Category Archives: neuromorphic engineering

New chip for neuromorphic computing runs at a fraction of the energy of today’s systems

An August 17, 2022 news item on Nanowerk announces big (so to speak) claims from a team researching neuromorphic (brainlike) computer chips,

An international team of researchers has designed and built a chip that runs computations directly in memory and can run a wide variety of artificial intelligence (AI) applications–all at a fraction of the energy consumed by computing platforms for general-purpose AI computing.

The NeuRRAM neuromorphic chip brings AI a step closer to running on a broad range of edge devices, disconnected from the cloud, where they can perform sophisticated cognitive tasks anywhere and anytime without relying on a network connection to a centralized server. Applications abound in every corner of the world and every facet of our lives, and range from smart watches, to VR headsets, smart earbuds, smart sensors in factories and rovers for space exploration.

The NeuRRAM chip is not only twice as energy efficient as the state-of-the-art “compute-in-memory” chips, an innovative class of hybrid chips that runs computations in memory, it also delivers results that are just as accurate as conventional digital chips. Conventional AI platforms are a lot bulkier and typically are constrained to using large data servers operating in the cloud.

In addition, the NeuRRAM chip is highly versatile and supports many different neural network models and architectures. As a result, the chip can be used for many different applications, including image recognition and reconstruction as well as voice recognition.

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An August 17, 2022 University of California at San Diego (UCSD) news release (also on EurekAlert), which originated the news item, provides more detail than usually found in a news release,

“The conventional wisdom is that the higher efficiency of compute-in-memory is at the cost of versatility, but our NeuRRAM chip obtains efficiency while not sacrificing versatility,” said Weier Wan, the paper’s first corresponding author and a recent Ph.D. graduate of Stanford University who worked on the chip while at UC San Diego, where he was co-advised by Gert Cauwenberghs in the Department of Bioengineering. 

The research team, co-led by bioengineers at the University of California San Diego, presents their results in the Aug. 17 [2022] issue of Nature.

Currently, AI computing is both power hungry and computationally expensive. Most AI applications on edge devices involve moving data from the devices to the cloud, where the AI processes and analyzes it. Then the results are moved back to the device. That’s because most edge devices are battery-powered and as a result only have a limited amount of power that can be dedicated to computing. 

By reducing power consumption needed for AI inference at the edge, this NeuRRAM chip could lead to more robust, smarter and accessible edge devices and smarter manufacturing. It could also lead to better data privacy as the transfer of data from devices to the cloud comes with increased security risks. 

On AI chips, moving data from memory to computing units is one major bottleneck. 

“It’s the equivalent of doing an eight-hour commute for a two-hour work day,” Wan said. 

To solve this data transfer issue, researchers used what is known as resistive random-access memory, a type of non-volatile memory that allows for computation directly within memory rather than in separate computing units. RRAM and other emerging memory technologies used as synapse arrays for neuromorphic computing were pioneered in the lab of Philip Wong, Wan’s advisor at Stanford and a main contributor to this work. Computation with RRAM chips is not necessarily new, but generally it leads to a decrease in the accuracy of the computations performed on the chip and a lack of flexibility in the chip’s architecture. 

“Compute-in-memory has been common practice in neuromorphic engineering since it was introduced more than 30 years ago,” Cauwenberghs said.  “What is new with NeuRRAM is that the extreme efficiency now goes together with great flexibility for diverse AI applications with almost no loss in accuracy over standard digital general-purpose compute platforms.”

A carefully crafted methodology was key to the work with multiple levels of “co-optimization” across the abstraction layers of hardware and software, from the design of the chip to its configuration to run various AI tasks. In addition, the team made sure to account for various constraints that span from memory device physics to circuits and network architecture. 

“This chip now provides us with a platform to address these problems across the stack from devices and circuits to algorithms,” said Siddharth Joshi, an assistant professor of computer science and engineering at the University of Notre Dame , who started working on the project as a Ph.D. student and postdoctoral researcher in Cauwenberghs lab at UC San Diego. 

Chip performance

Researchers measured the chip’s energy efficiency by a measure known as energy-delay product, or EDP. EDP combines both the amount of energy consumed for every operation and the amount of times it takes to complete the operation. By this measure, the NeuRRAM chip achieves 1.6 to 2.3 times lower EDP (lower is better) and 7 to 13 times higher computational density than state-of-the-art chips. 

Researchers ran various AI tasks on the chip. It achieved 99% accuracy on a handwritten digit recognition task; 85.7% on an image classification task; and 84.7% on a Google speech command recognition task. In addition, the chip also achieved a 70% reduction in image-reconstruction error on an image-recovery task. These results are comparable to existing digital chips that perform computation under the same bit-precision, but with drastic savings in energy. 

Researchers point out that one key contribution of the paper is that all the results featured are obtained directly on the hardware. In many previous works of compute-in-memory chips, AI benchmark results were often obtained partially by software simulation. 

Next steps include improving architectures and circuits and scaling the design to more advanced technology nodes. Researchers also plan to tackle other applications, such as spiking neural networks.

“We can do better at the device level, improve circuit design to implement additional features and address diverse applications with our dynamic NeuRRAM platform,” said Rajkumar Kubendran, an assistant professor for the University of Pittsburgh, who started work on the project while a Ph.D. student in Cauwenberghs’ research group at UC San Diego.

In addition, Wan is a founding member of a startup that works on productizing the compute-in-memory technology. “As a researcher and  an engineer, my ambition is to bring research innovations from labs into practical use,” Wan said. 

New architecture 

The key to NeuRRAM’s energy efficiency is an innovative method to sense output in memory. Conventional approaches use voltage as input and measure current as the result. But this leads to the need for more complex and more power hungry circuits. In NeuRRAM, the team engineered a neuron circuit that senses voltage and performs analog-to-digital conversion in an energy efficient manner. This voltage-mode sensing can activate all the rows and all the columns of an RRAM array in a single computing cycle, allowing higher parallelism. 

In the NeuRRAM architecture, CMOS neuron circuits are physically interleaved with RRAM weights. It differs from conventional designs where CMOS circuits are typically on the peripheral of RRAM weights.The neuron’s connections with the RRAM array can be configured to serve as either input or output of the neuron. This allows neural network inference in various data flow directions without incurring overheads in area or power consumption. This in turn makes the architecture easier to reconfigure. 

To make sure that accuracy of the AI computations can be preserved across various neural network architectures, researchers developed a set of hardware algorithm co-optimization techniques. The techniques were verified on various neural networks including convolutional neural networks, long short-term memory, and restricted Boltzmann machines. 

As a neuromorphic AI chip, NeuroRRAM performs parallel distributed processing across 48 neurosynaptic cores. To simultaneously achieve high versatility and high efficiency, NeuRRAM supports data-parallelism by mapping a layer in the neural network model onto multiple cores for parallel inference on multiple data. Also, NeuRRAM offers model-parallelism by mapping different layers of a model onto different cores and performing inference in a pipelined fashion.

An international research team

The work is the result of an international team of researchers. 

The UC San Diego team designed the CMOS circuits that implement the neural functions interfacing with the RRAM arrays to support the synaptic functions in the chip’s architecture, for high efficiency and versatility. Wan, working closely with the entire team, implemented the design; characterized the chip; trained the AI models; and executed the experiments. Wan also developed a software toolchain that maps AI applications onto the chip. 

The RRAM synapse array and its operating conditions were extensively characterized and optimized at Stanford University. 

The RRAM array was fabricated and integrated onto CMOS at Tsinghua University. 

The Team at Notre Dame contributed to both the design and architecture of the chip and the subsequent machine learning model design and training.

The research started as part of the National Science Foundation funded Expeditions in Computing project on Visual Cortex on Silicon at Penn State University, with continued funding support from the Office of Naval Research Science of AI program, the Semiconductor Research Corporation and DARPA [{US} Defense Advanced Research Projects Agency] JUMP program, and Western Digital Corporation. 

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

A compute-in-memory chip based on resistive random-access memory by Weier Wan, Rajkumar Kubendran, Clemens Schaefer, Sukru Burc Eryilmaz, Wenqiang Zhang, Dabin Wu, Stephen Deiss, Priyanka Raina, He Qian, Bin Gao, Siddharth Joshi, Huaqiang Wu, H.-S. Philip Wong & Gert Cauwenberghs. Nature volume 608, pages 504–512 (2022) DOI: https://doi.org/10.1038/s41586-022-04992-8 Published: 17 August 2022 Issue Date: 18 August 2022

This paper is open access.

Synaptic transistors for brainlike computers based on (more environmentally friendly) graphene

An August 9, 2022 news item on ScienceDaily describes research investigating materials other than silicon for neuromorphic (brainlike) computing purposes,

Computers that think more like human brains are inching closer to mainstream adoption. But many unanswered questions remain. Among the most pressing, what types of materials can serve as the best building blocks to unlock the potential of this new style of computing.

For most traditional computing devices, silicon remains the gold standard. However, there is a movement to use more flexible, efficient and environmentally friendly materials for these brain-like devices.

In a new paper, researchers from The University of Texas at Austin developed synaptic transistors for brain-like computers using the thin, flexible material graphene. These transistors are similar to synapses in the brain, that connect neurons to each other.

An August 8, 2022 University of Texas at Austin news release (also on EurekAlert but published August 9, 2022), which originated the news item, provides more detail about the research,

“Computers that think like brains can do so much more than today’s devices,” said Jean Anne Incorvia, an assistant professor in the Cockrell School of Engineering’s Department of Electrical and Computer Engineer and the lead author on the paper published today in Nature Communications. “And by mimicking synapses, we can teach these devices to learn on the fly, without requiring huge training methods that take up so much power.”

The Research: A combination of graphene and nafion, a polymer membrane material, make up the backbone of the synaptic transistor. Together, these materials demonstrate key synaptic-like behaviors — most importantly, the ability for the pathways to strengthen over time as they are used more often, a type of neural muscle memory. In computing, this means that devices will be able to get better at tasks like recognizing and interpreting images over time and do it faster.

Another important finding is that these transistors are biocompatible, which means they can interact with living cells and tissue. That is key for potential applications in medical devices that come into contact with the human body. Most materials used for these early brain-like devices are toxic, so they would not be able to contact living cells in any way.

Why It Matters: With new high-tech concepts like self-driving cars, drones and robots, we are reaching the limits of what silicon chips can efficiently do in terms of data processing and storage. For these next-generation technologies, a new computing paradigm is needed. Neuromorphic devices mimic processing capabilities of the brain, a powerful computer for immersive tasks.

“Biocompatibility, flexibility, and softness of our artificial synapses is essential,” said Dmitry Kireev, a post-doctoral researcher who co-led the project. “In the future, we envision their direct integration with the human brain, paving the way for futuristic brain prosthesis.”

Will It Really Happen: Neuromorphic platforms are starting to become more common. Leading chipmakers such as Intel and Samsung have either produced neuromorphic chips already or are in the process of developing them. However, current chip materials place limitations on what neuromorphic devices can do, so academic researchers are working hard to find the perfect materials for soft brain-like computers.

“It’s still a big open space when it comes to materials; it hasn’t been narrowed down to the next big solution to try,” Incorvia said. “And it might not be narrowed down to just one solution, with different materials making more sense for different applications.”

The Team: The research was led by Incorvia and Deji Akinwande, professor in the Department of Electrical and Computer Engineering. The two have collaborated many times together in the past, and Akinwande is a leading expert in graphene, using it in multiple research breakthroughs, most recently as part of a wearable electronic tattoo for blood pressure monitoring.

The idea for the project was conceived by Samuel Liu, a Ph.D. student and first author on the paper, in a class taught by Akinwande. Kireev then suggested the specific project. Harrison Jin, an undergraduate electrical and computer engineering student, measured the devices and analyzed data.

The team collaborated with T. Patrick Xiao and Christopher Bennett of Sandia National Laboratories, who ran neural network simulations and analyzed the resulting data.

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

Metaplastic and energy-efficient biocompatible graphene artificial synaptic transistors for enhanced accuracy neuromorphic computing by Dmitry Kireev, Samuel Liu, Harrison Jin, T. Patrick Xiao, Christopher H. Bennett, Deji Akinwande & Jean Anne C. Incorvia. Nature Communications volume 13, Article number: 4386 (2022) DOI: https://doi.org/10.1038/s41467-022-32078-6 Published: 28 July 2022

This paper is open access.

Neuromorphic computing and liquid-light interaction

Simulation result of light affecting liquid geometry, which in turn affects reflection and transmission properties of the optical mode, thus constituting a two-way light–liquid interaction mechanism. The degree of deformation serves as an optical memory allowing to store the power magnitude of the previous optical pulse and use fluid dynamics to affect the subsequent optical pulse at the same actuation region, thus constituting an architecture where memory is part of the computation process. Credit: Gao et al., doi 10.1117/1.AP.4.4.046005

This is a fascinating approach to neuromorphic (brainlike) computing and given my recent post (August 29, 2022) about human cells being incorporated into computer chips, it’s part o my recent spate of posts about neuromorphic computing. From a July 25, 2022 news item on phys.org,

Sunlight sparkling on water evokes the rich phenomena of liquid-light interaction, spanning spatial and temporal scales. While the dynamics of liquids have fascinated researchers for decades, the rise of neuromorphic computing has sparked significant efforts to develop new, unconventional computational schemes based on recurrent neural networks, crucial to supporting wide range of modern technological applications, such as pattern recognition and autonomous driving. As biological neurons also rely on a liquid environment, a convergence may be attained by bringing nanoscale nonlinear fluid dynamics to neuromorphic computing.

A July 25, 2022 SPIE (International Society for Optics and Photonics) press release (also on EurekAlert), which originated the news item,

Researchers from University of California San Diego recently proposed a novel paradigm where liquids, which usually do not strongly interact with light on a micro- or nanoscale, support significant nonlinear response to optical fields. As reported in Advanced Photonics, the researchers predict a substantial light–liquid interaction effect through a proposed nanoscale gold patch operating as an optical heater and generating thickness changes in a liquid film covering the waveguide.

The liquid film functions as an optical memory. Here’s how it works: Light in the waveguide affects the geometry of the liquid surface, while changes in the shape of the liquid surface affect the properties of the optical mode in the waveguide, thus constituting a mutual coupling between the optical mode and the liquid film. Importantly, as the liquid geometry changes, the properties of the optical mode undergo a nonlinear response; after the optical pulse stops, the magnitude of liquid film’s deformation indicates the power of the previous optical pulse.

Remarkably, unlike traditional computational approaches, the nonlinear response and the memory reside at the same spatial region, thus suggesting realization of a compact (beyond von-Neumann) architecture where memory and computational unit occupy the same space. The researchers demonstrate that the combination of memory and nonlinearity allow the possibility of “reservoir computing” capable of performing digital and analog tasks, such as nonlinear logic gates and handwritten image recognition.

Their model also exploits another significant liquid feature: nonlocality. This enables them to predict computation enhancement that is simply not possible in solid state material platforms with limited nonlocal spatial scale. Despite nonlocality, the model does not quite achieve the levels of modern solid-state optics-based reservoir computing systems, yet the work nonetheless presents a clear roadmap for future experimental works aiming to validate the predicted effects and explore intricate coupling mechanisms of various physical processes in a liquid environment for computation.

Using multiphysics simulations to investigate coupling between light, fluid dynamics, heat transport, and surface tension effects, the researchers predict a family of novel nonlinear and nonlocal optical effects. They go a step further by indicating how these can be used to realize versatile, nonconventional computational platforms. Taking advantage of a mature silicon photonics platform, they suggest improvements to state-of-the-art liquid-assisted computation platforms by around five orders magnitude in space and at least two orders of magnitude in speed.

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

Thin liquid film as an optical nonlinear-nonlocal medium and memory element in integrated optofluidic reservoir computer by Chengkuan Gao, Prabhav Gaur, Shimon Rubin, Yeshaiahu Fainman. Advanced Photonics, 4(4), 046005 (2022). https://doi.org/10.1117/1.AP.4.4.046005 Published: 1 July 2022

This paper is open access.

Guide for memristive hardware design

An August 15 ,2022 news item on ScienceDaily announces a type of guide for memristive hardware design,

They are many times faster than flash memory and require significantly less energy: memristive memory cells could revolutionize the energy efficiency of neuromorphic [brainlike] computers. In these computers, which are modeled on the way the human brain works, memristive cells function like artificial synapses. Numerous groups around the world are working on the use of corresponding neuromorphic circuits — but often with a lack of understanding of how they work and with faulty models. Jülich researchers have now summarized the physical principles and models in a comprehensive review article in the renowned journal Advances in Physics.

An August 15, 2022 Forschungszentrum Juelich press release (also on EurekAlert), which originated the news item, describes two papers designed to help researchers better understand and design memristive hardware,

Certain tasks – such as recognizing patterns and language – are performed highly efficiently by a human brain, requiring only about one ten-thousandth of the energy of a conventional, so-called “von Neumann” computer. One of the reasons lies in the structural differences: In a von Neumann architecture, there is a clear separation between memory and processor, which requires constant moving of large amounts of data. This is time and energy consuming – the so-called von Neumann bottleneck. In the brain, the computational operation takes place directly in the data memory and the biological synapses perform the tasks of memory and processor at the same time.

In Jülich, scientists have been working for more than 15 years on special data storage devices and components that can have similar properties to the synapses in the human brain. So-called memristive memory devices, also known as memristors, are considered to be extremely fast, energy-saving and can be miniaturized very well down to the nanometer range. The functioning of memristive cells is based on a very special effect: Their electrical resistance is not constant, but can be changed and reset again by applying an external voltage, theoretically continuously. The change in resistance is controlled by the movement of oxygen ions. If these move out of the semiconducting metal oxide layer, the material becomes more conductive and the electrical resistance drops. This change in resistance can be used to store information.

The processes that can occur in cells are very complex and vary depending on the material system. Three researchers from the Jülich Peter Grünberg Institute – Prof. Regina Dittmann, Dr. Stephan Menzel, and Prof. Rainer Waser – have therefore compiled their research results in a detailed review article, “Nanoionic memristive phenomena in metal oxides: the valence change mechanism”. They explain in detail the various physical and chemical effects in memristors and shed light on the influence of these effects on the switching properties of memristive cells and their reliability.

“If you look at current research activities in the field of neuromorphic memristor circuits, they are often based on empirical approaches to material optimization,” said Rainer Waser, director at the Peter Grünberg Institute. “Our goal with our review article is to give researchers something to work with in order to enable insight-driven material optimization.” The team of authors worked on the approximately 200-page article for ten years and naturally had to keep incorporating advances in knowledge.

“The analogous functioning of memristive cells required for their use as artificial synapses is not the normal case. Usually, there are sudden jumps in resistance, generated by the mutual amplification of ionic motion and Joule heat,” explains Regina Dittmann of the Peter Grünberg Institute. “In our review article, we provide researchers with the necessary understanding of how to change the dynamics of the cells to enable an analog operating mode.”

“You see time and again that groups simulate their memristor circuits with models that don’t take into account high dynamics of the cells at all. These circuits will never work.” said Stephan Menzel, who leads modeling activities at the Peter Grünberg Institute and has developed powerful compact models that are now in the public domain (www.emrl.de/jart.html). “In our review article, we provide the basics that are extremely helpful for a correct use of our compact models.”

Roadmap neuromorphic computing

The “Roadmap of Neuromorphic Computing and Engineering”, which was published in May 2022, shows how neuromorphic computing can help to reduce the enormous energy consumption of IT globally. In it, researchers from the Peter Grünberg Institute (PGI-7), together with leading experts in the field, have compiled the various technological possibilities, computational approaches, learning algorithms and fields of application. 

According to the study, applications in the field of artificial intelligence, such as pattern recognition or speech recognition, are likely to benefit in a special way from the use of neuromorphic hardware. This is because they are based – much more so than classical numerical computing operations – on the shifting of large amounts of data. Memristive cells make it possible to process these gigantic data sets directly in memory without transporting them back and forth between processor and memory. This could reduce the energy efficiency of artificial neural networks by orders of magnitude.

Memristive cells can also be interconnected to form high-density matrices that enable neural networks to learn locally. This so-called edge computing thus shifts computations from the data center to the factory floor, the vehicle, or the home of people in need of care. Thus, monitoring and controlling processes or initiating rescue measures can be done without sending data via a cloud. “This achieves two things at the same time: you save energy, and at the same time, personal data and data relevant to security remain on site,” says Prof. Dittmann, who played a key role in creating the roadmap as editor.

Here’s a link to and a citation for the ‘roadmap’,

2022 roadmap on neuromorphic computing and engineering by Dennis V Christensen, Regina Dittmann, Bernabe Linares-Barranco, Abu Sebastian, Manuel Le Gallo, Andrea Redaelli, Stefan Slesazeck, Thomas Mikolajick, Sabina Spiga, Stephan Menzel, Ilia Valov, Gianluca Milano, Carlo Ricciardi, Shi-Jun Liang, Feng Miao, Mario Lanza, Tyler J Quill, Scott T Keene, Alberto Salleo, Julie Grollier, Danijela Marković, Alice Mizrahi, Peng Yao, J Joshua Yang, Giacomo Indiveri, John Paul Strachan, Suman Datta, Elisa Vianello, Alexandre Valentian, Johannes Feldmann, Xuan Li, Wolfram H P Pernice, Harish Bhaskaran, Steve Furber, Emre Neftci, Franz Scherr, Wolfgang Maass, Srikanth Ramaswamy, Jonathan Tapson, Priyadarshini Panda, Youngeun Kim, Gouhei Tanaka, Simon Thorpe, Chiara Bartolozzi, Thomas A Cleland, Christoph Posch, ShihChii Liu, Gabriella Panuccio, Mufti Mahmud, Arnab Neelim Mazumder, Morteza Hosseini, Tinoosh Mohsenin, Elisa Donati, Silvia Tolu, Roberto Galeazzi, Martin Ejsing Christensen, Sune Holm, Daniele Ielmini and N Pryds. Neuromorphic Computing and Engineering , Volume 2, Number 2 DOI: 10.1088/2634-4386/ac4a83 20 May 2022 • © 2022 The Author(s)

This paper is open access.

Here’s the most recent paper,

Nanoionic memristive phenomena in metal oxides: the valence change mechanism by Regina Dittmann, Stephan Menzel & Rainer Waser. Advances in Physics
Volume 70, 2021 – Issue 2 Pages 155-349 DOI: https://doi.org/10.1080/00018732.2022.2084006 Published online: 06 Aug 2022

This paper is behind a paywall.

Memristive forming strategy

This is highly technical and it’s here since I’m informally collecting all the research that I stumble across concerning memristors and neuromorphic engineering.

From a Sept. 5, 2022 news item on Nanowerk, Note: A link has been removed,

The silicon-based CMOS [complementary metal-oxide-semiconductor] technology is fast approaching its physical limits, and the electronics industry is urgently calling for new techniques to keep the long-term development. Two-dimensional (2D) semiconductors, like transition-metal dichalcogenides (TMDs), have become a competitive alternative to traditional semiconducting materials in the post-Moore era, and caused worldwide interest. However, before they can be used in practical applications, some key obstacles must be resolved. One of them is the large electrical contact resistances at the metal-semiconductor interfaces.

The large contact resistances mainly come from two aspects: the high tunneling barrier caused by the wide van der Waals (vdW) gap between the 2D material and the metal electrode; the high Schottky barrier accompanied by strong Fermi level pinning at the metal-semiconductor interface.

Four strategies including edge contact, doping TMDs, phase engineering, and using special metals, have been developed to address this problem. However, they all have shortcomings.

In a new work (Nano Letters, “Van der Waals Epitaxy and Photoresponse of Hexagonal Tellurium Nanoplates on Flexible Mica Sheets”) coming out of Zhenxing Wang’s group at the National Center for Nanoscience and Technology [located in Beijing, China], the researchers have proposed a brand-new contact resistance lowering strategy of 2D semiconductors with a good feasibility, a wide generality and a high stability.

You can fill in the blanks at Nanowerk or there’s this link and citation for the paper

Van der Waals Epitaxy and Photoresponse of Hexagonal Tellurium Nanoplates on Flexible Mica Sheets by Qisheng Wang, Muhammad Safdar, Kai Xu, Misbah Mirza, Zhenxing Wang, and Jun He. ACS Nano 2014, 8, 7, 7497–7505 DOI: https://doi.org/10.1021/nn5028104 Publication Date:July 2, 2014 Copyright © 2014 American Chemical Society

This paper is behind a paywall.

Reconfiguring a LEGO-like AI chip with light

MIT engineers have created a reconfigurable AI chip that comprises alternating layers of sensing and processing elements that can communicate with each other. Credit: Figure courtesy of the researchers and edited by MIT News

This image certainly challenges any ideas I have about what Lego looks like. It seems they see things differently at the Massachusetts Institute of Technology (MIT). From a June 13, 2022 MIT news release (also on EurekAlert),

Imagine a more sustainable future, where cellphones, smartwatches, and other wearable devices don’t have to be shelved or discarded for a newer model. Instead, they could be upgraded with the latest sensors and processors that would snap onto a device’s internal chip — like LEGO bricks incorporated into an existing build. Such reconfigurable chipware could keep devices up to date while reducing our electronic waste. 

Now MIT engineers have taken a step toward that modular vision with a LEGO-like design for a stackable, reconfigurable artificial intelligence chip.

The design comprises alternating layers of sensing and processing elements, along with light-emitting diodes (LED) that allow for the chip’s layers to communicate optically. Other modular chip designs employ conventional wiring to relay signals between layers. Such intricate connections are difficult if not impossible to sever and rewire, making such stackable designs not reconfigurable.

The MIT design uses light, rather than physical wires, to transmit information through the chip. The chip can therefore be reconfigured, with layers that can be swapped out or stacked on, for instance to add new sensors or updated processors.

“You can add as many computing layers and sensors as you want, such as for light, pressure, and even smell,” says MIT postdoc Jihoon Kang. “We call this a LEGO-like reconfigurable AI chip because it has unlimited expandability depending on the combination of layers.”

The researchers are eager to apply the design to edge computing devices — self-sufficient sensors and other electronics that work independently from any central or distributed resources such as supercomputers or cloud-based computing.

“As we enter the era of the internet of things based on sensor networks, demand for multifunctioning edge-computing devices will expand dramatically,” says Jeehwan Kim, associate professor of mechanical engineering at MIT. “Our proposed hardware architecture will provide high versatility of edge computing in the future.”

The team’s results are published today in Nature Electronics. In addition to Kim and Kang, MIT authors include co-first authors Chanyeol Choi, Hyunseok Kim, and Min-Kyu Song, and contributing authors Hanwool Yeon, Celesta Chang, Jun Min Suh, Jiho Shin, Kuangye Lu, Bo-In Park, Yeongin Kim, Han Eol Lee, Doyoon Lee, Subeen Pang, Sang-Hoon Bae, Hun S. Kum, and Peng Lin, along with collaborators from Harvard University, Tsinghua University, Zhejiang University, and elsewhere.

Lighting the way

The team’s design is currently configured to carry out basic image-recognition tasks. It does so via a layering of image sensors, LEDs, and processors made from artificial synapses — arrays of memory resistors, or “memristors,” that the team previously developed, which together function as a physical neural network, or “brain-on-a-chip.” Each array can be trained to process and classify signals directly on a chip, without the need for external software or an Internet connection.

In their new chip design, the researchers paired image sensors with artificial synapse arrays, each of which they trained to recognize certain letters — in this case, M, I, and T. While a conventional approach would be to relay a sensor’s signals to a processor via physical wires, the team instead fabricated an optical system between each sensor and artificial synapse array to enable communication between the layers, without requiring a physical connection. 

“Other chips are physically wired through metal, which makes them hard to rewire and redesign, so you’d need to make a new chip if you wanted to add any new function,” says MIT postdoc Hyunseok Kim. “We replaced that physical wire connection with an optical communication system, which gives us the freedom to stack and add chips the way we want.”

The team’s optical communication system consists of paired photodetectors and LEDs, each patterned with tiny pixels. Photodetectors constitute an image sensor for receiving data, and LEDs to transmit data to the next layer. As a signal (for instance an image of a letter) reaches the image sensor, the image’s light pattern encodes a certain configuration of LED pixels, which in turn stimulates another layer of photodetectors, along with an artificial synapse array, which classifies the signal based on the pattern and strength of the incoming LED light.

Stacking up

The team fabricated a single chip, with a computing core measuring about 4 square millimeters, or about the size of a piece of confetti. The chip is stacked with three image recognition “blocks,” each comprising an image sensor, optical communication layer, and artificial synapse array for classifying one of three letters, M, I, or T. They then shone a pixellated image of random letters onto the chip and measured the electrical current that each neural network array produced in response. (The larger the current, the larger the chance that the image is indeed the letter that the particular array is trained to recognize.)

The team found that the chip correctly classified clear images of each letter, but it was less able to distinguish between blurry images, for instance between I and T. However, the researchers were able to quickly swap out the chip’s processing layer for a better “denoising” processor, and found the chip then accurately identified the images.

“We showed stackability, replaceability, and the ability to insert a new function into the chip,” notes MIT postdoc Min-Kyu Song.

The researchers plan to add more sensing and processing capabilities to the chip, and they envision the applications to be boundless.

“We can add layers to a cellphone’s camera so it could recognize more complex images, or makes these into healthcare monitors that can be embedded in wearable electronic skin,” offers Choi, who along with Kim previously developed a “smart” skin for monitoring vital signs.

Another idea, he adds, is for modular chips, built into electronics, that consumers can choose to build up with the latest sensor and processor “bricks.”

“We can make a general chip platform, and each layer could be sold separately like a video game,” Jeehwan Kim says. “We could make different types of neural networks, like for image or voice recognition, and let the customer choose what they want, and add to an existing chip like a LEGO.”

This research was supported, in part, by the Ministry of Trade, Industry, and Energy (MOTIE) from South Korea; the Korea Institute of Science and Technology (KIST); and the Samsung Global Research Outreach Program.

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

Reconfigurable heterogeneous integration using stackable chips with embedded artificial intelligence by Chanyeol Choi, Hyunseok Kim, Ji-Hoon Kang, Min-Kyu Song, Hanwool Yeon, Celesta S. Chang, Jun Min Suh, Jiho Shin, Kuangye Lu, Bo-In Park, Yeongin Kim, Han Eol Lee, Doyoon Lee, Jaeyong Lee, Ikbeom Jang, Subeen Pang, Kanghyun Ryu, Sang-Hoon Bae, Yifan Nie, Hyun S. Kum, Min-Chul Park, Suyoun Lee, Hyung-Jun Kim, Huaqiang Wu, Peng Lin & Jeehwan Kim. Nature Electronics volume 5, pages 386–393 (2022) 05 May 2022 Issue Date: June 2022 Published: 13 June 2022 DOI: https://doi.org/10.1038/s41928-022-00778-y

This paper is behind a paywall.

Photonic synapses with low power consumption (and a few observations)

This work on brainlike (neuromorphic) computing was announced in a June 30, 2022 Compuscript Ltd news release on EurekAlert,

Photonic synapses with low power consumption and high sensitivity are expected to integrate sensing-memory-preprocessing capabilities

A new publication from Opto-Electronic Advances; DOI 10.29026/oea.2022.210069 discusses how photonic synapses with low power consumption and high sensitivity are expected to integrate sensing-memory-preprocessing capabilities.

Neuromorphic photonics/electronics is the future of ultralow energy intelligent computing and artificial intelligence (AI). In recent years, inspired by the human brain, artificial neuromorphic devices have attracted extensive attention, especially in simulating visual perception and memory storage. Because of its advantages of high bandwidth, high interference immunity, ultrafast signal transmission and lower energy consumption, neuromorphic photonic devices are expected to realize real-time response to input data. In addition, photonic synapses can realize non-contact writing strategy, which contributes to the development of wireless communication. The use of low-dimensional materials provides an opportunity to develop complex brain-like systems and low-power memory logic computers. For example, large-scale, uniform and reproducible transition metal dichalcogenides (TMDs) show great potential for miniaturization and low-power biomimetic device applications due to their excellent charge-trapping properties and compatibility with traditional CMOS processes. The von Neumann architecture with discrete memory and processor leads to high power consumption and low efficiency of traditional computing. Therefore, the sensor-memory fusion or sensor-memory- processor integration neuromorphic architecture system can meet the increasingly developing demands of big data and AI for low power consumption and high performance devices. Artificial synaptic devices are the most important components of neuromorphic systems. The performance evaluation of synaptic devices will help to further apply them to more complex artificial neural networks (ANN).

Chemical vapor deposition (CVD)-grown TMDs inevitably introduce defects or impurities, showed a persistent photoconductivity (PPC) effect. TMDs photonic synapses integrating synaptic properties and optical detection capabilities show great advantages in neuromorphic systems for low-power visual information perception and processing as well as brain memory.

The research Group of Optical Detection and Sensing (GODS) have reported a three-terminal photonic synapse based on the large-area, uniform multilayer MoS2 films. The reported device realized ultrashort optical pulse detection within 5 μs and ultralow power consumption about 40 aJ, which means its performance is much better than the current reported properties of photonic synapses. Moreover, it is several orders of magnitude lower than the corresponding parameters of biological synapses, indicating that the reported photonic synapse can be further used for more complex ANN. The photoconductivity of MoS2 channel grown by CVD is regulated by photostimulation signal, which enables the device to simulate short-term synaptic plasticity (STP), long-term synaptic plasticity (LTP), paired-pulse facilitation (PPF) and other synaptic properties. Therefore, the reported photonic synapse can simulate human visual perception, and the detection wavelength can be extended to near infrared light. As the most important system of human learning, visual perception system can receive 80% of learning information from the outside. With the continuous development of AI, there is an urgent need for low-power and high sensitivity visual perception system that can effectively receive external information. In addition, with the assistant of gate voltage, this photonic synapse can simulate the classical Pavlovian conditioning and the regulation of different emotions on memory ability. For example, positive emotions enhance memory ability and negative emotions weaken memory ability. Furthermore, a significant contrast in the strength of STP and LTP based on the reported photonic synapse suggests that it can preprocess the input light signal. These results indicate that the photo-stimulation and backgate control can effectively regulate the conductivity of MoS2 channel layer by adjusting carrier trapping/detrapping processes. Moreover, the photonic synapse presented in this paper is expected to integrate sensing-memory-preprocessing capabilities, which can be used for real-time image detection and in-situ storage, and also provides the possibility to break the von Neumann bottleneck. 

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

Photonic synapses with ultralow energy consumption for artificial visual perception and brain storage by Caihong Li, Wen Du, Yixuan Huang, Jihua Zou, Lingzhi Luo, Song Sun, Alexander O. Govorov, Jiang Wu, Hongxing Xu, Zhiming Wang. Opto-Electron Adv Vol 5, No 9 210069 (2022). doi: 10.29026/oea.2022.210069

This paper is open access.

Observations

I don’t have much to say about the research itself other than, I believe this is the first time I’ve seen a news release about neuromorphic computing research from China.

it’s China that most interests me, especially these bits from the June 30, 2022 Compuscript Ltd news release on EurekAlert,

Group of Optical Detection and Sensing (GODS) [emphasis mine] was established in 2019. It is a research group focusing on compound semiconductors, lasers, photodetectors, and optical sensors. GODS has established a well-equipped laboratory with research facilities such as Molecular Beam Epitaxy system, IR detector test system, etc. GODS is leading several research projects funded by NSFC and National Key R&D Programmes. GODS have published more than 100 research articles in Nature Electronics, Light: Science and Applications, Advanced Materials and other international well-known high-level journals with the total citations beyond 8000.

Jiang Wu obtained his Ph.D. from the University of Arkansas Fayetteville in 2011. After his Ph.D., he joined UESTC as associate professor and later professor. He joined University College London [UCL] as a research associate in 2012 and then lecturer in the Department of Electronic and Electrical Engineering at UCL from 2015 to 2018. He is now a professor at UESTC [University of Electronic Science and Technology of China] [emphases mine]. His research interests include optoelectronic applications of semiconductor heterostructures. He is a Fellow of the Higher Education Academy and Senior Member of IEEE.

Opto-Electronic Advances (OEA) is a high-impact, open access, peer reviewed monthly SCI journal with an impact factor of 9.682 (Journals Citation Reports for IF 2020). Since its launch in March 2018, OEA has been indexed in SCI, EI, DOAJ, Scopus, CA and ICI databases over the time and expanded its Editorial Board to 36 members from 17 countries and regions (average h-index 49). [emphases mine]

The journal is published by The Institute of Optics and Electronics, Chinese Academy of Sciences, aiming at providing a platform for researchers, academicians, professionals, practitioners, and students to impart and share knowledge in the form of high quality empirical and theoretical research papers covering the topics of optics, photonics and optoelectronics.

The research group’s awkward name was almost certainly developed with the rather grandiose acronym, GODS, in mind. I don’t think you could get away with doing this in an English-speaking country as your colleagues would mock you mercilessly.

It’s Jiang Wu’s academic and work history that’s of most interest as it might provide insight into China’s Young Thousand Talents program. A January 5, 2023 American Association for the Advancement of Science (AAAS) news release describes the program,

In a systematic evaluation of China’s Young Thousand Talents (YTT) program, which was established in 2010, researchers find that China has been successful in recruiting and nurturing high-caliber Chinese scientists who received training abroad. Many of these individuals outperform overseas peers in publications and access to funding, the study shows, largely due to access to larger research teams and better research funding in China. Not only do the findings demonstrate the program’s relative success, but they also hold policy implications for the increasing number of governments pursuing means to tap expatriates for domestic knowledge production and talent development. China is a top sender of international students to United States and European Union science and engineering programs. The YTT program was created to recruit and nurture the productivity of high-caliber, early-career, expatriate scientists who return to China after receiving Ph.Ds. abroad. Although there has been a great deal of international attention on the YTT, some associated with the launch of the U.S.’s controversial China Initiative and federal investigations into academic researchers with ties to China, there has been little evidence-based research on the success, impact, and policy implications of the program itself. Dongbo Shi and colleagues evaluated the YTT program’s first 4 cohorts of scholars and compared their research productivity to that of their peers that remained overseas. Shi et al. found that China’s YTT program successfully attracted high-caliber – but not top-caliber – scientists. However, those young scientists that did return outperformed others in publications across journal-quality tiers – particularly in last-authored publications. The authors suggest that this is due to YTT scholars’ greater access to larger research teams and better research funding in China. The authors say the dearth of such resources in the U.S. and E.U. “may not only expedite expatriates’ return decisions but also motivate young U.S.- and E.U.-born scientists to seek international research opportunities.” They say their findings underscore the need for policy adjustments to allocate more support for young scientists.

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

Has China’s Young Thousand Talents program been successful in recruiting and nurturing top-caliber scientists? by Dongbo Shi, Weichen Liu, and Yanbo Wang. Science 5 Jan 2023 Vol 379, Issue 6627 pp. 62-65 DOI: 10.1126/science.abq1218

This paper is behind a paywall.

Kudos to the folks behind China’s Young Thousands Talents program! Jiang Wu’s career appears to be a prime example of the program’s success. Perhaps Canadian policy makers will be inspired.

FrogHeart’s 2022 comes to an end as 2023 comes into view

I look forward to 2023 and hope it will be as stimulating as 2022 proved to be. Here’s an overview of the year that was on this blog:

Sounds of science

It seems 2022 was the year that science discovered the importance of sound and the possibilities of data sonification. Neither is new but this year seemed to signal a surge of interest or maybe I just happened to stumble onto more of the stories than usual.

This is not an exhaustive list, you can check out my ‘Music’ category for more here. I have tried to include audio files with the postings but it all depends on how accessible the researchers have made them.

Aliens on earth: machinic biology and/or biological machinery?

When I first started following stories in 2008 (?) about technology or machinery being integrated with the human body, it was mostly about assistive technologies such as neuroprosthetics. You’ll find most of this year’s material in the ‘Human Enhancement’ category or you can search the tag ‘machine/flesh’.

However, the line between biology and machine became a bit more blurry for me this year. You can see what’s happening in the titles listed below (you may recognize the zenobot story; there was an earlier version of xenobots featured here in 2021):

This was the story that shook me,

Are the aliens going to come from outer space or are we becoming the aliens?

Brains (biological and otherwise), AI, & our latest age of anxiety

As we integrate machines into our bodies, including our brains, there are new issues to consider:

  • Going blind when your neural implant company flirts with bankruptcy (long read) April 5, 2022 posting
  • US National Academies Sept. 22-23, 2022 workshop on techno, legal & ethical issues of brain-machine interfaces (BMIs) September 21, 2022 posting

I hope the US National Academies issues a report on their “Brain-Machine and Related Neural Interface Technologies: Scientific, Technical, Ethical, and Regulatory Issues – A Workshop” for 2023.

Meanwhile the race to create brainlike computers continues and I have a number of posts which can be found under the category of ‘neuromorphic engineering’ or you can use these search terms ‘brainlike computing’ and ‘memristors’.

On the artificial intelligence (AI) side of things, I finally broke down and added an ‘artificial intelligence (AI) category to this blog sometime between May and August 2021. Previously, I had used the ‘robots’ category as a catchall. There are other stories but these ones feature public engagement and policy (btw, it’s a Canadian Science Policy Centre event), respectively,

  • “The “We are AI” series gives citizens a primer on AI” March 23, 2022 posting
  • “Age of AI and Big Data – Impact on Justice, Human Rights and Privacy Zoom event on September 28, 2022 at 12 – 1:30 pm EDT” September 16, 2022 posting

These stories feature problems, which aren’t new but seem to be getting more attention,

While there have been issues over AI, the arts, and creativity previously, this year they sprang into high relief. The list starts with my two-part review of the Vancouver Art Gallery’s AI show; I share most of my concerns in part two. The third post covers intellectual property issues (mostly visual arts but literary arts get a nod too). The fourth post upends the discussion,

  • “Mad, bad, and dangerous to know? Artificial Intelligence at the Vancouver (Canada) Art Gallery (1 of 2): The Objects” July 28, 2022 posting
  • “Mad, bad, and dangerous to know? Artificial Intelligence at the Vancouver (Canada) Art Gallery (2 of 2): Meditations” July 28, 2022 posting
  • “AI (artificial intelligence) and art ethics: a debate + a Botto (AI artist) October 2022 exhibition in the Uk” October 24, 2022 posting
  • Should AI algorithms get patents for their inventions and is anyone talking about copyright for texts written by AI algorithms? August 30, 2022 posting

Interestingly, most of the concerns seem to be coming from the visual and literary arts communities; I haven’t come across major concerns from the music community. (The curious can check out Vancouver’s Metacreation Lab for Artificial Intelligence [located on a Simon Fraser University campus]. I haven’t seen any cautionary or warning essays there; it’s run by an AI and creativity enthusiast [professor Philippe Pasquier]. The dominant but not sole focus is art, i.e., music and AI.)

There is a ‘new kid on the block’ which has been attracting a lot of attention this month. If you’re curious about the latest and greatest AI anxiety,

  • Peter Csathy’s December 21, 2022 Yahoo News article (originally published in The WRAP) makes this proclamation in the headline “Chat GPT Proves That AI Could Be a Major Threat to Hollywood Creatives – and Not Just Below the Line | PRO Insight”
  • Mouhamad Rachini’s December 15, 2022 article for the Canadian Broadcasting Corporation’s (CBC) online news overs a more generalized overview of the ‘new kid’ along with an embedded CBC Radio file which runs approximately 19 mins. 30 secs. It’s titled “ChatGPT a ‘landmark event’ for AI, but what does it mean for the future of human labour and disinformation?” The chat bot’s developer, OpenAI, has been mentioned here many times including the previously listed July 28, 2022 posting (part two of the VAG review) and the October 24, 2022 posting.

Opposite world (quantum physics in Canada)

Quantum computing made more of an impact here (my blog) than usual. it started in 2021 with the announcement of a National Quantum Strategy in the Canadian federal government budget for that year and gained some momentum in 2022:

  • “Quantum Mechanics & Gravity conference (August 15 – 19, 2022) launches Vancouver (Canada)-based Quantum Gravity Institute and more” July 26, 2022 posting Note: This turned into one of my ‘in depth’ pieces where I comment on the ‘Canadian quantum scene’ and highlight the appointment of an expert panel for the Council of Canada Academies’ report on Quantum Technologies.
  • “Bank of Canada and Multiverse Computing model complex networks & cryptocurrencies with quantum computing” July 25, 2022 posting
  • “Canada, quantum technology, and a public relations campaign?” December 29, 2022 posting

This one was a bit of a puzzle with regard to placement in this end-of-year review, it’s quantum but it’s also about brainlike computing

It’s getting hot in here

Fusion energy made some news this year.

There’s a Vancouver area company, General Fusion, highlighted in both postings and the October posting includes an embedded video of Canadian-born rapper Baba Brinkman’s “You Must LENR” [L ow E nergy N uclear R eactions or sometimes L attice E nabled N anoscale R eactions or Cold Fusion or CANR (C hemically A ssisted N uclear R eactions)].

BTW, fusion energy can generate temperatures up to 150 million degrees Celsius.

Ukraine, science, war, and unintended consequences

Here’s what you might expect,

These are the unintended consequences (from Rachel Kyte’s, Dean of the Fletcher School, Tufts University, December 26, 2022 essay on The Conversation [h/t December 27, 2022 news item on phys.org]), Note: Links have been removed,

Russian President Vladimir Putin’s war on Ukraine has reverberated through Europe and spread to other countries that have long been dependent on the region for natural gas. But while oil-producing countries and gas lobbyists are arguing for more drilling, global energy investments reflect a quickening transition to cleaner energy. [emphasis mine]

Call it the Putin effect – Russia’s war is speeding up the global shift away from fossil fuels.

In December [2022?], the International Energy Agency [IEA] published two important reports that point to the future of renewable energy.

First, the IEA revised its projection of renewable energy growth upward by 30%. It now expects the world to install as much solar and wind power in the next five years as it installed in the past 50 years.

The second report showed that energy use is becoming more efficient globally, with efficiency increasing by about 2% per year. As energy analyst Kingsmill Bond at the energy research group RMI noted, the two reports together suggest that fossil fuel demand may have peaked. While some low-income countries have been eager for deals to tap their fossil fuel resources, the IEA warns that new fossil fuel production risks becoming stranded, or uneconomic, in the next 20 years.

Kyte’s essay is not all ‘sweetness and light’ but it does provide a little optimism.

Kudos, nanotechnology, culture (pop & otherwise), fun, and a farewell in 2022

This one was a surprise for me,

Sometimes I like to know where the money comes from and I was delighted to learn of the Ărramăt Project funded through the federal government’s New Frontiers in Research Fund (NFRF). Here’s more about the Ărramăt Project from the February 14, 2022 posting,

“The Ărramăt Project is about respecting the inherent dignity and interconnectedness of peoples and Mother Earth, life and livelihood, identity and expression, biodiversity and sustainability, and stewardship and well-being. Arramăt is a word from the Tamasheq language spoken by the Tuareg people of the Sahel and Sahara regions which reflects this holistic worldview.” (Mariam Wallet Aboubakrine)

Over 150 Indigenous organizations, universities, and other partners will work together to highlight the complex problems of biodiversity loss and its implications for health and well-being. The project Team will take a broad approach and be inclusive of many different worldviews and methods for research (i.e., intersectionality, interdisciplinary, transdisciplinary). Activities will occur in 70 different kinds of ecosystems that are also spiritually, culturally, and economically important to Indigenous Peoples.

The project is led by Indigenous scholars and activists …

Kudos to the federal government and all those involved in the Salmon science camps, the Ărramăt Project, and other NFRF projects.

There are many other nanotechnology posts here but this appeals to my need for something lighter at this point,

  • “Say goodbye to crunchy (ice crystal-laden) in ice cream thanks to cellulose nanocrystals (CNC)” August 22, 2022 posting

The following posts tend to be culture-related, high and/or low but always with a science/nanotechnology edge,

Sadly, it looks like 2022 is the last year that Ada Lovelace Day is to be celebrated.

… this year’s Ada Lovelace Day is the final such event due to lack of financial backing. Suw Charman-Anderson told the BBC [British Broadcasting Corporation] the reason it was now coming to an end was:

You can read more about it here:

In the rearview mirror

A few things that didn’t fit under the previous heads but stood out for me this year. Science podcasts, which were a big feature in 2021, also proliferated in 2022. I think they might have peaked and now (in 2023) we’ll see what survives.

Nanotechnology, the main subject on this blog, continues to be investigated and increasingly integrated into products. You can search the ‘nanotechnology’ category here for posts of interest something I just tried. It surprises even me (I should know better) how broadly nanotechnology is researched and applied.

If you want a nice tidy list, Hamish Johnston in a December 29, 2022 posting on the Physics World Materials blog has this “Materials and nanotechnology: our favourite research in 2022,” Note: Links have been removed,

“Inherited nanobionics” makes its debut

The integration of nanomaterials with living organisms is a hot topic, which is why this research on “inherited nanobionics” is on our list. Ardemis Boghossian at EPFL [École polytechnique fédérale de Lausanne] in Switzerland and colleagues have shown that certain bacteria will take up single-walled carbon nanotubes (SWCNTs). What is more, when the bacteria cells split, the SWCNTs are distributed amongst the daughter cells. The team also found that bacteria containing SWCNTs produce a significantly more electricity when illuminated with light than do bacteria without nanotubes. As a result, the technique could be used to grow living solar cells, which as well as generating clean energy, also have a negative carbon footprint when it comes to manufacturing.

Getting to back to Canada, I’m finding Saskatchewan featured more prominently here. They do a good job of promoting their science, especially the folks at the Canadian Light Source (CLS), Canada’s synchrotron, in Saskatoon. Canadian live science outreach events seeming to be coming back (slowly). Cautious organizers (who have a few dollars to spare) are also enthusiastic about hybrid events which combine online and live outreach.

After what seems like a long pause, I’m stumbling across more international news, e.g. “Nigeria and its nanotechnology research” published December 19, 2022 and “China and nanotechnology” published September 6, 2022. I think there’s also an Iran piece here somewhere.

With that …

Making resolutions in the dark

Hopefully this year I will catch up with the Council of Canadian Academies (CCA) output and finally review a few of their 2021 reports such as Leaps and Boundaries; a report on artificial intelligence applied to science inquiry and, perhaps, Powering Discovery; a report on research funding and Natural Sciences and Engineering Research Council of Canada.

Given what appears to a renewed campaign to have germline editing (gene editing which affects all of your descendants) approved in Canada, I might even reach back to a late 2020 CCA report, Research to Reality; somatic gene and engineered cell therapies. it’s not the same as germline editing but gene editing exists on a continuum.

For anyone who wants to see the CCA reports for themselves they can be found here (both in progress and completed).

I’m also going to be paying more attention to how public relations and special interests influence what science is covered and how it’s covered. In doing this 2022 roundup, I noticed that I featured an overview of fusion energy not long before the breakthrough. Indirect influence on this blog?

My post was precipitated by an article by Alex Pasternak in Fast Company. I’m wondering what precipitated Alex Pasternack’s interest in fusion energy since his self-description on the Huffington Post website states this “… focus on the intersections of science, technology, media, politics, and culture. My writing about those and other topics—transportation, design, media, architecture, environment, psychology, art, music … .”

He might simply have received a press release that stimulated his imagination and/or been approached by a communications specialist or publicists with an idea. There’s a reason for why there are so many public relations/media relations jobs and agencies.

Que sera, sera (Whatever will be, will be)

I can confidently predict that 2023 has some surprises in store. I can also confidently predict that the European Union’s big research projects (1B Euros each in funding for the Graphene Flagship and Human Brain Project over a ten year period) will sunset in 2023, ten years after they were first announced in 2013. Unless, the powers that be extend the funding past 2023.

I expect the Canadian quantum community to provide more fodder for me in the form of a 2023 report on Quantum Technologies from the Council of Canadian academies, if nothing else otherwise.

I’ve already featured these 2023 science events but just in case you missed them,

  • 2023 Preview: Bill Nye the Science Guy’s live show and Marvel Avengers S.T.A.T.I.O.N. (Scientific Training And Tactical Intelligence Operative Network) coming to Vancouver (Canada) November 24, 2022 posting
  • September 2023: Auckland, Aotearoa New Zealand set to welcome women in STEM (science, technology, engineering, and mathematics) November 15, 2022 posting

Getting back to this blog, it may not seem like a new year during the first few weeks of 2023 as I have quite the stockpile of draft posts. At this point I have drafts that are dated from June 2022 and expect to be burning through them so as not to fall further behind but will be interspersing them, occasionally, with more current posts.

Most importantly: a big thank you to everyone who drops by and reads (and sometimes even comments) on my posts!!! it’s very much appreciated and on that note: I wish you all the best for 2023.

Swiss researchers, memristors, perovskite crystals, and neuromorphic (brainlike) computing

A May 18, 2022 news item on Nanowerk highlights research into making memristors more ‘flexible’, (Note: There’s an almost identical May 18, 2022 news item on ScienceDaily but the issuing agency is listed as ETH Zurich rather than Empa as listed on Nanowerk),

Compared with computers, the human brain is incredibly energy-efficient. Scientists are therefore drawing on how the brain and its interconnected neurons function for inspiration in designing innovative computing technologies. They foresee that these brain-inspired computing systems, will be more energy-efficient than conventional ones, as well as better at performing machine-learning tasks.

Much like neurons, which are responsible for both data storage and data processing in the brain, scientists want to combine storage and processing in a single type of electronic component, known as a memristor. Their hope is that this will help to achieve greater efficiency because moving data between the processor and the storage, as conventional computers do, is the main reason for the high energy consumption in machine-learning applications.

Researchers at ETH Zurich, Empa and the University of Zurich have now developed an innovative concept for a memristor that can be used in a far wider range of applications than existing memristors.

“There are different operation modes for memristors, and it is advantageous to be able to use all these modes depending on an artificial neural network’s architecture,” explains ETH Zurich postdoc Rohit John. “But previous conventional memristors had to be configured for one of these modes in advance.”

The new memristors can now easily switch between two operation modes while in use: a mode in which the signal grows weaker over time and dies (volatile mode), and one in which the signal remains constant (non-volatile mode).

Once you get past the first two paragraphs in the Nanowerk news item, you find the ETH Zurich and Empa May 18, 2022 press releases by Fabio Begamin, in both cases, are identical (ETH is listed as the authoring agency on EurekAlert), (Note: A link has been removed in the following),

Just like in the brain

“These two operation modes are also found in the human brain,” John says. On the one hand, stimuli at the synapses are transmitted from neuron to neuron with biochemical neurotransmitters. These stimuli start out strong and then gradually become weaker. On the other hand, new synaptic connections to other neurons form in the brain while we learn. These connections are longer-​lasting.

John, who is a postdoc in the group headed by ETH Professor Maksym Kovalenko, was awarded an ETH fellowship for outstanding postdoctoral researchers in 2020. John conducted this research together with Yiğit Demirağ, a doctoral student in Professor Giacomo Indiveri’s group at the Institute for Neuroinformatics of the University of Zurich and ETH Zurich.

Semiconductor known from solar cells

The memristors the researchers have developed are made of halide perovskite nanocrystals, a semiconductor material known primarily from its use in photovoltaic cells. “The ‘nerve conduction’ in these new memristors is mediated by temporarily or permanently stringing together silver ions from an electrode to form a nanofilament penetrating the perovskite structure through which current can flow,” explains Kovalenko.

This process can be regulated to make the silver-​ion filament either thin, so that it gradually breaks back down into individual silver ions (volatile mode), or thick and permanent (non-​volatile mode). This is controlled by the intensity of the current conducted on the memristor: applying a weak current activates the volatile mode, while a strong current activates the non-​volatile mode.

New toolkit for neuroinformaticians

“To our knowledge, this is the first memristor that can be reliably switched between volatile and non-​volatile modes on demand,” Demirağ says. This means that in the future, computer chips can be manufactured with memristors that enable both modes. This is a significance advance because it is usually not possible to combine several different types of memristors on one chip.

Within the scope of the study, which they published in the journal Nature Communications, the researchers tested 25 of these new memristors and carried out 20,000 measurements with them. In this way, they were able to simulate a computational problem on a complex network. The problem involved classifying a number of different neuron spikes as one of four predefined patterns.

Before these memristors can be used in computer technology, they will need to undergo further optimisation.  However, such components are also important for research in neuroinformatics, as Indiveri points out: “These components come closer to real neurons than previous ones. As a result, they help researchers to better test hypotheses in neuroinformatics and hopefully gain a better understanding of the computing principles of real neuronal circuits in humans and animals.”

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

Reconfigurable halide perovskite nanocrystal memristors for neuromorphic computing by Rohit Abraham John, Yiğit Demirağ, Yevhen Shynkarenko, Yuliia Berezovska, Natacha Ohannessian, Melika Payvand, Peng Zeng, Maryna I. Bodnarchuk, Frank Krumeich, Gökhan Kara, Ivan Shorubalko, Manu V. Nair, Graham A. Cooke, Thomas Lippert, Giacomo Indiveri & Maksym V. Kovalenko. Nature Communications volume 13, Article number: 2074 (2022) DOI: https://doi.org/10.1038/s41467-022-29727-1 Published: 19 April 2022

This paper is open access.

Kempner Institute for the Study of Natural and Artificial Intelligence launched at Harvard University and University of Manchester pushes the boundaries of smart robotics and AI

Before getting to the two news items, it might be a good idea to note that ‘artificial intelligence (AI)’ and ‘robot’ are not synonyms although they are often used that way, even by people who should know better. (sigh … I do it too)

A robot may or may not be animated with artificial intelligence while artificial intelligence algorithms may be installed on a variety of devices such as a phone or a computer or a thermostat or a … .

It’s something to bear in mind when reading about the two new institutions being launched. Now, on to Harvard University.

Kempner Institute for the Study of Natural and Artificial Intelligence

A September 23, 2022 Chan Zuckerberg Initiative (CZI) news release (also on EurekAlert) announces a symposium to launch a new institute close to Mark Zuckerberg’s heart,

On Thursday [September 22, 2022], leadership from the Chan Zuckerberg Initiative (CZI) and Harvard University celebrated the launch of the Kempner Institute for the Study of Natural and Artificial Intelligence at Harvard University with a symposium on Harvard’s campus. Speakers included CZI Head of Science Stephen Quake, President of Harvard University Lawrence Bacow, Provost of Harvard University Alan Garber, and Kempner Institute co-directors Bernardo Sabatini and Sham Kakade. The event also included remarks and panels from industry leaders in science, technology, and artificial intelligence, including Bill Gates, Eric Schmidt, Andy Jassy, Daniel Huttenlocher, Sam Altman, Joelle Pineau, Sangeeta Bhatia, and Yann LeCun, among many others.

The Kempner Institute will seek to better understand the basis of intelligence in natural and artificial systems. Its bold premise is that the two fields are intimately interconnected; the next generation of AI will require the same principles that our brains use for fast, flexible natural reasoning, and understanding how our brains compute and reason requires theories developed for AI. The Kempner Institute will study AI systems, including artificial neural networks, to develop both principled theories [emphasis mine] and a practical understanding of how these systems operate and learn. It will also focus on research topics such as learning and memory, perception and sensation, brain function, and metaplasticity. The Institute will recruit and train future generations of researchers from undergraduates and graduate students to post-docs and faculty — actively recruiting from underrepresented groups at every stage of the pipeline — to study intelligence from biological, cognitive, engineering, and computational perspectives.

CZI Co-Founder and Co-CEO Mark Zuckerberg [chairman and chief executive officer of Meta/Facebook] said: “The Kempner Institute will be a one-of-a-kind institute for studying intelligence and hopefully one that helps us discover what intelligent systems really are, how they work, how they break and how to repair them. There’s a lot of exciting implications because once you understand how something is supposed to work and how to repair it once it breaks, you can apply that to the broader mission the Chan Zuckerberg Initiative has to empower scientists to help cure, prevent or manage all diseases.”

CZI Co-Founder and Co-CEO Priscilla Chan said: “Just attending this school meant the world to me. But to stand on this stage and to be able to give something back is truly a dream come true … All of this progress starts with building one fundamental thing: a Kempner community that’s diverse, multi-disciplinary and multi-generational, because incredible ideas can come from anyone. If you bring together people from all different disciplines to look at a problem and give them permission to articulate their perspective, you might start seeing insights or solutions in a whole different light. And those new perspectives lead to new insights and discoveries and generate new questions that can lead an entire field to blossom. So often, that momentum is what breaks the dam and tears down old orthodoxies, unleashing new floods of new ideas that allow us to progress together as a society.”

CZI Head of Science Stephen Quake said: “It’s an honor to partner with Harvard in building this extraordinary new resource for students and science. This is a once-in-a-generation moment for life sciences and medicine. We are living in such an extraordinary and exciting time for science. Many breakthrough discoveries are going to happen not only broadly but right here on this campus and at this institute.”

CZI’s 10-year vision is to advance research and develop technologies to observe, measure, and analyze any biological process within the human body — across spatial scales and in real time. CZI’s goal is to accelerate scientific progress by funding scientific research to advance entire fields; working closely with scientists and engineers at partner institutions like the Chan Zuckerberg Biohub and Chan Zuckerberg Institute for Advanced Biological Imaging to do the research that can’t be done in conventional environments; and building and democratizing next-generation software and hardware tools to drive biological insights and generate more accurate and biologically important sources of data.

President of Harvard University Lawrence Bacow said: “Here we are with this incredible opportunity that Priscilla Chan and Mark Zuckerberg have given us to imagine taking what we know about the brain, neuroscience and how to model intelligence and putting them together in ways that can inform both, and can truly advance our understanding of intelligence from multiple perspectives.”

Kempner Institute Co-Director and Gordon McKay Professor of Computer Science and of Statistics at the Harvard John A. Paulson School of Engineering and Applied Sciences Sham Kakade said: “Now we begin assembling a world-leading research and educational program at Harvard that collectively tries to understand the fundamental mechanisms of intelligence and seeks to apply these new technologies for the benefit of humanity … We hope to create a vibrant environment for all of us to engage in broader research questions … We want to train the next generation of leaders because those leaders will go on to do the next set of great things.”

Kempner Institute Co-Director and the Alice and Rodman W. Moorhead III Professor of Neurobiology at Harvard Medical School Bernardo Sabatini said: “We’re blending research, education and computation to nurture, raise up and enable any scientist who is interested in unraveling the mysteries of the brain. This field is a nascent and interdisciplinary one, so we’re going to have to teach neuroscience to computational biologists, who are going to have to teach machine learning to cognitive scientists and math to biologists. We’re going to do whatever is necessary to help each individual thrive and push the field forward … Success means we develop mathematical theories that explain how our brains compute and learn, and these theories should be specific enough to be testable and useful enough to start to explain diseases like schizophrenia, dyslexia or autism.”

About the Chan Zuckerberg Initiative

The Chan Zuckerberg Initiative was founded in 2015 to help solve some of society’s toughest challenges — from eradicating disease and improving education, to addressing the needs of our communities. Through collaboration, providing resources and building technology, our mission is to help build a more inclusive, just and healthy future for everyone. For more information, please visit chanzuckerberg.com.

Principled theories, eh. I don’t see a single mention of ethicists or anyone in the social sciences or the humanities or the arts. How are scientists and engineers who have no training in or education in or, even, an introduction to ethics or social impacts or psychology going to manage this?

Mark Zuckerberg’s approach to these issues was something along the lines of “it’s easier to ask for forgiveness than to ask for permission.” I understand there have been changes but it took far too long to recognize the damage let alone attempt to address it.

If you want to gain a little more insight into the Kempner Institute, there’s a December 7, 2021 article by Alvin Powell announcing the institute for the Harvard Gazette,

The institute will be funded by a $500 million gift from Priscilla Chan and Mark Zuckerberg, which was announced Tuesday [December 7, 2021] by the Chan Zuckerberg Initiative. The gift will support 10 new faculty appointments, significant new computing infrastructure, and resources to allow students to flow between labs in pursuit of ideas and knowledge. The institute’s name honors Zuckerberg’s mother, Karen Kempner Zuckerberg, and her parents — Zuckerberg’s grandparents — Sidney and Gertrude Kempner. Chan and Zuckerberg have given generously to Harvard in the past, supporting students, faculty, and researchers in a range of areas, including around public service, literacy, and cures.

“The Kempner Institute at Harvard represents a remarkable opportunity to bring together approaches and expertise in biological and cognitive science with machine learning, statistics, and computer science to make real progress in understanding how the human brain works to improve how we address disease, create new therapies, and advance our understanding of the human body and the world more broadly,” said President Larry Bacow.

Q&A

Bernardo Sabatini and Sham Kakade [Institute co-directors]

GAZETTE: Tell me about the new institute. What is its main reason for being?

SABATINI: The institute is designed to take from two fields and bring them together, hopefully to create something that’s essentially new, though it’s been tried in a couple of places. Imagine that you have over here cognitive scientists and neurobiologists who study the human brain, including the basic biological mechanisms of intelligence and decision-making. And then over there, you have people from computer science, from mathematics and statistics, who study artificial intelligence systems. Those groups don’t talk to each other very much.

We want to recruit from both populations to fill in the middle and to create a new population, through education, through graduate programs, through funding programs — to grow from academic infancy — those equally versed in neuroscience and in AI systems, who can be leaders for the next generation.

Over the millions of years that vertebrates have been evolving, the human brain has developed specializations that are fundamental for learning and intelligence. We need to know what those are to understand their benefits and to ask whether they can make AI systems better. At the same time, as people who study AI and machine learning (ML) develop mathematical theories as to how those systems work and can say that a network of the following structure with the following properties learns by calculating the following function, then we can take those theories and ask, “Is that actually how the human brain works?”

KAKADE: There’s a question of why now? In the technological space, the advancements are remarkable even to me, as a researcher who knows how these things are being made. I think there’s a long way to go, but many of us feel that this is the right time to study intelligence more broadly. You might also ask: Why is this mission unique and why is this institute different from what’s being done in academia and in industry? Academia is good at putting out ideas. Industry is good at turning ideas into reality. We’re in a bit of a sweet spot. We have the scale to study approaches at a very different level: It’s not going to be just individual labs pursuing their own ideas. We may not be as big as the biggest companies, but we can work on the types of problems that they work on, such as having the compute resources to work on large language models. Industry has exciting research, but the spectrum of ideas produced is very different, because they have different objectives.

For the die-hards, there’s a September 23, 2022 article by Clea Simon in Harvard Gazette, which updates the 2021 story,

Next, Manchester, England.

Manchester Centre for Robotics and AI

Robotots take a break at a lab at The University of Manchester – picture courtesy of Marketing Manchester [downloaded from https://www.manchester.ac.uk/discover/news/manchester-ai-summit-aims-to-attract-experts-in-advanced-engineering-and-robotics/]

A November 22, 2022 University of Manchester press release (also on EurekAlert) announces both a meeting and a new centre, Note: Links to the Centre have been retained; all others have been removed,

How humans and super smart robots will live and work together in the future will be among the key issues being scrutinised by experts at a new centre of excellence for AI and autonomous machines based at The University of Manchester.

The Manchester Centre for Robotics and AI will be a new specialist multi-disciplinary centre to explore developments in smart robotics through the lens of artificial intelligence (AI) and autonomous machinery.

The University of Manchester has built a modern reputation of excellence in AI and robotics, partly based on the legacy of pioneering thought leadership begun in this field in Manchester by legendary codebreaker Alan Turing.

Manchester’s new multi-disciplinary centre is home to world-leading research from across the academic disciplines – and this group will hold its first conference on Wednesday, Nov 23, at the University’s new engineering and materials facilities.

A  highlight will be a joint talk by robotics expert Dr Andy Weightman and theologian Dr Scott Midson which is expected to put a spotlight on ‘posthumanism’, a future world where humans won’t be the only highly intelligent decision-makers.

Dr Weightman, who researches home-based rehabilitation robotics for people with neurological impairment, and Dr Midson, who researches theological and philosophical critiques of posthumanism, will discuss how interdisciplinary research can help with the special challenges of rehabilitation robotics – and, ultimately, what it means to be human “in the face of the promises and challenges of human enhancement through robotic and autonomous machines”.

Other topics that the centre will have a focus on will include applications of robotics in extreme environments.

For the past decade, a specialist Manchester team led by Professor Barry Lennox has designed robots to work safely in nuclear decommissioning sites in the UK. A ground-breaking robot called Lyra that has been developed by Professor Lennox’s team – and recently deployed at the Dounreay site in Scotland, the “world’s deepest nuclear clean up site” – has been listed in Time Magazine’s Top 200 innovations of 2022.

Angelo Cangelosi, Professor of Machine Learning and Robotics at Manchester, said the University offers a world-leading position in the field of autonomous systems – a technology that will be an integral part of our future world. 

Professor Cangelosi, co-Director of Manchester’s Centre for Robotics and AI, said: “We are delighted to host our inaugural conference which will provide a special showcase for our diverse academic expertise to design robotics for a variety of real world applications.

“Our research and innovation team are at the interface between robotics, autonomy and AI – and their knowledge is drawn from across the University’s disciplines, including biological and medical sciences – as well the humanities and even theology. [emphases mine]

“This rich diversity offers Manchester a distinctive approach to designing robots and autonomous systems for real world applications, especially when combined with our novel use of AI-based knowledge.”

Delegates will have a chance to observe a series of robots and autonomous machines being demoed at the new conference.

The University of Manchester’s Centre for Robotics and AI will aim to: 

  • design control systems with a focus on bio-inspired solutions to mechatronics, eg the use of biomimetic sensors, actuators and robot platforms; 
  • develop new software engineering and AI methodologies for verification in autonomous systems, with the aim to design trustworthy autonomous systems; 
  • research human-robot interaction, with a pioneering focus on the use of brain-inspired approaches [emphasis mine] to robot control, learning and interaction; and 
  • research the ethics and human-centred robotics issues, for the understanding of the impact of the use of robots and autonomous systems with individuals and society. 

In some ways, the Kempner Institute and the Manchester Centre for Robotics and AI have very similar interests, especially where the brain is concerned. What fascinates me is the Manchester Centre’s inclusion of theologian Dr Scott Midson and the discussion (at the meeting) of ‘posthumanism’. The difference is between actual engagement at the symposium (the centre) and mere mention in a news release (the institute).

I wish the best for both institutions.