New research, led by the University of Southampton, has demonstrated that a nanoscale device, called a memristor, could be used to power artificial systems that can mimic the human brain.
Artificial neural networks (ANNs) exhibit learning abilities and can perform tasks which are difficult for conventional computing systems, such as pattern recognition, on-line learning and classification. Practical ANN implementations are currently hampered by the lack of efficient hardware synapses; a key component that every ANN requires in large numbers.
In the study, published in Nature Communications, the Southampton research team experimentally demonstrated an ANN that used memristor synapses supporting sophisticated learning rules in order to carry out reversible learning of noisy input data.
Memristors are electrical components that limit or regulate the flow of electrical current in a circuit and can remember the amount of charge that was flowing through it and retain the data, even when the power is turned off.
Lead author Dr Alex Serb, from Electronics and Computer Science at the University of Southampton, said: “If we want to build artificial systems that can mimic the brain in function and power we need to use hundreds of billions, perhaps even trillions of artificial synapses, many of which must be able to implement learning rules of varying degrees of complexity. Whilst currently available electronic components can certainly be pieced together to create such synapses, the required power and area efficiency benchmarks will be extremely difficult to meet -if even possible at all- without designing new and bespoke ‘synapse components’.
“Memristors offer a possible route towards that end by supporting many fundamental features of learning synapses (memory storage, on-line learning, computationally powerful learning rule implementation, two-terminal structure) in extremely compact volumes and at exceptionally low energy costs. If artificial brains are ever going to become reality, therefore, memristive synapses have to succeed.”
Acting like synapses in the brain, the metal-oxide memristor array was capable of learning and re-learning input patterns in an unsupervised manner within a probabilistic winner-take-all (WTA) network. This is extremely useful for enabling low-power embedded processors (needed for the Internet of Things) that can process in real-time big data without any prior knowledge of the data.
Co-author Dr Themis Prodromakis, Reader in Nanoelectronics and EPSRC Fellow in Electronics and Computer Science at the University of Southampton, said: “The uptake of any new technology is typically hampered by the lack of practical demonstrators that showcase the technology’s benefits in practical applications. Our work establishes such a technological paradigm shift, proving that nanoscale memristors can indeed be used to formulate in-silico neural circuits for processing big-data in real-time; a key challenge of modern society.
“We have shown that such hardware platforms can independently adapt to its environment without any human intervention and are very resilient in processing even noisy data in real-time reliably. This new type of hardware could find a diverse range of applications in pervasive sensing technologies to fuel real-time monitoring in harsh or inaccessible environments; a highly desirable capability for enabling the Internet of Things vision.”
Future nanoelectronic information storage devices are also tiny batteries – astounding finding opens up new possibilities
Resistive memory cells (ReRAM) are regarded as a promising solution for future generations of computer memories. They will dramatically reduce the energy consumption of modern IT systems while significantly increasing their performance. Unlike the building blocks of conventional hard disk drives and memories, these novel memory cells are not purely passive components but must be regarded as tiny batteries. This has been demonstrated by researchers of Jülich Aachen Research Alliance (JARA), whose findings have now been published in the prestigious journal Nature Communications. The new finding radically revises the current theory and opens up possibilities for further applications. The research group has already filed a patent application for their first idea on how to improve data readout with the aid of battery voltage.
Conventional data memory works on the basis of electrons that are moved around and stored. However, even by atomic standards, electrons are extremely small. It is very difficult to control them, for example by means of relatively thick insulator walls, so that information will not be lost over time. This does not only limit storage density, it also costs a great deal of energy. For this reason, researchers are working feverishly all over the world on nanoelectronic components that make use of ions, i.e. charged atoms, for storing data. Ions are some thousands of times heavier that electrons and are therefore much easier to ‘hold down’. In this way, the individual storage elements can almost be reduced to atomic dimensions, which enormously improves the storage density.