Researcher's computer components could cut energy consumption 'by factor of 100'

'Memristor' chips can store information at different resistance levels

The energy consumption of computing devices could be cut by a factor of 100 if the University of Michigan has its way.

The institution announced that one of its researchers has developed a new way of arranging advanced computer components called "memristors" on a chip that could enable them to be used for general computing.

The researcher, who goes by the name of Wei Lu, is a professor of electrical and computer engineering at the University, as well as the co-founder of memristor startup Crossbar.

Lu claims that the technology could improve performance in low-power compute environments, such as smartphones, or help make more power-efficient supercomputers.

"Historically, the semiconductor industry has improved performance by making devices faster. But although the processors and memories are very fast, they can't be efficient because they have to wait for data to come in and out," said Lu, adding that memristors might be the answer.

Named as a hybrid of memory and resistor, this memristor tech can apparently be programmed to have different resistance states, meaning they store information as resistance levels.

These circuit elements enable memory and processing in the same device, cutting out the data-transfer bottleneck experienced by conventional computers in which the memory is separate from the processor.

However, unlike ordinary bits, which are 1 or 0, memristors can have resistances that are on a continuum. Some applications, such as computing that mimics the brain, take advantage of the analogue nature of memristors.

But for ordinary computing, trying to differentiate among small variations in the current passing through a memristor device is not precise enough for numerical calculations.

Lu and his colleagues said they got around this problem by digitising the current outputs, defining current ranges as specific bit values (for example; 0 or 1). The team was also able to map large mathematical problems into smaller blocks within the array, improving the efficiency and flexibility of the system.