Demonstration of memflector brings brain-like computing a step closer

The first ever demonstration of simultaneous information processing and storage using phase-change materials points the way to computers more closely resembling biological systems, say University of Exeter researchers. The techniques used could revolutionize computing by making computers faster and more energy-efficient, as well as making them more “brain-like.”

Today’s computers handle processing and memory separately, resulting in a speed and power bottleneck caused by the need to continually move data around. In human brains, however, no real distinction is made between memory and computation. To bring these two functions together in a computer, the research team used phase-change materials, a kind of semi-conductor that exhibits special properties.

Documenting their phase-change experiments in the journal Advanced Materials, the researchers show how they can not only perform a range of general-purpose computing operations, but also emulate a number of brain-like functions. Specifically, they demonstrated:

  • The reliable execution by a phase-change processor of the four basic arithmetic functions of addition, subtraction, multiplication and division.
  • The demonstration of an “integrate and fire” hardware neuron using a single phase-change cell.
  • The exposition of synaptic-like functionality via a “memflector” (an optical analogue of the memristor).

The team’s fledgling attempts at the creation of artificial neurons and synapses could point the way to devices that learn and process information in a similar way to our own brains. “We have uncovered a technique for potentially developing new forms of ‘brain-like’ computer systems that could learn, adapt and change over time. This is something that researchers have been striving for over many years,” said lead author Professor David Wright.

The next stage in the team’s research will be to build systems of interconnected cells that can learn to perform simple tasks, such as identification of objects and patterns.

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Source: University of Exeter


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