Computers doing "approximate computing" for tasks not requiring perfect accuracy could double efficiency and reduce energy consumption, U.S. scientists say.

Such computers could run a growing number of applications designed to tolerate "noisy" real-world inputs and use statistical or probabilistic types of computations, computer engineers at Purdue University said.

"The need for approximate computing is driven by two factors: a fundamental shift in the nature of computing workloads, and the need for new sources of efficiency," said researcher Anand Raghunathan.

"Computers were first designed to be precise calculators that solved problems where they were expected to produce an exact numerical value," he said. "However, the demand for computing today is driven by very different applications."

Current computers are designed to compute precise results even when it is not necessary, the researchers said, whereas approximate computing could endow computers with a capability similar to the human brain's ability to scale the degree of accuracy needed for a given task.

"If I asked you to divide 500 by 21 and I asked you whether the answer is greater than one, you would say yes right away," Raghunathan said. "You are doing division but not to the full accuracy. If I asked you whether it is greater than 30, you would probably take a little longer, but if I ask you if it's greater than 23, you might have to think even harder.

"The application context dictates different levels of effort, and humans are capable of this scalable approach, but computer software and hardware are not like that."

Purdue researchers said they're working on a range of hardware techniques to demonstrate approximate computing, showing a potential for improvements in energy efficiency.