The development of "brain-like" computers took a major step forward recently with the publication of research led by the University of Exeter.
The study, which was published in the journal Advanced Materials and funded by the UK's Engineering and Physical Sciences Research Council, involved the first ever demonstration of simultaneous information processing and storage using phase-change materials.
It's a novel technique that could revolutionize computing by making computers faster and more energy-efficient, as well as making them more closely resemble biological systems.
Computers currently deal with processing and memory separately, resulting in a speed and power "bottleneck" caused by the need to continually move data around. This is totally unlike anything in biology. For example, human brains differ from traditional computers because they make no real distinction between memory and computation.
In order to mimic the brain's method of processing and perform both memory and computational functions simultaneously, the University of Exeter research team used phase-change materials, a kind of semi-conductor that exhibits remarkable properties.
Their study demonstrates conclusively that phase-change materials can store and process information simultaneously. It also shows experimentally for the first time that these materials can perform general-purpose computing operations, such as addition, subtraction, multiplication, and division. More strikingly perhaps, the work shows that phase-change materials can be used to make artificial neurons and synapses. This means that an artificial system made entirely from phase-change devices could potentially learn and process information in a similar way to our own brains.
Lead author, Professor David Wright of the University of Exeter said: “Our findings have major implications for the development of entirely new forms of computing, including ‘brain-like’ computers. 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.”
This study focused on the performance of a single phase-change cell. The next stage in Exeter's research will be to build systems of interconnected cells that can learn to perform simple tasks, such as identification of certain objects and patterns.