Tag Archives: artificial neurons

Electronics begone! Enter: the light-based brainlike computing chip

At this point, it’s possible I’m wrong but I think this is the first ‘memristor’ type device (also called a neuromorphic chip) based on light rather than electronics that I’ve featured here on this blog. In other words, it’s not, technically speaking, a memristor but it does have the same properties so it is a neuromorphic chip.

Caption: The optical microchips that the researchers are working on developing are about the size of a one-cent piece. Credit: WWU Muenster – Peter Leßmann

A May 8, 2019 news item on Nanowerk announces this new approach to neuromorphic hardware (Note: A link has been removed),

Researchers from the Universities of Münster (Germany), Oxford and Exeter (both UK) have succeeded in developing a piece of hardware which could pave the way for creating computers which resemble the human brain.

The scientists produced a chip containing a network of artificial neurons that works with light and can imitate the behaviour of neurons and their synapses. The network is able to “learn” information and use this as a basis for computing and recognizing patterns. As the system functions solely with light and not with electrons, it can process data many times faster than traditional systems. …

A May 8, 2019 University of Münster press release (also on EurekAlert), which originated the news item, reveals the full story,

A technology that functions like a brain? In these times of artificial intelligence, this no longer seems so far-fetched – for example, when a mobile phone can recognise faces or languages. With more complex applications, however, computers still quickly come up against their own limitations. One of the reasons for this is that a computer traditionally has separate memory and processor units – the consequence of which is that all data have to be sent back and forth between the two. In this respect, the human brain is way ahead of even the most modern computers because it processes and stores information in the same place – in the synapses, or connections between neurons, of which there are a million-billion in the brain. An international team of researchers from the Universities of Münster (Germany), Oxford and Exeter (both UK) have now succeeded in developing a piece of hardware which could pave the way for creating computers which resemble the human brain. The scientists managed to produce a chip containing a network of artificial neurons that works with light and can imitate the behaviour of neurons and their synapses.

The researchers were able to demonstrate, that such an optical neurosynaptic network is able to “learn” information and use this as a basis for computing and recognizing patterns – just as a brain can. As the system functions solely with light and not with traditional electrons, it can process data many times faster. “This integrated photonic system is an experimental milestone,” says Prof. Wolfram Pernice from Münster University and lead partner in the study. “The approach could be used later in many different fields for evaluating patterns in large quantities of data, for example in medical diagnoses.” The study is published in the latest issue of the “Nature” journal.

The story in detail – background and method used

Most of the existing approaches relating to so-called neuromorphic networks are based on electronics, whereas optical systems – in which photons, i.e. light particles, are used – are still in their infancy. The principle which the German and British scientists have now presented works as follows: optical waveguides that can transmit light and can be fabricated into optical microchips are integrated with so-called phase-change materials – which are already found today on storage media such as re-writable DVDs. These phase-change materials are characterised by the fact that they change their optical properties dramatically, depending on whether they are crystalline – when their atoms arrange themselves in a regular fashion – or amorphous – when their atoms organise themselves in an irregular fashion. This phase-change can be triggered by light if a laser heats the material up. “Because the material reacts so strongly, and changes its properties dramatically, it is highly suitable for imitating synapses and the transfer of impulses between two neurons,” says lead author Johannes Feldmann, who carried out many of the experiments as part of his PhD thesis at the Münster University.

In their study, the scientists succeeded for the first time in merging many nanostructured phase-change materials into one neurosynaptic network. The researchers developed a chip with four artificial neurons and a total of 60 synapses. The structure of the chip – consisting of different layers – was based on the so-called wavelength division multiplex technology, which is a process in which light is transmitted on different channels within the optical nanocircuit.

In order to test the extent to which the system is able to recognise patterns, the researchers “fed” it with information in the form of light pulses, using two different algorithms of machine learning. In this process, an artificial system “learns” from examples and can, ultimately, generalise them. In the case of the two algorithms used – both in so-called supervised and in unsupervised learning – the artificial network was ultimately able, on the basis of given light patterns, to recognise a pattern being sought – one of which was four consecutive letters.

“Our system has enabled us to take an important step towards creating computer hardware which behaves similarly to neurons and synapses in the brain and which is also able to work on real-world tasks,” says Wolfram Pernice. “By working with photons instead of electrons we can exploit to the full the known potential of optical technologies – not only in order to transfer data, as has been the case so far, but also in order to process and store them in one place,” adds co-author Prof. Harish Bhaskaran from the University of Oxford.

A very specific example is that with the aid of such hardware cancer cells could be identified automatically. Further work will need to be done, however, before such applications become reality. The researchers need to increase the number of artificial neurons and synapses and increase the depth of neural networks. This can be done, for example, with optical chips manufactured using silicon technology. “This step is to be taken in the EU joint project ‘Fun-COMP’ by using foundry processing for the production of nanochips,” says co-author and leader of the Fun-COMP project, Prof. C. David Wright from the University of Exeter.

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

All-optical spiking neurosynaptic networks with self-learning capabilities by J. Feldmann, N. Youngblood, C. D. Wright, H. Bhaskaran & W. H. P. Pernice. Nature volume 569, pages208–214 (2019) DOI: https://doi.org/10.1038/s41586-019-1157-8 Issue Date: 09 May 2019

This paper is behind a paywall.

For the curious, I found a little more information about Fun-COMP (functionally-scaled computer technology). It’s a European Commission (EC) Horizon 2020 project coordinated through the University of Exeter. For information with details such as the total cost, contribution from the EC, the list of partnerships and more there is the Fun-COMP webpage on fabiodisconzi.com.

Brainlike computing with spintronic devices

Adding to the body of ‘memristor’ research I have here, there’s an April 17, 2019 news item on Nanowerk announcing the development of ‘memristor’ hardware by Japanese researchers (Note: A link has been removed),

A research group from Tohoku University has developed spintronics devices which are promising for future energy-efficient and adoptive computing systems, as they behave like neurons and synapses in the human brain (Advanced Materials, “Artificial Neuron and Synapse Realized in an Antiferromagnet/Ferromagnet Heterostructure Using Dynamics of Spin–Orbit Torque Switching”).

Just because this ‘synapse’ is pretty,

Courtesy: Tohoku University

An April 16, 2019 Tohoku University press release, which originated the news item, expands on the theme,

Today’s information society is built on digital computers that have evolved drastically for half a century and are capable of executing complicated tasks reliably. The human brain, by contrast, operates under very limited power and is capable of executing complex tasks efficiently using an architecture that is vastly different from that of digital computers.

So the development of computing schemes or hardware inspired by the processing of information in the brain is of broad interest to scientists in fields ranging from physics, chemistry, material science and mathematics, to electronics and computer science.

In computing, there are various ways to implement the processing of information by a brain. Spiking neural network is a kind of implementation method which closely mimics the brain’s architecture and temporal information processing. Successful implementation of spiking neural network requires dedicated hardware with artificial neurons and synapses that are designed to exhibit the dynamics of biological neurons and synapses.

Here, the artificial neuron and synapse would ideally be made of the same material system and operated under the same working principle. However, this has been a challenging issue due to the fundamentally different nature of the neuron and synapse in biological neural networks.

The research group – which includes Professor Hideo Ohno (currently the university president), Associate Professor Shunsuke Fukami, Dr. Aleksandr Kurenkov and Professor Yoshihiko Horio – created an artificial neuron and synapse by using spintronics technology. Spintronics is an academic field that aims to simultaneously use an electron’s electric (charge) and magnetic (spin) properties.

The research group had previously developed a functional material system consisting of antiferromagnetic and ferromagnetic materials. This time, they prepared artificial neuronal and synaptic devices microfabricated from the material system, which demonstrated fundamental behavior of biological neuron and synapse – leaky integrate-and-fire and spike-timing-dependent plasticity, respectively – based on the same concept of spintronics.

The spiking neural network is known to be advantageous over today’s artificial intelligence for the processing and prediction of temporal information. Expansion of the developed technology to unit-circuit, block and system levels is expected to lead to computers that can process time-varying information such as voice and video with a small amount of power or edge devices that have the an ability to adopt users and the environment through usage.

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

Artificial Neuron and Synapse Realized in an Antiferromagnet/Ferromagnet Heterostructure Using Dynamics of Spin–Orbit Torque Switching by Aleksandr Kurenkov, Samik DuttaGupta, Chaoliang Zhang, Shunsuke Fukami, Yoshihiko Horio, Hideo Ohno. Advanced Materials https://doi.org/10.1002/adma.201900636 First published: 16 April 2019

This paper is behind a paywall.

Memristive capabilities from IBM (International Business Machines)

Does memristive mean it’s like a memristor but it’s not one? In any event, IBM is claiming some new ground in the world of cognitive computing (also known as, neuromorphic computing).

An artistic rendering of a population of stochastic phase-change neurons which appears on the cover of Nature Nanotechnology, 3 August 2016. (Credit: IBM Research)

An artistic rendering of a population of stochastic phase-change neurons which appears on the cover of Nature Nanotechnology, 3 August 2016. (Credit: IBM Research)

From an Aug. 3, 2016 news item on phys.org,

IBM scientists have created randomly spiking neurons using phase-change materials to store and process data. This demonstration marks a significant step forward in the development of energy-efficient, ultra-dense integrated neuromorphic technologies for applications in cognitive computing.

Inspired by the way the biological brain functions, scientists have theorized for decades that it should be possible to imitate the versatile computational capabilities of large populations of neurons. However, doing so at densities and with a power budget that would be comparable to those seen in biology has been a significant challenge, until now.

“We have been researching phase-change materials for memory applications for over a decade, and our progress in the past 24 months has been remarkable,” said IBM Fellow Evangelos Eleftheriou. “In this period, we have discovered and published new memory techniques, including projected memory, stored 3 bits per cell in phase-change memory for the first time, and now are demonstrating the powerful capabilities of phase-change-based artificial neurons, which can perform various computational primitives such as data-correlation detection and unsupervised learning at high speeds using very little energy.”

An Aug. 3, 2016 IBM news release, which originated the news item, expands on the theme,

The artificial neurons designed by IBM scientists in Zurich consist of phase-change materials, including germanium antimony telluride, which exhibit two stable states, an amorphous one (without a clearly defined structure) and a crystalline one (with structure). These materials are the basis of re-writable Blu-ray discs. However, the artificial neurons do not store digital information; they are analog, just like the synapses and neurons in our biological brain.

In the published demonstration, the team applied a series of electrical pulses to the artificial neurons, which resulted in the progressive crystallization of the phase-change material, ultimately causing the neuron to fire. In neuroscience, this function is known as the integrate-and-fire property of biological neurons. This is the foundation for event-based computation and, in principle, is similar to how our brain triggers a response when we touch something hot.

Exploiting this integrate-and-fire property, even a single neuron can be used to detect patterns and discover correlations in real-time streams of event-based data. For example, in the Internet of Things, sensors can collect and analyze volumes of weather data collected at the edge for faster forecasts. The artificial neurons could be used to detect patterns in financial transactions to find discrepancies or use data from social media to discover new cultural trends in real time. Large populations of these high-speed, low-energy nano-scale neurons could also be used in neuromorphic coprocessors with co-located memory and processing units.

IBM scientists have organized hundreds of artificial neurons into populations and used them to represent fast and complex signals. Moreover, the artificial neurons have been shown to sustain billions of switching cycles, which would correspond to multiple years of operation at an update frequency of 100 Hz. The energy required for each neuron update was less than five picojoule and the average power less than 120 microwatts — for comparison, 60 million microwatts power a 60 watt lightbulb.

“Populations of stochastic phase-change neurons, combined with other nanoscale computational elements such as artificial synapses, could be a key enabler for the creation of a new generation of extremely dense neuromorphic computing systems,” said Tomas Tuma, a co-author of the paper.

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

Stochastic phase-change neurons by Tomas Tuma, Angeliki Pantazi, Manuel Le Gallo, Abu Sebastian, & Evangelos Eleftheriou. Nature Nanotechnology  11, 693–699 (2016) doi:10.1038/nnano.2016.70 Published online 16 May 2016

I gather IBM waited for the print version of the paper before publicizing the work. The online version is behind paper. For those who can’t get past the paywall, there is a video offering a demonstration of sorts,

For the interested, the US government recently issued a white paper on neuromorphic computing (my Aug. 22, 2016 post).

This team has published a paper that has a similar theme to the one in Nature Nanotechnology,

All-memristive neuromorphic computing with level-tuned neurons by Angeliki Pantazi, Stanisław Woźniak, Tomas Tuma, and Evangelos Eleftheriou. Nanotechnology, Volume 27, Number 35  DOI: 10.1088/0957-4484/27/35/355205 Published 26 July 2016

© 2016 IOP Publishing Ltd

This paper is open access.

An Aug. 18, 2016 news piece by Lisa Zyga for phys.org provides a summary of the research in the July 2016 published paper.