Tag Archives: Stanley Williams

Neuromorphic engineering: an overview

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In a February 13, 2023 essay, Michael Berger who runs the Nanowerk website provides an overview of brainlike (neuromorphic) engineering.

This essay is the most extensive piece I’ve seen on Berger’s website and it covers everything from the reasons why scientists are so interested in mimicking the human brain to specifics about memristors. Here are a few excerpts (Note: Links have been removed),

Neuromorphic engineering is a cutting-edge field that focuses on developing computer hardware and software systems inspired by the structure, function, and behavior of the human brain. The ultimate goal is to create computing systems that are significantly more energy-efficient, scalable, and adaptive than conventional computer systems, capable of solving complex problems in a manner reminiscent of the brain’s approach.

This interdisciplinary field draws upon expertise from various domains, including neuroscience, computer science, electronics, nanotechnology, and materials science. Neuromorphic engineers strive to develop computer chips and systems incorporating artificial neurons and synapses, designed to process information in a parallel and distributed manner, akin to the brain’s functionality.

Key challenges in neuromorphic engineering encompass developing algorithms and hardware capable of performing intricate computations with minimal energy consumption, creating systems that can learn and adapt over time, and devising methods to control the behavior of artificial neurons and synapses in real-time.

Neuromorphic engineering has numerous applications in diverse areas such as robotics, computer vision, speech recognition, and artificial intelligence. The aspiration is that brain-like computing systems will give rise to machines better equipped to tackle complex and uncertain tasks, which currently remain beyond the reach of conventional computers.

It is essential to distinguish between neuromorphic engineering and neuromorphic computing, two related but distinct concepts. Neuromorphic computing represents a specific application of neuromorphic engineering, involving the utilization of hardware and software systems designed to process information in a manner akin to human brain function.

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One of the major obstacles in creating brain-inspired computing systems is the vast complexity of the human brain. Unlike traditional computers, the brain operates as a nonlinear dynamic system that can handle massive amounts of data through various input channels, filter information, store key information in short- and long-term memory, learn by analyzing incoming and stored data, make decisions in a constantly changing environment, and do all of this while consuming very little power.

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The Human Brain Project [emphasis mine], a large-scale research project launched in 2013, aims to create a comprehensive, detailed, and biologically realistic simulation of the human brain, known as the Virtual Brain. One of the goals of the project is to develop new brain-inspired computing technologies, such as neuromorphic computing.

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The Human Brain Project has been funded by the European Union (1B Euros over 10 years starting in 2013 and sunsetting in 2023). From the Human Brain Project Media Invite,

The final Human Brain Project Summit 2023 will take place in Marseille, France, from March 28-31, 2023.

As the ten-year European Flagship Human Brain Project (HBP) approaches its conclusion in September 2023, the final HBP Summit will highlight the scientific achievements of the project at the interface of neuroscience and technology and the legacy that it will leave for the brain research community. …

One last excerpt from the essay,

Neuromorphic computing is a radical reimagining of computer architecture at the transistor level, modeled after the structure and function of biological neural networks in the brain. This computing paradigm aims to build electronic systems that attempt to emulate the distributed and parallel computation of the brain by combining processing and memory in the same physical location.

This is unlike traditional computing, which is based on von Neumann systems consisting of three different units: processing unit, I/O unit, and storage unit. This stored program architecture is a model for designing computers that uses a single memory to store both data and instructions, and a central processing unit to execute those instructions. This design, first proposed by mathematician and computer scientist John von Neumann, is widely used in modern computers and is considered to be the standard architecture for computer systems and relies on a clear distinction between memory and processing.

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I found the diagram Berger Included with von Neumann’s design contrasted with a neuromorphic design illuminating,

A graphical comparison of the von Neumann and Neuromorphic architecture. Left: The von Neumann architecture used in traditional computers. The red lines depict the data communication bottleneck in the von Neumann architecture. Right: A graphical representation of a general neuromorphic architecture. In this architecture, the processing and memory is decentralized across different neuronal units(the yellow nodes) and synapses(the black lines connecting the nodes), creating a naturally parallel computing environment via the mesh-like structure. (Source: DOI: 10.1109/IS.2016.7737434) [downloaded from https://www.nanowerk.com/spotlight/spotid=62353.php]

Berger offers a very good overview and I recommend reading his February 13, 2023 essay on neuromorphic engineering with one proviso, Note: A link has been removed,

Many researchers in this field see memristors as a key device component for neuromorphic engineering. Memristor – or memory resistor – devices are non-volatile nanoelectronic memory devices that were first theorized [emphasis mine] by Leon Chua in the 1970’s. However, it was some thirty years later that the first practical device was fabricated in 2008 by a group led by Stanley Williams [sometimes cited as R. Stanley Williams] at HP Research Labs.

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Chua wasn’t the first as he, himself, has noted. Chua arrived at his theory independently in the 1970s but Bernard Widrow theorized what he called a ‘memistor’ in the 1960s. In fact “Memristors: they are older than you think” is a May 22, 2012 posting which featured an article “Two centuries of memristors” by Themistoklis Prodromakis, Christofer Toumazou and Leon Chua published in Nature Materials.

Most of us try to get it right but we don’t always succeed. It’s always good practice to read everyone (including me) with a little skepticism.

An artificial synapse tuned by light, a ferromagnetic memristor, and a transparent, flexible artificial synapse

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Down the memristor rabbit hole one more time.* I started out with news about two new papers and inadvertently found two more. In a bid to keep this posting to a manageable size, I’m stopping at four.

UK

In a June 19, 2019 Nanowerk Spotlight article, Dr. Neil Kemp discusses memristors and some of his latest work (Note: A link has been removed),

Memristor (or memory resistors) devices are non-volatile electronic memory devices that were first theorized by Leon Chua in the 1970’s. However, it was some thirty years later that the first practical device was fabricated. This was in 2008 when a group led by Stanley Williams at HP Research Labs realized that switching of the resistance between a conducting and less conducting state in metal-oxide thin-film devices was showing Leon Chua’s memristor behaviour.

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The high interest in memristor devices also stems from the fact that these devices emulate the memory and learning properties of biological synapses. i.e. the electrical resistance value of the device is dependent on the history of the current flowing through it.

There is a huge effort underway to use memristor devices in neuromorphic computing applications and it is now reasonable to imagine the development of a new generation of artificial intelligent devices with very low power consumption (non-volatile), ultra-fast performance and high-density integration.

These discoveries come at an important juncture in microelectronics, since there is increasing disparity between computational needs of Big Data, Artificial Intelligence (A.I.) and the Internet of Things (IoT), and the capabilities of existing computers. The increases in speed, efficiency and performance of computer technology cannot continue in the same manner as it has done since the 1960s.

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To date, most memristor research has focussed on the electronic switching properties of the device. However, for many applications it is useful to have an additional handle (or degree of freedom) on the device to control its resistive state. For example memory and processing in the brain also involves numerous chemical and bio-chemical reactions that control the brain structure and its evolution through development.

To emulate this in a simple solid-state system composed of just switches alone is not possible. In our research, we are interested in using light to mediate this essential control.

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We have demonstrated that light can be used to make short and long-term memory and we have shown how light can modulate a special type of learning, called spike timing dependent plasticity (STDP). STDP involves two neuronal spikes incident across a synapse at the same time. Depending on the relative timing of the spikes and their overlap across the synaptic cleft, the connection strength is other strengthened or weakened.

In our earlier work, we were only able to achieve to small switching effects in memristors using light. In our latest work (Advanced Electronic Materials, “Percolation Threshold Enables Optical Resistive-Memory Switching and Light-Tuneable Synaptic Learning in Segregated Nanocomposites”), we take advantage of a percolating-like nanoparticle morphology to vastly increase the magnitude of the switching between electronic resistance states when light is incident on the device.

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We have used an inhomogeneous percolating network consisting of metallic nanoparticles distributed in filamentary-like conduction paths. Electronic conduction and the resistance of the device is very sensitive to any disruption of the conduction path(s).

By embedding the nanoparticles in a polymer that can expand or contract with light the conduction pathways are broken or re-connected causing very large changes in the electrical resistance and memristance of the device.

Our devices could lead to the development of new memristor-based artificial intelligence systems that are adaptive and reconfigurable using a combination of optical and electronic signalling. Furthermore, they have the potential for the development of very fast optical cameras for artificial intelligence recognition systems.

Our work provides a nice proof-of-concept but the materials used means the optical switching is slow. The materials are also not well suited to industry fabrication. In our on-going work we are addressing these switching speed issues whilst also focussing on industry compatible materials.

Currently we are working on a new type of optical memristor device that should give us orders of magnitude improvement in the optical switching speeds whilst also retaining a large difference between the resistance on and off states. We hope to be able to achieve nanosecond switching speeds. The materials used are also compatible with industry standard methods of fabrication.

The new devices should also have applications in optical communications, interfacing and photonic computing. We are currently looking for commercial investors to help fund the research on these devices so that we can bring the device specifications to a level of commercial interest.

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If you’re interested in memristors, Kemp’s article is well written and quite informative for nonexperts, assuming of course you can tolerate not understanding everything perfectly.

Here are links and citations for two papers. The first is the latest referred to in the article, a May 2019 paper and the second is a paper appearing in July 2019.

Percolation Threshold Enables Optical Resistive‐Memory Switching and Light‐Tuneable Synaptic Learning in Segregated Nanocomposites by Ayoub H. Jaafar, Mary O’Neill, Stephen M. Kelly, Emanuele Verrelli, Neil T. Kemp. Advanced Electronic Materials DOI: https://doi.org/10.1002/aelm.201900197 First published: 28 May 2019

Wavelength dependent light tunable resistive switching graphene oxide nonvolatile memory devices by Ayoub H.Jaafar, N.T.Kemp. DOI: https://doi.org/10.1016/j.carbon.2019.07.007 Carbon Available online 3 July 2019

The first paper (May 2019) is definitely behind a paywall and the second paper (July 2019) appears to be behind a paywall.

Dr. Kemp’s work has been featured here previously in a January 3, 2018 posting in the subsection titled, Shining a light on the memristor.

China

This work from China was announced in a June 20, 2019 news item on Nanowerk,

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Memristors, demonstrated by solid-state devices with continuously tunable resistance, have emerged as a new paradigm for self-adaptive networks that require synapse-like functions. Spin-based memristors offer advantages over other types of memristors because of their significant endurance and high energy effciency.

However, it remains a challenge to build dense and functional spintronic memristors with structures and materials that are compatible with existing ferromagnetic devices. Ta/CoFeB/MgO heterostructures are commonly used in interfacial PMA-based [perpendicular magnetic anisotropy] magnetic tunnel junctions, which exhibit large tunnel magnetoresistance and are implemented in commercial MRAM [magnetic random access memory] products.

“To achieve the memristive function, DW is driven back and forth in a continuous manner in the CoFeB layer by applying in-plane positive or negative current pulses along the Ta layer, utilizing SOT that the current exerts on the CoFeB magnetization,” said Shuai Zhang, a coauthor in the paper. “Slowly propagating domain wall generates a creep in the detection area of the device, which yields a broad range of intermediate resistive states in the AHE [anomalous Hall effect] measurements. Consequently, AHE resistance is modulated in an analog manner, being controlled by the pulsed current characteristics including amplitude, duration, and repetition number.”

“For a follow-up study, we are working on more neuromorphic operations, such as spike-timing-dependent plasticity and paired pulsed facilitation,” concludes You. …

Here’s are links to and citations for the paper (Note: It’s a little confusing but I believe that one of the links will take you to the online version, as for the ‘open access’ link, keep reading),

A Spin–Orbit‐Torque Memristive Device by Shuai Zhang, Shijiang Luo, Nuo Xu, Qiming Zou, Min Song, Jijun Yun, Qiang Luo, Zhe Guo, Ruofan Li, Weicheng Tian, Xin Li, Hengan Zhou, Huiming Chen, Yue Zhang, Xiaofei Yang, Wanjun Jiang, Ka Shen, Jeongmin Hong, Zhe Yuan, Li Xi, Ke Xia, Sayeef Salahuddin, Bernard Dieny, Long You. Advanced Electronic Materials Volume 5, Issue 4 April 2019 (print version) 1800782 DOI: https://doi.org/10.1002/aelm.201800782 First published [online]: 30 January 2019 Note: there is another DOI, https://doi.org/10.1002/aelm.201970022 where you can have open access to Memristors: A Spin–Orbit‐Torque Memristive Device (Adv. Electron. Mater. 4/2019)

The paper published online in January 2019 is behind a paywall and the paper (almost the same title) published in April 2019 has a new DOI and is open access. Final note: I tried accessing the ‘free’ paper and opened up a free file for the artwork featuring the work from China on the back cover of the April 2019 of Advanced Electronic Materials.

Korea

Usually when I see the words transparency and flexibility, I expect to see graphene is one of the materials. That’s not the case for this paper (link to and citation for),

Transparent and flexible photonic artificial synapse with piezo-phototronic modulator: Versatile memory capability and higher order learning algorithm by Mohit Kumar, Joondong Kim, Ching-Ping Wong. Nano Energy Volume 63, September 2019, 103843 DOI: https://doi.org/10.1016/j.nanoen.2019.06.039 Available online 22 June 2019

Here’s the abstract for the paper where you’ll see that the material is made up of zinc oxide silver nanowires,

An artificial photonic synapse having tunable manifold synaptic response can be an essential step forward for the advancement of novel neuromorphic computing. In this work, we reported the development of highly transparent and flexible two-terminal ZnO/Ag-nanowires/PET photonic artificial synapse [emphasis mine]. The device shows purely photo-triggered all essential synaptic functions such as transition from short-to long-term plasticity, paired-pulse facilitation, and spike-timing-dependent plasticity, including in the versatile memory capability. Importantly, strain-induced piezo-phototronic effect within ZnO provides an additional degree of regulation to modulate all of the synaptic functions in multi-levels. The observed effect is quantitatively explained as a dynamic of photo-induced electron-hole trapping/detraining via the defect states such as oxygen vacancies. We revealed that the synaptic functions can be consolidated and converted by applied strain, which is not previously applied any of the reported synaptic devices. This study will open a new avenue to the scientific community to control and design highly transparent wearable neuromorphic computing.

This paper is behind a paywall.

Mott memristor

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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.

…

(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.

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

Chaotic dynamics in nanoscale NbO2 Mott memristors for analogue computing by Suhas Kumar, John Paul Strachan & R. Stanley Williams. Nature 548, 318–321 (17 August 2017) doi:10.1038/nature23307 Published online: 09 August 2017

This paper is behind a paywall.

*2700th posting: new generation of hybird memristive nanodevices and an update of HP labs and its memristive products

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Hard to believe this is the *2700th posting but yay! To commemorate this special occasion I’m featuring two items about memristors, work on protein-based memristors and an update of my Feb. 7, 2013 posting on the HP Labs and its promises of memristor-based products.

Michael Berger’s Dec. 16, 2013 issue of Nanowerk Spotlight focused on memristor research from bioengineers at Singapore’s Nanyang Technological University (Note: Links have been removed),

 Based on the rapid development of synthetic chemistry and bioengineering, researchers have begun to build hybrid nanostructures with various biomolecules to fulfill the functional requirements of advanced nanocircuits. Proteins already perform functions such as signalling, charge transport or storage, in all biochemical processes.

“Although the diversity of these natural molecules is vast – for instance, more than a million variants of an individual protein may be created via genetic engineering – tailoring their structures to fit the variable and complex requirements of both the biological and non-biological world is achievable by leveraging on the rapidly developing bioengineering field,” Xiaodong Chen, an Associate Professor in the School of Materials Science & Engineering at Nanyang Technological University, tells Nanowerk. “On a parallel note, bioengineering may provide an alternative approach to tune the structural and electronic properties of functional molecules leading to further development in the field of molecular electronics.”

Berger provides more context on this work by way of a 2011 Spotlight about the research (featured in my Sept. 19, 2011 posting) and then describes Chen’s latest work,

In new work, reported in a recent edition of Small (“Bioengineered Tunable Memristor Based on Protein Nanocage”) Chen and his team demonstrate a strategy for the fabrication of memristive nanodevices with stable and tunable performance by assembling ferritin monolayer inside a on-wire lithography-generated ∼12 nm gap.

Whereas the protein-based memristor devices in the previous work were fabricated from the commercial horse spleen ferritin, the new work uses the unique high iron loading capacity of Archaeoglobus fulgidus ferritin (AfFtn).

“We hypothesized that if the composition of this iron complex core can be modulated, the switching performance of the protein-based device can be controlled accordingly,” says Chen.

They found that the (tunable) iron loading in the AfFtn nanocages drastically impacts the performance of the memristive devices. The higher iron loading amount contributes to better memristive performance due to higher electrochemical activity of the ferric complex core.

This work is not going to be found in any applications for molecular devices at any time soon but it seems promising at this stage. For those who’d like more information, there’s Berger’s article or this link and a citation to the researchers’ paper,

Bioengineered Tunable Memristor Based on Protein Nanocage by Fanben Meng, Barindra Sana, Yuangang Li, Yuanjun Liu, Sierin Lim, & Xiaodong Chen. Article first published online: 19 AUG 2013 DOI: 10.1002/smll.201300810
© 2013 WILEY-VCH Verlag GmbH & Co. KGaA, Weinheim

This paper is behind a paywall but Wiley does offer a number of viewing options at different price points.

HP Labs and its memristor-based products

Following on last year’s Feb. 7, 2013 update (scroll down about 1/2 way), it seems like another annual update is in order unfortunately, the news seems like a retread. Memristor’-based devices from HP Labs will not be launched (in the marketplace or even to show at technology shows) this year either. In fact, any sort of launch is much further in the future according to Chris Mellor’s Nov. 1, 2013 article for The Register; Note: Links have been removed),

HP has warned El Reg not to get its hopes up too high after the tech titan’s CTO Martin Fink suggested StoreServ arrays could be packed with 100TB Memristor drives come 2018.

In five years, according to Fink, DRAM and NAND scaling will hit a wall, limiting the maximum capacity of the technologies: process shrinks will come to a shuddering halt when the memories’ reliability drops off a cliff as a side effect of reducing the size of electronics on the silicon dies.

The HP answer to this scaling wall is Memristor, its flavour of resistive RAM technology that is supposed to have DRAM-like speed and better-than-NAND storage density. Fink claimed at an HP Discover event in Las Vegas that Memristor devices will be ready by the time flash NAND hits its limit in five years. He also showed off a Memristor wafer, adding that it could have a 1.5PB capacity by the end of the decade.

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Fink spoke about the tech in June, but this week a HP spokesperson clarified to The Reg:

As with many other ground-breaking technologies being developed at HP Labs, HP has not yet committed to a specific product roadmap for Memristor-based products. HP does have internal milestones that are subject to change, depending on shifting market, technology and business conditions.

Every time I read about it HP Labs’ memristor-based products  they keep receding further into the future. Compare this latest announcement with what was being said at the time of my Feb.7, 2013 posting,

… Stanley Williams’ presence in the video reminded me of the memristor and an announcement (mentioned in my April 19, 2012 posting) that HP Labs would be rolling out some memristor-enabled products in 2013. Sadly, later in the year I missed this announcement, from a July 9, 2012 posting by Chris Mellor for TheRegister.co.uk,

Previously he (Stanley Williams) has said that HP and fab partner Hynix would launch a memristor product in the summer of 2013. At the Kavli do [Kavli Foundation Roundtable, June 2012], Williams said: “In terms of commercialisation, we’ll have something technologically viable by the end of next year [2014].”

To be fair, it seems HP Labs had abandoned plans for a commercial launch of memristor-based products even in 2013 but now it seems there is no roadmap of any kind.

* Corrected from ‘3000’ to ‘2700’.

CeNSE (Central Nervous System of the Earth) and billions of tiny sensors from HP plus a memristor update

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Mike Thacker’s Feb. 1, 2013 (?) post features an HP Labs video trumpeting what is described as their most progressive work, from the official HP Labs blog,

… HP Labs in Palo Alto, for example, which is using nanotechnology capabilities to create low-cost censors that act as a central nervous system for the earth. The technology can be used to closely monitor — and quickly respond to — changes in agriculture, food supply and architectural infrastructure around the world.

CeNSE (Central Nervous System of the Earth) sounds like something new, eh? Almost three years ago, Greg Lindsay wrote about CeNSE and its first customer, Shell Oil, in a Feb. 12, 2010 article for Fast Company (Note: Links have been removed),

Just days after Cisco signaled it will horn into IBM’s turf by rewiring an aging city in Massachusetts, Hewlett Packard announced this morning the first commercial application of its own holistic blueprint–the torturously acronymed “CeNSE” (short for Central Nervous System for the Earth). Much like IBM’s “Smarter Planet” campaign, HP proposes sticking billions of sensors on everything in sight and boiling down the resulting flood of data into insights for making the world a better, greener place. But what sets HP apart from its rivals is its determination to create a smarter planet almost entirely within house, from sensors of its own design and manufacture to servers to software to the consultants who will tie it all together. And its first customer could not be less green: Shell Oil.

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The Shell deal also unintentionally explodes the myth that a smarter planet is necessarily a greener one. HP’s bleeding-edge accelerometers are being deployed for the least green thing you can think of: sucking every last drop of oil out of the ground. While absolutely necessary for the current trajectory of our way of life (and buying us more time to develop alternatives), it’s hard to argue that technology for more efficiently recovering fossil fuels is in any way sustainable. (Although Wacker [Jeff Wacker, the leader of services innovation at HP and the head of its efforts to commercialize CeNSE] gamely argues the same technology is needed for finding empty pockets suitable for carbon sequestration.) While corporate-sponsored smarter cities can, in fact, be greener ones, their charter is the same as it ever was: profit. [emphasis mine]

Lindsay’s article echoes some of what I noted in the context of the Carbon Management Canada (CMC) network (government- and industry-funded) in my Feb. 4, 2013 posting about ultra-sensitive nanosensors and attempts to reduce carbon emissions in the Alberta oil sands. While the industry may work to reduce emissions, its raison d’être is profit and that can lead to complex situations with conflicting agendas.

As for what these billions and billions of tiny sensors might do for us, it seems there might be alternatives to at least one of the capabilities claimed by HP Labs and its sensors, ‘sensing changes in architectural infrastructures’. My Jan. 3, 2013 post, Signal danger with smart paint, mentioned a much more modest effort,

An innovative low-cost smart paint that can detect microscopic faults in wind turbines, mines and bridges before structural damage occurs is being developed by researchers at the University of Strathclyde in Glasgow, Scotland. [emphasis mine]

The environmentally-friendly paint uses nanotechnology to detect movement in large structures, and could shape the future of safety monitoring.

I digress slightly. The reference to the ‘central nervous system of the earth’ and Stanley Williams’ presence in the video reminded me of the memristor and an announcement (mentioned in my April 19, 2012 posting) that HP Labs would be rolling out some memristor-enabled products in 2013. Sadly, later in the year I missed this announcement, from a July 9, 2012 posting by Chris Mellor for TheRegister.co.uk,

Previously he (Stanley Williams) has said that HP and fab partner Hynix would launch a memristor product in the summer of 2013. At the Kavli do [Kavli Foundation Roundtable, June 2012], Williams said: “In terms of commercialisation, we’ll have something technologically viable by the end of next year.”

But that doesn’t mean a commercial product launch, and Hynix’s concerns about memristor device effect on flash are relevant: “Our partner, Hynix, is a major producer of flash memory, and memristors will cannibalise its existing business by replacing some flash memory with a different technology. So the way we time the introduction of memristors turns out to be important. There’s a lot more money being spent on understanding and modeling the market than on any of the research,” said Williams. [emphasis mine]

We might see a memristor product by summer 2014 but it could be later, as Hynix balances memristor device revenues, starting from zero, cutting into flash revenues in the millions of dollars.

I think the reason innovation is often introduced by outsiders is that they have no vested interest in maintaining the status quo as per the situation with Hynix and HP Labs, i.e., not wanting to cannibalize a current and profitable product line by introducing something new and, one gathers, an improvement.