme is Anticipatory Technologies: Data and Disorientation.
Invitation
S.NET invites contributions to the tenth annual meeting of The Society for the Study of New and Emerging Technologies (S.NET), to be held at Maastricht University, the Netherlands, on June 25 – 27, 2018. The three-day conference will assemble scholars, practitioners and policy makers from around the world interested in the development and implications of emerging technologies.
About S.NET
S.NET is an international association that promotes intellectual exchange and critical inquiry about the advancement of new and emerging technologies in society. The aim of the association is to advance critical reflection from various perspectives on developments in a broad range of new and emerging fields, including, but not limited to, nanoscale science and engineering, biotechnology, synthetic biology, cognitive science, ICT and Big Data, and geo-engineering. Current S.NET board members are: Michael Bennett (chair), Marianne Boenink, Ana Delgado, Clare Shelley-Egan, Chris Toumey, Poonam Pandey, Christopher Coenen, Colin Milburn, Kornelia Konrad, Nora Vaage, Maria Belen Albornoz, and Ryan LaBar.
Conference Theme: Anticipatory technologies – data and disorientation
Any effort on new and emerging technologies unavoidably deals with the non-existing and the speculative. The future is permanently mobilized to promote decisions and policies regarding the science, technology and society nexus. Anticipatory technologies like predictive policing and preventive medicine promise to give us better epistemic access and practical control over the future. The basic irony, however, is that anticipatory technologies do not only increase data but also disorientation. Is the disorientation vis-á-vis the future in spite of the astonishing growth of data, or can it be a result of that growth? Does the growing control over future events in terms of risk make people more acutely aware of what they don’t control? Contributions are invited that explore existing ways in which the future is mobilized, technologically mediated, and economically exploited; that map the manifold ways it is contested both in discourse and in action; and that reflect on the extent to which new technologies ironically undermine our faith in the future.
Key note speakers
Prof Cyrus Mody is an historian of recent science and technology and has published on the history of nanotechnology and micro-electronics. He studies the commercialization of academic research, countercultural science and technology, and the longue durée of responsible research and innovation. He worked at Rice University, Texas, the NSF Center for Nanotechnology in Society and now has a chair at Maastricht University.
Prof Marjolein van Asselthas a strong profile on governance, risk and uncertainty in both academic and policy circles. Currently she is member of the Dutch Safety Board and was a member of the Scientific Council for Government Policy for many years. She has a Governance chair at Maastricht University.
Third key note speaker to be announced.
Themes, topics and conference strands for the 10th Annual Meeting
S.NET encompasses communities, perspectives, and methodologies from across the social sciences, humanities and natural sciences, and welcomes contributions from technology developers and other practitioners. The program committee invites contributions from the full breadth of disciplines, methodologies, and perspectives, as well as from applied, participatory, and practical approaches to studying these emerging fields. Regionally or internationally comparative perspectives are especially welcome. Possible themes and topics have been organized into one overarching conference theme and six ‘strands’. While applicants are asked to indicate the strand relevant to the topic of their paper, submissions dealing with themes or topics outside the present strands are also welcome.
R&D practices and the dynamics of new and emerging sciences and technologies
Research networks & collaborations, ways of organizing research & development, emerging research fields, practices of ‘doing’ new and emerging fields of science and technology, including historical and philosophical studies of these practices.
Innovation and the use of new and emerging sciences and technologies
Innovation networks and systems, commercialization, implications for industry structures, translation from lab to practice, application and use of products and other innovations, critical analyses of growth and consumption, including economic, social and cultural approaches of innovation processes.
Governance of newly emerging sciences and technologies
Regulations, anticipatory governance practices, risk assessment, risk concerns, (constructive) TA, forms of public participation and engagement, including critical evaluation of forms of governance.
Visions and cultural imaginaries of newly emerging sciences and technologies
Promises, expectations, visions, science fiction, imagination, socio – technical change, moral change, role of media, including assessments of such visions and analyses of their role in innovation processes.
Publics and their relations to newly emerging science s and technologies
Science communication, risk communication, public engagement, participation and discourses on NEST, science museums, informal science learning initiatives, including critical evaluation of such initiatives and the notion of ‘publics’.
Politics and ethics of new and emerging sciences and technologies
Responsible innovation, (in)equality, equity, development, global and social distribution of benefits and risks, sustainability, ‘soft’ and ‘hard’ impacts of emerging technologies, including theoretical perspectives on NEST and global developments.
How to apply
S.NET encourages proposals for individual papers, posters, traditional panels, roundtable discussions and other innovative formats. Abstracts should be approximately 250 words in length. Proposals for panel sessions should include a general introduction and abstracts of the separate contributions. Proposals should include the theme or strand to which the abstract/panel session is submitted. If an abstract fits more strands, or does not fit the existing strands, simply note that in your submission. The deadline for abstract submissions is March 2, 2018; send your abstract in PDF form to 2018snet@gmail.com. All submitters will be notified about the results of the review process by the end of April 2018. Details of the submission process are available online: www.maastrichtsts.nl/snet.
The local organizing committee
Tsjalling Swierstra, Harro van Lente, Nora Vaage, Conor Douglas, Danielle Shanley, Darian Meacham, Cindy van Montfoort, Jacqueline Graff.
Location
Maastricht is an ancient Roman city of some 120.000 inhabitants in the south of The Netherlands and has a beautiful medieval inner-city. Generally known as the venue of the Treaty of Maastricht, it has a distinctly international orientation. Maastricht can easily be reached by plane, train and car. Maastricht University is internationally oriented; its students come from all over the world. The Faculty of Arts and Social Sciences (FASoS) is located in the centre of Maastricht.
