Tag Archives: neuromorphic computing

As researchers build high-yield memristor chips, brain-inspired AI moves closer

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This November 7, 2025 news item on Nanowerk highlights research into memristors from Daegu Gyeongbuk Institute of Science and Technology (DGIST), Note: Links have been removed,

A research team at DGIST in South Korea has taken a major step toward building chips that work more like the human brain. Led by Professor Sanghyeon Choi from the Department of Electrical Engineering and Computer Science, the group has developed a highly integrated memristor device at full wafer scale, a milestone for next-generation AI hardware (Nature Communications, “Wafer-scale fabrication of memristive passive crossbar circuits for brain-scale neuromorphic computing”).

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A November 28, 2025 Daegu Gyeongbuk Institute of Science and Technology (DGIST) press release (also on EurekAlert but published November 4, 2025), which originated the news item, announced its ‘wafer-scale memristor’ and its possible impact on neuromorphic (brain-like) computing,

A research team led by Professor Sanghyeon Choi from the Department of Electrical Engineering and Computer Science at DGIST (President Kunwoo Lee) successfully developed the “memristor,” which is gaining recognition as a next-generation semiconductor device, through mass-integration at the wafer scale. This study proposes a new technological platform for implementing a highly integrated AI semiconductor replicating the human brain, overcoming the limitations of conventional semiconductors.

The human brain contains about 100 billion neurons and around 100 trillion synapses, allowing it to store and process enormous amounts of information within a compact space. Next-generation AI research aims to develop “brain-like AI chips” that replicate this structure. Yet, current AI semiconductors remain far less efficient than the human brain, largely because of their intricate circuitry and substantial power requirements.

The memristor is an emerging alternative option that can overcome these limitations. As a semiconductor device capable of remembering the amount of current flowed, it simultaneously executes memory and computation tasks. Owing to its simple architecture, the circuit can be configured with a much higher density than typical semiconductors. Specifically, an arrangement in a crossbar format enables dozens of times more information to be stored in the same area, compared to SRAM.

However, memristor integration technology has so far been limited to small-scale experimental demonstrations. The main reasons include process complexity, low yield (product completion rate), voltage loss, and current leakage, all of which have hindered its expansion to large-scale wafer production.

Thus, Professor Choi and his team carried out joint research with Dr. Dmitri Strukov’s group at UC Santa Barbara [University of California at Santa Barbara] and introduced a new approach of “co-designing material, component, circuit, and algorithm.” This method enabled the implementation of a memristor crossbar circuit that achieved an approximately 95% yield on a 4-inch wafer without requiring a complex fabrication process.

Furthermore, the research team successfully demonstrated a 3D vertical stacking structure. This signifies the possibility of a memristor-based circuit being expanded into a large-scale AI computation system in the future.

In addition, when a spiking neural network was applied based on the proposed technology, notable efficiency and stable execution were confirmed in actual AI computation.

Professor Choi stated, “This study proposed a method for improving memristor integration technology, which had been limited in the past” and added, “We are expecting it to lead to the development of a next-generation semiconductor platform in the future.”

This study was supported by the U.S. National Science Foundation, Industrial Innovation Talent Growth Support Program of the Korea Institute for Advancement of Technology, and Engineering Academic Research Support Program of the National Research Foundation of Korea’s Science. The research, led by Professor Choi of DGIST as both the first and corresponding author, with Professor Dmitri Strukov of UC Santa Barbara as a co-author, was published in October in the prestigious multidisciplinary journal “Nature Communications.”

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

Wafer-scale fabrication of memristive passive crossbar circuits for brain-scale neuromorphic computing by Sanghyeon Choi, Sai Sukruth Bezugam, Tinish Bhattacharya, Dongseok Kwon & Dmitri B. Strukov. Nature Communications volume 16, Article number: 8757 (2025) Published: 01 October 2025 Version of record: 01 October 2025 DOI: https://doi.org/10.1038/s41467-025-63831-2

This paper is open access.

Artificial neurons replicate real brain processes with ion-based diffusive memristors

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A November 5, 2025 news item on ScienceDaily announced memristors that more closely resemble biological neurons,

USC [University of Southern California] researchers built artificial neurons that replicate real brain processes using ion-based diffusive memristors. These devices emulate how neurons use chemicals to transmit and process signals, offering massive energy and size advantages. The technology may enable brain-like, hardware-based learning systems. It could transform AI into something closer to natural intelligence.

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An October 28, 2026 University of Southern California (USC) news release (also on Eurekalert but published October 29, 2025, which originated the news item, provides more detail about this engineering feat,

Researchers at the USC Viterbi School of Engineering and School of Advanced Computing have developed artificial neurons that replicate the complex electrochemical behavior of biological brain cells. The innovation, documented in Nature Electronics, is a leap forward in neuromorphic computing technology. The innovation will allow for reduction in the chip size by orders of magnitude, reduce its energy consumption by orders of magnitude and could advance artificial general intelligence.

Unlike conventional digital processors or existing neuromorphic chips based on silicon technology that merely simulate neural activity, these artificial neurons physically embody or emulate the analog dynamics of their biological counterparts. Just as neurochemicals initiate brain activity, chemicals can be used to initiate computation in neuromorphic, or-brain-inspired hardware devices. By being a physical replication of the biological process, they differ from prior iterations of artificial neurons that were solely mathematical equations.

The work, led by USC Computer and Electrical Engineering Professor Joshua Yang, who also led the work in a seminal paper on artificial synapses over a decade ago, introduces a new type of artificial neuron based on the so-called “diffusive memristor”. The Nature Electronics paper explores how such artificial neurons can enable a new class of chips that complement and augment today’s silicon-based technologies, which power nearly all modern electronics and rely on the movement of electrons for computation. Instead, the diffusive device introduced by Yang and colleagues to build the neurons would rely on movement of atoms. Such neurons can enable newer chips that would operate more similarly to how our brain works, would be more energy efficient and could lend themselves to usher in what’s known as artificial general intelligence (AGI).

How it works:
In the biological process, the brain uses both electrical and chemical signals to drive action in the body. Neurons or nerve cells start out with electrical signals that when they reach the space or gap at the end of the neuron called the synapse, the electrical signals are converted into chemical signals into order to pass on and process the information. Once the information crosses to the next neuron, some of those signals are once again converted to electrical signals through the body of the neuron. This is the physical process that Yang and colleagues have succeeded in emulating with high fidelity in several critical aspects. The big advantage: their diffusive memristor-based artificial neuron requires only the space of a single transistor, rather than the tens to hundreds used in conventional designs.

In particular, in the biological model, ions or charged particles help generate the electrical signals to cause action within the neuron. In the human brain, such processes rely on chemicals (e.g., ions) like potassium, sodium, or calcium to force this action.

In the current paper, Yang, who is Director of the Center of Excellence on Neuromorphic Computing at USC, uses silver ions in oxide to generate the electrical pulse and emulate the processes to perform computing for activities such as movement, learning, and planning.

“Even though it’s not exactly the same ions in our artificial synapses and neurons, the physics governing the ion motion and the dynamics are very similar,” says Yang.

Yang explains, “Silver is easy to diffuse and gives us the dynamics we need to emulate the biosystem so that we can achieve the function of the neurons, with a very simple structure.”  The new device that can enable a brain-like chip is called the “diffusive memristor” because of the ion motion and the dynamic diffusion that occurs with the use of silver.

He adds, the team chose to utilize ion dynamics for building artificial intelligent systems “because that is what happens in the human brain, for a good reason and since the human brain, is the ‘winner in evolution–the most efficient intelligent engine.”

“It’s more efficient,” says Yang.

This is critical, explains Yang, “It’s not that our chips or computers are not powerful enough for whatever they are doing. It’s that they aren’t efficient enough. They use too much energy.” This is particularly relevant given the level of energy needed to run large software models with a huge amount of data like machine learning for artificial intelligence.

Yang goes on to explain that unlike the brain, “Our existing computing systems were never intended to process massive amounts of data or to learn from just a few examples on their own. One way to boost both energy and learning efficiency is to build artificial systems that operate according to principles observed in the brain.”

If you are looking for pure speed, electrons that run modern computing would be the best for fast operations. But, he explains, “Ions are a better medium than electrons for embodying principles of the brain. Because electrons are lightweight and volatile, computing with them enables software-based learning rather than hardware-based learning, which is fundamentally different from how the brain operates.”

In contrast, he says, “The brain learns by moving ions across membranes, achieving energy-efficient and adaptive learning directly in hardware, or more precisely, in what people may call ‘wetware’.”

For example, a young child can learn to recognize handwritten digits after seeing only a few examples of each, whereas a computer typically needs thousands to achieve the same task. Yet, the human brain accomplishes this remarkable learning while consuming only about 20 watts of power, compared to the megawatts required by today’s supercomputers.

Potential Impact:
This new method is one step closer to mimicking natural intelligence.

Yang noted that silver used in the experiment is not readily compatible with conventional semiconductor manufacturing, and that alternative ionic species will need to be investigated for similar functionalities.

The efficiency of these diffusive memristors include not only the energy, but size. Normally one smart phone has about 10 chips but billions of transistors or switches that control the on/off or 0’s and 1’s that underpin computation.

“Instead [with this innovation], we just use a footprint of one transistor for each neuron. We are designing the building blocks that eventually led us to reduce the chip size by orders of magnitude, reduce the energy consumption by orders of magnitude, so it can be sustainable to perform AI in the future, with similar level of intelligence without burning energy that we cannot sustain,” says Yang.

Now that we have demonstrated capable and compact building blocks, artificial synapses and neurons, the next step is to integrate large numbers of them and test how closely we can replicate the brain’s efficiency and capabilities. “Even more exciting,” says Yang, “is the prospect that such brain-faithful systems could help us uncover new insights into how the brain itself works.”

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

A spiking artificial neuron based on one diffusive memristor, one transistor and one resistor by Ruoyu Zhao, Tong Wang, Taehwan Moon, Yichun Xu, Jian Zhao, Piyush Sud, Seung Ju Kim, Han-Ting Liao, Ye Zhuo, Rivu Midya, Shiva Asapu, Dawei Gao, Zixuan Rong, Qinru Qiu, Cynthia Bowers, Krishnamurthy Mahalingam, S. Ganguli, A. K. Roy, Qing Wu, Jin-Woo Han, R. Stanley Williams, Yong Chen & J. Joshua Yang. Nature Electronics volume 8, pages 1211–1221 (2025) Published: 27 October 2025 Version of record: 27 October 2025 Issue date: December 2025 DOI: https://doi.org/10.1038/s41928-025-01488-x

This paper is behind a paywall.

Mushroom computer chips act as fungal memristors for brain-like computing?

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Caption: Fungal memristors could be ideal interfaces for high-frequency bioelectronics, researchers say. Photo provided by John LaRocco.

An October 26, 2025 news item (rewritten slightly) on ScienceDaily announces research into ‘fungal computers’ from Ohio State University (OSU),

Fungal networks could one day replace the tiny metal components that process and store computer data, according to new research.

Mushrooms are known for their toughness and unusual biological properties, qualities that make them attractive for bioelectronics. This emerging field blends biology and technology to design innovative, sustainable materials for future computing systems.

Turning Mushrooms Into Living Memory Devices

Researchers at The Ohio State University recently discovered that edible fungi, such as shiitake mushrooms, can be cultivated and guided to function as organic memristors. These components act like memory cells that retain information about previous electrical states.

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An October 24, 2025 Ohio State University (OSU) news release (also on EurekAlert) by Tatyana Woodall, which originated the news item (Note: ScienceDaily has made mostly minor editorial changes), provides further context and detail, Note: Links have been removed,

“Being able to develop microchips that mimic actual neural activity means you don’t need a lot of power for standby or when the machine isn’t being used,” said John LaRocco, lead author of the study and a research scientist in psychiatry at Ohio State’s College of Medicine. “That’s something that can be a huge potential computational and economic advantage.”

Fungal electronics aren’t a new concept, but they have become ideal candidates for developing sustainable computing systems, said LaRocco. This is because they minimize electrical waste by being biodegradable and cheaper to fabricate than conventional memristors and semiconductors, which often require costly rare-earth minerals and high amounts of energy from data centers. 

“Mycelium as a computing substrate has been explored before in less intuitive setups, but our work tries to push one of these memristive systems to its limits,” he said. 

The study was recently published in the journal PLOS One.

To explore the new memristors’ capabilities, researchers cultured samples of shiitake and button mushrooms. Once mature, they were dehydrated to ensure long-term viability, connected to special electronic circuits, and then electrocuted at various voltages and frequencies. 

“We would connect electrical wires and probes at different points on the mushrooms because distinct parts of it have different electrical properties,” said LaRocco. “Depending on the voltage and connectivity, we were seeing different performances.”

After two months, the team discovered that when used as RAM – the computer memory that stores data – their mushroom memristor was able to switch between electrical states at up to 5,850 signals per second, with about 90% accuracy. However, performance dropped as the frequency of the electrical voltages increased, but much like an actual brain, it could be fixed by connecting more mushrooms to the circuit.  

Overall, their research details how surprisingly easy it is to program and preserve mushrooms to behave in unexpected and useful ways, said Qudsia Tahmina, co-author of the study and an associate professor in electrical and computer engineering at Ohio State. Moreover, it’s an example of how technology can advance when it relies on the natural world. 

“Society has become increasingly aware of the need to protect our environment and ensure that we preserve it for future generations,” said Tahmina.“So that could be one of the driving factors behind new bio-friendly ideas like these.”

Building on the flexibility mushrooms offer also suggests there are possibilities for scaling up fungal computing, said Tahmina. For instance, larger mushroom systems may be useful in edge computing and aerospace exploration; smaller ones in enhancing the performance of autonomous systems and wearable devices. 

Organic memristors are still in early development, but future work could optimize the production process by improving cultivation techniques and miniaturizing the devices, as viable fungal memristors would need to be far smaller than what researchers achieved in this work. 

“Everything you’d need to start exploring fungi and computing could be as small as a compost heap and some homemade electronics, or as big as a culturing factory with pre-made templates,” said LaRocco. “All of them are viable with the resources we have in front of us now.” 

Other Ohio State co-authors include Ruben Petreaca, John Simonis and Justin Hill. This study was supported by the Honda Research Institute.

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

Sustainable memristors from shiitake mycelium for high-frequency bioelectronics by John LaRocco, Qudsia Tahmina, Ruben Petreaca, John Simonis, Justin Hill. PLOS [Public Library of Science] One Published: October 10, 2025 DOI: https://doi.org/10.1371/journal.pone.0328965

This paper is open access.

Ultrafast neuromorphic (brainlike) computing at room temperature made possible by utilizing polariton nonlinearities

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A June 6, 2025 news item on Nanowerk describes research into the development of ultrafast neuromorphic (brainlike) computing, Note: A link has been removed,

Neuromorphic computing, inspired by the human brain, is considered as the next-generation paradigm for artificial intelligence (AI), offering dramatically increased speed and lower energy consumption. While software-based artificial neural networks (ANNs) have made remarkable strides, unlocking their full potential calls for physical platforms that combine ultrafast operation, high computational density, energy efficiency, and scalability.

Among various physical systems, microcavity exciton polaritons have attracted attention for neuromorphic computing due to their ultrafast dynamics, strong nonlinearities, and light-based architecture, which naturally align with the requirements of brain-inspired computation. However, their practical use has been hampered by the need for cryogenic operation and intricate fabrication processes.

In a new paper published in eLight (“Ultrafast neuromorphic computing driven by polariton nonlinearities”), a team of scientists led by Professor Qihua Xiong from Tsinghua University and Beijing Academy of Quantum Information Sciences report a demonstration of neuromorphic computing utilizing perovskite microcavity exciton polaritons operating at room temperature. Their novel system achieves high-speed digit recognition with 92% accuracy using only single-step training and opens new opportunities for scalable, light-driven neural hardware.

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A June 4, 2025 Light Publishing Center, Changchun Institute of Optics, Fine Mechanics And Physics, CAS (Chinese Academy of Sciences) press release on EurekAlert, which originated the news item, provides more technical details,

The core of their system is a planar FAPbBr3 perovskite microcavity which supports exciton-polariton condensation under non-resonant optical pumping. Input images from the MNIST dataset are optically encoded by a spatial light modulator (SLM) and projected onto the microcavity as spatially structured excitation beams. The resulting polariton emission patterns serve as the output of the ANN, which is then linearly processed using ridge regression. Remarkably, this scheme requires no predefined network structure—only the physical response of the polariton system—and achieves competitive accuracy using a lightweight training set of 900 images.

“Unlike conventional approaches that rely on prefabricated structures or predefined network nodes, our method employs a fully connected spatial mapping, utilizing the entire perovskite sample area without additional structural constraints,” the corresponding author Qihua Xiong replied. This not only improves the system’s scalability but also simplifies experimental realization.

What makes this system stand out is the intrinsic nonlinear and dynamical response of the polaritons. The researchers show that below the condensation threshold, the system behaves nearly linearly, while near and above threshold, nonlinearities emerge sharply, enhancing pattern discrimination. Moreover, by applying ultrafast Kerr-gated time-resolved photoluminescence, the team probes the temporal evolution of polariton responses. They find that polariton dynamics unfold on the picosecond scale and exhibit time-dependent nonlinear mappings, which significantly broaden the system’s capacity for processing complex and temporally varying inputs.

The researchers conclude that “perovskite microcavity exciton polaritons offer ultrafast processing speeds on the picosecond timescale and exhibit exceptionally strong nonlinear interactions, significantly surpassing those in traditional photonic systems.” These attributes make them powerful candidates for future physical neural networks capable of real-time, energy-efficient AI.

This work highlights the growing role of halide perovskites in next-generation photonic computing and marks an important step toward developing all-optical neuromorphic hardware—free from the energy and speed limitations of traditional electronics.

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

Ultrafast neuromorphic computing driven by polariton nonlinearities by Yusong Gan, Ying Shi, Sanjib Ghosh, Haiyun Liu, Huawen Xu & Qihua Xiong. eLight volume 5, Article number: 9 (2025) DOI: https://doi.org/10.1186/s43593-025-00087-9 Published: 02 June 2025

This paper is open access.

A Multidisciplinary Centre for Neuromorphic (brainlike) Computing in the UK

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A May 6, 2025 Aston University press release (also on EurekAlert but published May 7, 2025) announces a UK ‘neuromorphic initiative’, Note: Links have been removed,

  • Aston University to lead the UK’s new centre to pioneer brain-inspired, energy-efficient computing technologies 
  • The initiative will receive £5.6 million over four years from the Engineering and Physical Sciences Research Council [EPSRC]
  • The aim of the centre is to become a focal point for networking and collaboration on fundamental research and technology.

The UK will be getting a new centre to pioneer brain-inspired, energy-efficient computing technologies.

The UK Multidisciplinary Centre for Neuromorphic Computing is led by Aston University and will receive £5.6 million over four years from the UKRI [UK Research and Innovation] Engineering and Physical Sciences Research Council (EPSRC).

The aim of the centre is to become a focal point for networking and collaboration on fundamental research and technology of neuromorphic computing to address the sustainability challenges facing today’s digital infrastructure and artificial intelligence systems.

The centre will be led by the Aston Institute of Photonic Technologies (AIPT) and will include the world-leading researchers from Aston University, the University of Oxford, the University of Cambridge, the University of Southampton, Queen Mary University of London, Loughborough University and the University of Strathclyde. 

Neuromorphic computing seeks to replicate the brain’s structural and functional principles, however scientists currently lack a deep, system-level understanding of how the human brain computes at cellular and network scales. The researchers aim to tackle that challenge directly, blending stem-cell-derived human neuron experiments with advanced computational models, low-power algorithms and novel photonic hardware.

The centre team includes world-leading researchers with broad and complementary expertise in neuroscience, non-conventional computing algorithms, photonics, opto- and nano-electronics and materials science. In collaboration with policymakers and industrial partners the scientists and engineers aim to demonstrate the capabilities of neuromorphic computing across a range of sectors and applications. The centre will be supported by a broad network of industry partners including Microsoft Research, Thales, BT, QinetiQ, Nokia Bell Labs, Hewlett Packard Labs, Leonardo, Northrop Grumman and a number of small to medium enterprises. Their contribution will focus on enhancing the centre’s impact on society.

Professor Rhein Parri, co-director and neurophysiologist at Aston University said: “For the first time, we can combine the study of living human neurons with that of advanced computing platforms to co-develop the future of computing. 

“This project is an exciting leap forward, learning from biology and technology in ways that were not previously possible.”

The experts aim to co-design brain-inspired neuromorphic systems by studying human neuronal function using the latest human induced pluripotent stem cell – or hiPSC technologies – and developing new computational paradigms and low-power AI algorithms. They also plan to create devices and hardware that are inspired by biological systems, like the human brain. These devices will use light – or photonic hardware – to process information. This approach will be the next big step in making computing more energy-efficient and capable of handling many tasks at the same time. They also aim to create a sustainable UK research ecosystem through training, road mapping, and international collaboration.

Professor Sergei K. Turitsyn, director of the centre and AIPT, said: “The project’s ambition is not only to develop future technologies, but also to create a new internationally known UK research brand in neuromorphic computing that will unite the UK’s best minds across disciplines and will lead to sustainable operation and a long-term impact. It’s a proud moment for AIPT and Aston University to lead this national effort.”

Professor Natalia Berloff, co-director of the centre who is based at the University of Cambridge said: “One of the most exciting aspects of neuromorphic computing is the potential of photonic hardware to deliver truly brain-like efficiency. 

“Light-based processors can exploit massive parallelism and ultrafast signal propagation to outperform conventional electronics on demanding AI workloads, while consuming far less power. By combining these photonic architectures with insights from living human neurons, we aim to co-design neuromorphic systems that move beyond incremental improvements and toward a genuinely transformative computing paradigm.”

In addition, the researchers aim to tackle the increasing global energy footprint of information and communication technologies which is developing at an unsustainable pace, driven partly by the explosive growth of artificial intelligence. Today’s AI systems are built on traditional computing hardware with increasingly high-power consumption (kW), posing a barrier to scalability and sustainability. In contrast, the human brain performs complex computation and communication tasks using just 20 watts.

Professor Dimitra Georgiadou, co-director of the centre who is based at the University of Southampton added: “To address the challenge of substantially lowering the power consumption in electronics, novel materials and device architectures are needed that can effectively emulate computation in the brain and cellular responses to certain stimuli.”

The centre’s ambition goes beyond technology development as it aims to serve as a foundation for a long-term, interdisciplinary research ecosystem – actively expanding its membership and reach over time. It aims to establish a sustainable centre that continues to be a focal point for the community and will thrive beyond the initial funding period, reinforcing innovation, partnership, and impact in the field of neuromorphic computing.

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Good luck to this effort to lower power consumption.

Neuromorphic wires (inspired by nerve cells) amplify their own signals—no amplifiers needed

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Katherine Bourzac’s September 16, 2024 article for the IEEE (Institute for Electrical and Electronics Engineers) Spectrum magazine provides an accessible (relatively speaking) description of a possible breakthrough for neuromorphic computing, Note: Links have been removed,

In electrical engineering, “we just take it for granted that the signal decays” as it travels, says Timothy Brown, a postdoc in materials physics at Sandia National Lab who was part of the group of researchers who made the self-amplifying device. Even the best wires and chip interconnects put up resistance to the flow of electrons, degrading signal quality over even relatively small distances. This constrains chip designs—lossy interconnects are broken up into ever smaller lengths, and signals are bolstered by buffers and drivers. A 1-square-centimeter chip has about 10,000 repeaters to drive signals, estimates R. Stanley Williams, a professor of computer engineering at Texas A&M University.

Williams is one of the pioneers of neuromorphic computing, which takes inspiration from the nervous system. Axons, the electrical cables that carry signals from the body of a nerve cell to synapses where they connect with projections from other cells, are made up of electrically resistant materials. Yet they can carry high fidelity signals over long distances. The longest axons in the human body are about 1 meter, running from the base of the spine to the feet. Blue whales are thought to have 30 m long axons stretching to the tips of their tails. If something bites the whale’s tail, it will react rapidly. Even from 30 meters away, “the pulses arrive perfectly,” says Williams. “That’s something that doesn’t exist in electrical engineering.”

That’s because axons are active transmission lines: they provide gain to the signal along their length. Williams says he started pondering how to mimic this in an inorganic system 12 years ago. A grant from the US Department of Energy enabled him to build a team with the necessary resources to make it happen. The team included Williams, Brown, and Suhas Kumar, a materials physicist at Sandia.

Axons are coated with an insulating layer called the myelin sheath. Where there are gaps in the sheath, negatively charged sodium ions and positively charged potassium ions can move in and out of the axon, changing the voltage across the cell membrane and pumping in energy in the process. Some of that energy gets taken up by the electrical signal, amplifying it.

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Williams and his team wanted to mimic this in a simple structure. They didn’t try to mimic all the physical structures in axons—instead, they sought guidance in a mathematical description of how they amplify signals. Axons operate in a mode called the “edge of chaos,” which combines stable and unstable qualities. This may seem inherently contradictory. Brown likens this kind of system to a saddle that’s curved with two dips. The saddle curves up towards the front and the back, keeping you stable as you rock back and forth. But if you get jostled from side to side, you’re more likely to fall off. When you’re riding in the saddle, you’re operating at the edge of chaos, in a semistable state. In the abstract space of electrical engineering, that jostling is equivalent to wiggles in current and voltage.

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There’s a long way to go from this first experimental demonstration to a reimagining of computer chip interconnects. The team is providing samples for other researchers [emphasis mine] who want to verify their measurements. And they’re trying other materials to see how well they do—LaCoO3 [lanthanum colbalt oxide] is only the first one they’ve tested.

Williams hopes this research will show electrical engineers new ideas about how to move forward. “The dream is to redesign chips,” he says. Electrical engineers have long known about nonlinear dynamics, but have hardly ever taken advantage of them, Williams says. “This requires thinking about things and doing measurements differently than they have been done for 50 years,” he says.

If you have the time, please read Bourzac’s September 16, 2024 article in its entirety. For those who want the technical nitty gritty, here’s a link to and a citation for the paper,

Axon-like active signal transmission by Timothy D. Brown, Alan Zhang, Frederick U. Nitta, Elliot D. Grant, Jenny L. Chong, Jacklyn Zhu, Sritharini Radhakrishnan, Mahnaz Islam, Elliot J. Fuller, A. Alec Talin, Patrick J. Shamberger, Eric Pop, R. Stanley Williams & Suhas Kumar. Nature volume 633, pages 804–810 (2024) DOI: https://doi.org/10.1038/s41586-024-07921 Published online: 11 September 2024 Issue Date: 26 September 2024

This paper is open access.

Huge leap forward in computing efficiency with Indian Institute of Science’s (IISc) neuromorphic (brainlike) platform

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This is pretty thrilling news in a September 11, 2024 Indian Institute of Science (IISc) press release (also on EurekAlert), Note: A link has been removed,

In a landmark advancement, researchers at the Indian Institute of Science (IISc) have developed a brain-inspired analog computing platform capable of storing and processing data in an astonishing 16,500 conductance states within a molecular film. Published today in the journal Nature, this breakthrough represents a huge step forward over traditional digital computers in which data storage and processing are limited to just two states. 

Such a platform could potentially bring complex AI tasks, like training Large Language Models (LLMs), to personal devices like laptops and smartphones, thus taking us closer to democratising the development of AI tools. These developments are currently restricted to resource-heavy data centres, due to a lack of energy-efficient hardware. With silicon electronics nearing saturation, designing brain-inspired accelerators that can work alongside silicon chips to deliver faster, more efficient AI is also becoming crucial.

“Neuromorphic computing has had its fair share of unsolved challenges for over a decade,” explains Sreetosh Goswami, Assistant Professor at the Centre for Nano Science and Engineering (CeNSE), IISc, who led the research team. “With this discovery, we have almost nailed the perfect system – a rare feat.”

The fundamental operation underlying most AI algorithms is quite basic – matrix multiplication, a concept taught in high school maths. But in digital computers, these calculations hog a lot of energy. The platform developed by the IISc team drastically cuts down both the time and energy involved, making these calculations a lot faster and easier.

The molecular system at the heart of the platform was designed by Sreebrata Goswami, Visiting Professor at CeNSE. As molecules and ions wiggle and move within a material film, they create countless unique memory states, many of which have been inaccessible so far. Most digital devices are only able to access two states (high and low conductance), without being able to tap into the infinite number of intermediate states possible.

By using precisely timed voltage pulses, the IISc team found a way to effectively trace a much larger number of molecular movements, and map each of these to a distinct electrical signal, forming an extensive “molecular diary” of different states. “This project brought together the precision of electrical engineering with the creativity of chemistry, letting us control molecular kinetics very precisely inside an electronic circuit powered by nanosecond voltage pulses,” explains Sreebrata Goswami.

Tapping into these tiny molecular changes allowed the team to create a highly precise and efficient neuromorphic accelerator, which can store and process data within the same location, similar to the human brain. Such accelerators can be seamlessly integrated with silicon circuits to boost their performance and energy efficiency. 

A key challenge that the team faced was characterising the various conductance states, which proved impossible using existing equipment. The team designed a custom circuit board that could measure voltages as tiny as a millionth of a volt, to pinpoint these individual states with unprecedented accuracy.

The team also turned this scientific discovery into a technological feat. They were able to recreate NASA’s iconic “Pillars of Creation” image from the James Webb Space Telescope data – originally created by a supercomputer – using just a tabletop computer. They were also able to do this at a fraction of the time and energy that traditional computers would need.

The team includes several students and research fellows at IISc. Deepak Sharma performed the circuit and system design and electrical characterisation, Santi Prasad Rath handled synthesis and fabrication, Bidyabhusan Kundu tackled the mathematical modelling, and Harivignesh S crafted bio-inspired neuronal response behaviour. The team also collaborated with Stanley Williams [also known as R. Stanley Williams], Professor at Texas A&M University and Damien Thompson, Professor at the University of Limerick. 

The researchers believe that this breakthrough could be one of India’s biggest leaps in AI hardware, putting the country on the map of global technology innovation. Navakanta Bhat, Professor at CeNSE and an expert in silicon electronics led the circuit and system design in this project. “What stands out is how we have transformed complex physics and chemistry understanding into groundbreaking technology for AI hardware,” he explains. “In the context of the India Semiconductor Mission, this development could be a game-changer, revolutionising industrial, consumer and strategic applications. The national importance of such research cannot be overstated.” 

With support from the Ministry of Electronics and Information Technology, the IISc team is now focused on developing a fully indigenous integrated neuromorphic chip. “This is a completely home-grown effort, from materials to circuits and systems,” emphasises Sreetosh Goswami. “We are well on our way to translating this technology into a system-on-a-chip.”  

Caption: Using their AI accelerator, the team recreated NASA’s iconic “Pillars of Creation” image from the James Webb Space Telescope data on a simple tabletop computer – achieving this in a fraction of the time and energy required by traditional systems. Credit: CeNSE, IISc

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

Linear symmetric self-selecting 14-bit kinetic molecular memristors by Deepak Sharma, Santi Prasad Rath, Bidyabhusan Kundu, Anil Korkmaz, Harivignesh S, Damien Thompson, Navakanta Bhat, Sreebrata Goswami, R. Stanley Williams & Sreetosh Goswami. Nature volume 633, pages 560–566 (2024) DOI: https://doi.org/10.1038/s41586-024-07902-2 Published online: 11 September 2024 Issue Date: 19 September 2024

This paper is behind a paywall.

Light-based neural networks

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It’s unusual to see the same headline used to highlight research from two different teams released in such proximity, February 2024 and July 2024, respectively. Both of these are neuromorphic (brainlike) computing stories.

February 2024: Neural networks made of light

The first team’s work is announced in a February 21, 2024 Friedrich Schiller University press release, Note: A link has been removed,

Researchers from the Leibniz Institute of Photonic Technology (Leibniz IPHT) and the Friedrich Schiller University in Jena, along with an international team, have developed a new technology that could significantly reduce the high energy demands of future AI systems. This innovation utilizes light for neuronal computing, inspired by the neural networks of the human brain. It promises not only more efficient data processing but also speeds many times faster than current methods, all while consuming considerably less energy. Published in the prestigious journal „Advanced Science,“ their work introduces new avenues for environmentally friendly AI applications, as well as advancements in computerless diagnostics and intelligent microscopy.

Artificial intelligence (AI) is pivotal in advancing biotechnology and medical procedures, ranging from cancer diagnostics to the creation of new antibiotics. However, the ecological footprint of large-scale AI systems is substantial. For instance, training extensive language models like ChatGPT-3 requires several gigawatt-hours of energy—enough to power an average nuclear power plant at full capacity for several hours.

Prof. Mario Chemnitz, new Junior Professor of Intelligent Photonic SystemsExternal link at Friedrich Schiller University Jena, and Dr Bennet Fischer from Leibniz IPHT in Jena, in collaboration with their international team, have devised an innovative method to develop potentially energy-efficient computing systems that forego the need for extensive electronic infrastructure. They harness the unique interactions of light waves within optical fibers to forge an advanced artificial learning system.

A single fiber instead of thousands of components

Unlike traditional systems that rely on computer chips containing thousands of electronic components, their system uses a single optical fiber. This fiber is capable of performing the tasks of various neural networks—at the speed of light. “We utilize a single optical fiber to mimic the computational power of numerous neural networks,“ Mario Chemnitz, who is also leader of the “Smart Photonics“ junior research group at Leibniz IPHT, explains. “By leveraging the unique physical properties of light, this system will enable the rapid and efficient processing of vast amounts of data in the future.“

Delving into the mechanics reveals how information transmission occurs through the mixing of light frequencies: Data—whether pixel values from images or frequency components of an audio track—are encoded onto the color channels of ultrashort light pulses. These pulses carry the information through the fiber, undergoing various combinations, amplifications, or attenuations. The emergence of new color combinations at the fiber’s output enables the prediction of data types or contexts. For example, specific color channels can indicate visible objects in images or signs of illness in a voice.

A prime example of machine learning is identifying different numbers from thousands of handwritten characters. Mario Chemnitz, Bennet Fischer, and their colleagues from the Institut National de la Recherche Scientifique (INRS) in Québec utilized their technique to encode images of handwritten digits onto light signals and classify them via the optical fiber. The alteration in color composition at the fiber’s end forms a unique color spectrum—a „fingerprint“ for each digit. Following training, the system can analyze and recognize new handwriting digits with significantly reduced energy consumption.

System recognizes COVID-19 from voice samples

“In simpler terms, pixel values are converted into varying intensities of primary colors—more red or less blue, for instance,“ Mario Chemnitz details. “Within the fiber, these primary colors blend to create the full spectrum of the rainbow. The shade of our mixed purple, for example, reveals much about the data processed by our system.“

The team has also successfully applied this method in a pilot study to diagnose COVID-19 infections using voice samples, achieving a detection rate that surpasses the best digital systems to date.

“We are the first to demonstrate that such a vibrant interplay of light waves in optical fibers can directly classify complex information without any additional intelligent software,“ Mario Chemnitz states.

Since December 2023, Mario Chemnitz has held the position of Junior Professor of Intelligent Photonic Systems at Friedrich Schiller University Jena. Following his return from INRS in Canada in 2022, where he served as a postdoc, Chemnitz has been leading an international team at Leibniz IPHT in Jena. With Nexus funding support from the Carl Zeiss Foundation, their research focuses on exploring the potentials of non-linear optics. Their goal is to develop computer-free intelligent sensor systems and microscopes, as well as techniques for green computing.

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

Neuromorphic Computing via Fission-based Broadband Frequency Generation by Bennet Fischer, Mario Chemnitz, Yi Zhu, Nicolas Perron, Piotr Roztocki, Benjamin MacLellan, Luigi Di Lauro, A. Aadhi, Cristina Rimoldi, Tiago H. Falk, Roberto Morandotti. Advanced Science Volume 10, Issue 35 December 15, 2023 2303835 DOI: https://doi.org/10.1002/advs.202303835. First published: 02 October 2023

This paper is open access.

July 2024: Neural networks made of light

A July 12, 2024 news item on ScienceDaily announces research from another German team,

Scientists propose a new way of implementing a neural network with an optical system which could make machine learning more sustainable in the future. The researchers at the Max Planck Institute for the Science of Light have published their new method in Nature Physics, demonstrating a method much simpler than previous approaches.

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A July 12, 2024 Max Planck Institute for the Science of Light press release (also on EurekAlert), which originated the news item, provides more detail about their approach to neuromorphic computiing,

Machine learning and artificial intelligence are becoming increasingly widespread with applications ranging from computer vision to text generation, as demonstrated by ChatGPT. However, these complex tasks require increasingly complex neural networks; some with many billion parameters. This rapid growth of neural network size has put the technologies on an unsustainable path due to their exponentially growing energy consumption and training times. For instance, it is estimated that training GPT-3 consumed more than 1,000 MWh of energy, which amounts to the daily electrical energy consumption of a small town. This trend has created a need for faster, more energy- and cost-efficient alternatives, sparking the rapidly developing field of neuromorphic computing. The aim of this field is to replace the neural networks on our digital computers with physical neural networks. These are engineered to perform the required mathematical operations physically in a potentially faster and more energy-efficient way.

Optics and photonics are particularly promising platforms for neuromorphic computing since energy consumption can be kept to a minimum. Computations can be performed in parallel at very high speeds only limited by the speed of light. However, so far, there have been two significant challenges: Firstly, realizing the necessary complex mathematical computations requires high laser powers. Secondly, the lack of an efficient general training method for such physical neural networks.

Both challenges can be overcome with the new method proposed by Clara Wanjura and Florian Marquardt from the Max Planck Institute for the Science of Light in their new article in Nature Physics. “Normally, the data input is imprinted on the light field. However, in our new methods we propose to imprint the input by changing the light transmission,” explains Florian Marquardt, Director at the Institute. In this way, the input signal can be processed in an arbitrary fashion. This is true even though the light field itself behaves in the simplest way possible in which waves interfere without otherwise influencing each other. Therefore, their approach allows one to avoid complicated physical interactions to realize the required mathematical functions which would otherwise require high-power light fields. Evaluating and training this physical neural network would then become very straightforward: “It would really be as simple as sending light through the system and observing the transmitted light. This lets us evaluate the output of the network. At the same time, this allows one to measure all relevant information for the training”, says Clara Wanjura, the first author of the study. The authors demonstrated in simulations that their approach can be used to perform image classification tasks with the same accuracy as digital neural networks.

In the future, the authors are planning to collaborate with experimental groups to explore the implementation of their method. Since their proposal significantly relaxes the experimental requirements, it can be applied to many physically very different systems. This opens up new possibilities for neuromorphic devices allowing physical training over a broad range of platforms.

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

Fully nonlinear neuromorphic computing with linear wave scattering by Clara C. Wanjura & Florian Marquardt. Nature Physics (2024) DOI: https://doi.org/10.1038/s41567-024-02534-9 Published: 09 July 2024

This paper is open access.

Dual functions—neuromorphic (brainlike) and security—with papertronic devices

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Michael Berger’s June 27, 2024 Nanowerk Spotlight article describes some of the latest work on developing electronic paper devices (yes, paper), Note 1: Links have been removed, Note 2: If you do check out Berger’s article, you will need to click a box confirming you are human,+

Paper-based electronic devices have long been an intriguing prospect for researchers, offering potential advantages in sustainability, cost-effectiveness, and flexibility. However, translating the unique properties of paper into functional electronic components has presented significant challenges. Traditional semiconductor manufacturing processes are incompatible with paper’s thermal sensitivity and porous structure. Previous attempts to create paper-based electronics often resulted in devices with limited functionality or poor durability.

Recent advances in materials science and nanofabrication techniques have opened new avenues for realizing sophisticated electronic devices on paper substrates. Researchers have made progress in developing conductive inks, flexible electrodes, and solution-processable semiconductors that can be applied to paper without compromising its inherent properties. These developments have paved the way for creating paper-based sensors, energy storage devices, and simple circuits.

Despite these advancements, achieving complex electronic functionalities on paper, particularly in areas like neuromorphic computing and security applications, has remained elusive. Neuromorphic devices, which mimic the behavior of biological synapses, typically require precise control of charge transport and storage mechanisms.

Similarly, physically unclonable functions (PUFs) used in security applications depend on the ability to generate random, unique patterns at the nanoscale level. Implementing these sophisticated functionalities on paper substrates has been a persistent challenge due to the material’s inherent variability and limited compatibility with advanced fabrication techniques.

A research team in Korea has now made significant strides in addressing these challenges, developing a versatile paper-based electronic device that demonstrates both neuromorphic and security capabilities. Their work, published in Advanced Materials (“Versatile Papertronics: Photo-Induced Synapse and Security Applications on Papers”), describes a novel approach to creating multifunctional “papertronics” using a combination of solution-processable materials and innovative device architectures.

…

The team showcased the potential of their device by simulating a facial recognition task. Using a simple neural network architecture and the light-responsive properties of their paper-based device, they achieved a recognition accuracy of 91.7% on a standard face database. This impressive performance was achieved with a remarkably low voltage bias of -0.01 V, demonstrating the energy efficiency of the approach. The ability to operate at such low voltages is particularly advantageous for portable and low-power applications.

In addition to its neuromorphic capabilities, the device also showed promise as a physically unclonable function (PUF) for security applications. The researchers leveraged the inherent randomness in the deposition of SnO2 nanoparticles [tin oxide nanoparticles] to create unique electrical characteristics in each device. By fabricating arrays of these devices on paper, they generated security keys that exhibited high levels of randomness and uniqueness.

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One of the most intriguing aspects of this research is the dual functionality achieved with a single device structure. The ability to serve as both a neuromorphic component and a security element could lead to the development of highly integrated, secure edge computing devices on paper substrates. This convergence of functionalities addresses growing concerns about data privacy and security in Internet of Things (IoT) applications.

…

Berger’s June 27, 2024 Nanowerk Spotlight article offers more detail about the work and it’s written in an accessible fashion. Berger also notes at the end, that there are still a lot of challenges before this work leaves the laboratory.

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

Versatile Papertronics: Photo-Induced Synapse and Security Applications on Papers by Wangmyung Choi, Jihyun Shin, Yeong Jae Kim, Jaehyun Hur, Byung Chul Jang, Hocheon Yoo. Advanced Materials DOI: https://doi.org/10.1002/adma.202312831 First published: 13 June 2024

This paper is behind a paywall.

Proposed platform for brain-inspired computing

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Researchers at the University of California at Santa Barbara (UCSB) have proposed a more energy-efficient architecture for neuromorphic (brainlike or brain-inspored) computing according to a June 25, 2024 news item on ScienceDaily,

Computers have come so far in terms of their power and potential, rivaling and even eclipsing human brains in their ability to store and crunch data, make predictions and communicate. But there is one domain where human brains continue to dominate: energy efficiency.

“The most efficient computers are still approximately four orders of magnitude — that’s 10,000 times — higher in energy requirements compared to the human brain for specific tasks such as image processing and recognition, although they outperform the brain in tasks like mathematical calculations,” said UC Santa Barbara electrical and computer engineering Professor Kaustav Banerjee, a world expert in the realm of nanoelectronics. “Making computers more energy efficient is crucial because the worldwide energy consumption by on-chip electronics stands at #4 in the global rankings of nation-wise energy consumption, and it is increasing exponentially each year, fueled by applications such as artificial intelligence.” Additionally, he said, the problem of energy inefficient computing is particularly pressing in the context of global warming, “highlighting the urgent need to develop more energy-efficient computing technologies.”

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A June 24, 2024 UCSB news release (also on Eurekalert), which originated the news item, delves further into the subject,

Neuromorphic (NM) computing has emerged as a promising way to bridge the energy efficiency gap. By mimicking the structure and operations of the human brain, where processing occurs in parallel across an array of low power-consuming neurons, it may be possible to approach brain-like energy efficiency. In a paper published in the journal Nature Communications, Banerjee and co-workers Arnab Pal, Zichun Chai, Junkai Jiang and Wei Cao, in collaboration with researchers Vivek De and Mike Davies from Intel Labs propose such an ultra-energy efficient platform, using 2D transition metal dichalcogenide (TMD)-based tunnel-field-effect transistors (TFETs). Their platform, the researchers say, can bring the energy requirements to within two orders of magnitude (about 100 times) with respect to the human brain.

Leakage currents and subthreshold swing

The concept of neuromorphic computing has been around for decades, though the research around it has intensified only relatively recently. Advances in circuitry that enable smaller, denser arrays of transistors, and therefore more processing and functionality for less power consumption are just scratching the surface of what can be done to enable brain-inspired computing. Add to that an appetite generated by its many potential applications, such as AI and the Internet-of-Things, and it’s clear that expanding the options for a hardware platform for neuromorphic computing must be addressed in order to move forward.

Enter the team’s 2D tunnel-transistors. Emerging out of Banerjee’s longstanding research efforts to develop high-performance, low-power consumption transistors to meet the growing hunger for processing without a matching increase in power requirement, these atomically thin, nanoscale transistors are responsive at low voltages, and as the foundation of the researchers’ NM platform, can mimic the highly energy efficient operations of the human brain. In addition to lower off-state currents, the 2D TFETs also have a low subthreshold swing (SS), a parameter that describes how effectively a transistor can switch from off to on. According to Banerjee, a lower SS means a lower operating voltage, and faster and more efficient switching.

“Neuromorphic computing architectures are designed to operate with very sparse firing circuits,” said lead author Arnab Pal, “meaning they mimic how neurons in the brain fire only when necessary.” In contrast to the more conventional von Neumann architecture of today’s computers, in which data is processed sequentially, memory and processing components are separated and which continuously draw power throughout the entire operation, an event-driven system such as a NM computer fires up only when there is input to process, and memory and processing are distributed across an array of transistors. Companies like Intel and IBM have developed brain-inspired platforms, deploying billions of interconnected transistors and generating significant energy savings.

However, there’s still room for energy efficiency improvement, according to the researchers.

“In these systems, most of the energy is lost through leakage currents when the transistors are off, rather than during their active state,” Banerjee explained. A ubiquitous phenomenon in the world of electronics, leakage currents are small amounts of electricity that flow through a circuit even when it is in the off state (but still connected to power). According to the paper, current NM chips use traditional metal-oxide-semiconductor field-effect transistors (MOSFETs) which have a high on-state current, but also high off-state leakage. “Since the power efficiency of these chips is constrained by the off-state leakage, our approach — using tunneling transistors with much lower off-state current — can greatly improve power efficiency,” Banerjee said.

When integrated into a neuromorphic circuit, which emulates the firing and reset of neurons, the TFETs proved themselves more energy efficient than state-of-the-art MOSFETs, particularly the FinFETs (a MOSFET design that incorporates vertical “fins” as a way to provide better control of switching and leakage). TFETs are still in the experimental stage, however the performance and energy efficiency of neuromorphic circuits based on them makes them a promising candidate for the next generation of brain-inspired computing.

According to co-authors Vivek De (Intel Fellow) and Mike Davies (Director of Intel’s Neuromorphic Computing Lab), “Once realized, this platform can bring the energy consumption in chips to within two orders of magnitude with respect to the human brain — not accounting for the interface circuitry and memory storage elements. This represents a significant improvement from what is achievable today.”

Eventually, one can realize three-dimensional versions of these 2D-TFET based neuromorphic circuits to provide even closer emulation of the human brain, added Banerjee, widely recognized as one of the key visionaries behind 3D integrated circuits that are now witnessing wide scale commercial proliferation.

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

An ultra energy-efficient hardware platform for neuromorphic computing enabled by 2D-TMD tunnel-FETs by Arnab Pal, Zichun Chai, Junkai Jiang, Wei Cao, Mike Davies, Vivek De & Kaustav Banerjee. Nature Communications volume 15, Article number: 3392 (2024) DOI: https://doi.org/10.1038/s41467-024-46397-3 Published: 22 April 2024

This paper is open access.