Darwinian evolution and neural networks

‘Designless’ nanoscale logic circuits resemble Cognitive Science/Neuroscience

September 30, 2015

Illustration of a nanoparticle network (about 200 nanometers in diameter). By applying electrical signals at the electrodes (yellow), and using artificial evolution, this disordered network can be configured into useful logic circuits. (credit: University of Twente)

Researchers at the University of Twente in The Netherlands have designed and demonstrated working electronic logic circuits produced using methods that resemble Darwinian evolution and neural networks like the human brain.

In a radical “designless” approach, the researchers used a 200-nanometer-wide cluster of 20-nanometer gold nanoparticles. They applied a series of voltages to eight electrodes and determined the resulting set of 16 different two-input Boolean logic gates.

Artificial evolution

Instead of designing logic circuits with specified functions, as with conventional transistors, this approach works around — or can even take advantage of — any material defects.

To do this, the researchers used an artificial evolution model — one that runs in less than an hour, rather than millions of years. “Natural evolution has led to powerful ‘computers’ like the human brain, which can solve complex problems in an energy-efficient way,” the researchers note. “Nature exploits complex networks that can execute many tasks in parallel.”

“This is the first time that scientists have succeeded in realizing robust electronics with small dimensions that can compete with commercial technology.”

Schematic device layout of the disordered network of gold nanoparticles, separated by ~1 nm 1-octanethiols, in between eight titanium-gold electrodes, as shown in the scanning electron micrograph (top inset). The gold nanoparticles act as single-electron transistors (SETs) at low temperature (<15 K). (credit: S. K. Bose, et al./Nature Nanotechnology)

Conventional transistors are limited to a handful of atoms. It would a major challenge to produce chips in which the millions of transistors required have the same characteristics, according to the researchers from the Twente MESA+ Institute for Nanotechnology and the CTIT Institute for ICT Research. Current transistor designs are also limited by their energy consumption, which is reaching unacceptable levels.

According to University of Twente prof. Wilfred van der Wiel, the logic circuits they discovered currently have limited computing power. “But with this research we have delivered a proof of principle. By scaling up the system, real added value will be produced in the future. This type of circuitry uses much less energy, both in production and use.” The researchers anticipate a wide range of applications — for example, in portable electronics and in the medical world.

“By choosing a smaller nanoparticle diameter, and scaling down the electrode geometry accordingly, our network would not only further reduce area, but room-temperature operation would come into sight as well,” the researchers note in a paper in the journal Nature Nanotechnology.

Mimicking brain-like systems

The researchers also contrast their “designless” reconfigurable approach with massively parallel (but still design-constrained) architectures such as IBM’s TrueNorth brain-inspired chip.

“An especially interesting avenue to explore is the suitability of this system for advanced functionality that is hard (or expensive) to realize in a conventional architecture, such as pattern recognition by mimicking brain-like systems, or simulations of complex physical systems,” the researchers note in the paper. “Our evolutionary approach works around device-to-device variations at the nanoscale and the accompanying uncertainties in performance, which is becoming more and more a bottleneck for the miniaturization of conventional electronic circuits.”

Abstract of Evolution of a designless nanoparticle network into reconfigurable Boolean logic

Natural computers exploit the emergent properties and massive parallelism of interconnected networks of locally active components. Evolution has resulted in systems that compute quickly and that use energy efficiently, utilizing whatever physical properties are exploitable. Man-made computers, on the other hand, are based on circuits of functional units that follow given design rules. Hence, potentially exploitable physical processes, such as capacitive crosstalk, to solve a problem are left out. Until now, designless nanoscale networks of inanimate matter that exhibit robust computational functionality had not been realized. Here we artificially evolve the electrical properties of a disordered nanomaterials system (by optimizing the values of control voltages using a genetic algorithm) to perform computational tasks reconfigurably. We exploit the rich behaviour that emerges from interconnected metal nanoparticles, which act as strongly nonlinear single-electron transistors, and find that this nanoscale architecture can be configured in situ into any Boolean logic gate. This universal, reconfigurable gate would require about ten transistors in a conventional circuit. Our system meets the criteria for the physical realization of (cellular) neural networks: universality (arbitrary Boolean functions), compactness, robustness and evolvability, which implies scalability to perform more advanced tasks. Our evolutionary approach works around device-to-device variations and the accompanying uncertainties in performance. Moreover, it bears a great potential for more energy-efficient computation, and for solving problems that are very hard to tackle in conventional architectures.

New Device Could Greatly Improve Speech and Image Recognition

New Device Could Greatly Improve Speech and Image Recognition

Researchers have demonstrated pattern recognition using a magnonic holographic memory device

RIVERSIDE, Calif. (www.ucr.edu) — Researchers at the University of California, Riverside Bourns College of Engineering and the Russian Academy of Sciences have successfully demonstrated pattern recognition using a magnonic holographic memory device, a development that could greatly improve speech and image recognition hardware.

Pattern recognition focuses on finding patterns and regularities in data. The uniqueness of the demonstrated work is that the input patterns are encoded into the phases of the input spin waves.

Spin waves are collective oscillations of spins in magnetic materials. Spin wave devices are advantageous over their optical counterparts because they are more scalable due to a shorter wavelength. Also, spin wave devices are compatible with conventional electronic devices and can be integrated within a chip.

The researchers built a prototype eight-terminal device consisting of a magnetic matrix with micro-antennas to excite and detect the spin waves. Experimental data they collected for several magnonic matrixes show unique output signatures correspond to specific phase patterns. The microantennas allow the researchers to generate and recognize any input phase pattern, a big advantage over existing practices.

illustrations/photos of magnonic holographic devices

Clockwise, photo of the prototype device; schematic of the eight-terminal magnonic holographic memory prototype; collection of experimental data obtained for two magnonic matrixes.

Then spin waves propagate through the magnetic matrix and interfere. Some of the input phase patterns produce high output voltage, and other combinations results in a low output voltage, where “high” and “low” are defined regarding the reference voltage (i.e. output is high if the output voltage is higher than 1 millivolt, and low if the voltage is less than 1 millivolt.

It takes about 100 nanoseconds for recognition, which is the time required for spin waves to propagate and to create the interference pattern.

The most appealing property of this approach is that all of the input ports operate in parallel. It takes the same amount of time to recognize patterns (numbers) from 0 to 999, and from 0 to 10,000,000. Potentially, magnonic holographic devices can be fundamentally more efficient than conventional digital circuits.

The work builds upon findings published last year by the researchers, who showed a 2-bit magnonic holographic memory device can recognize the internal magnetic memory states via spin wave superposition. That work was recognized as a top 10 physics breakthrough by Physics World magazine.

“We were excited by that recognition, but the latest research takes this to a new level,” said Alex Khitun, a research professor at UC Riverside, who is the lead researcher on the project. “Now, the device works not only as a memory but also a logic element.”

The latest findings were published in a paper called “Pattern recognition with magnonic holographic memory device” in the journal Applied Physics Letters. In addition to Khitun, authors are Frederick Gertz, a graduate student who works with Khitun at UC Riverside, and A. Kozhevnikov, Y. Filimonov and G. Dudko, all from the Russian Academy of Sciences.

Holography is a technique based on the wave nature of light which allows the use of wave interference between the object beam and the coherent background. It is commonly associated with images being made from light, such as on driver’s licenses or paper currency. However, this is only a narrow field of holography.

Holography has been also recognized as a future data storing technology with unprecedented data storage capacity and ability to write and read a large number of data in a highly parallel manner.

The main challenge associated with magnonic holographic memory is the scaling of the operational wavelength, which requires the development of sub-micrometer scale elements for spin wave generation and detection.

The research was supported in part by: The Center for Function Accelerated nanoMaterial Engineering (FAME), which is funded with $35 million from the Semiconductor Research Corporation (SRC), a consortium of semiconductor industry companies; the Defense Advanced Research Projects Agency; and the National Science Foundation under the NEB2020 Grant ECCS-1124714.


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New Device Could Greatly Improve Speech and Image Recognition