AI driven APOLLO predicts processor power consumption

Computer engineers at Duke University have developed APOLLO, a new AI method for predicting the power consumption of any type of computer processor over a trillion times per second. 

APOLLO
Image by Colin Behrens from Pixabay

According to Duke, APOLLO uses very little computational power, has been validated on real-world, high-performance microprocessors and could help improve the efficiency and inform the development of new microprocessors.

The approach is detailed in a paper published at MICRO-54: 54th Annual IEEE/ACM International Symposium on Microarchitecture.

“This is an intensively studied problem that has traditionally relied on extra circuitry to address,” said Zhiyao Xie, first author of the paper and a PhD candidate in the laboratory of Yiran Chen, professor of electrical and computer engineering at Duke. “But our approach runs directly on the microprocessor in the background, which opens many new opportunities.”

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Cycles of computations in computer processors are made on the order of three trillion times per second and tracking the power consumed by these transitions is important to maintain the entire chip’s performance and efficiency. If a processor draws too much power, it can overheat and cause damage. Sudden swings in power demand can cause internal electromagnetic complications that can slow the entire processor down.

“APOLLO approaches an ideal power estimation algorithm that is both accurate and fast and can easily be built into a processing core at a low power cost,” Xie said. “And because it can be used in any type of processing unit, it could become a common component in future chip design.”

The algorithm developed by Xie and Chen uses AI to identify and select 100 of a processor’s millions of signals that correlate most closely with its power consumption. It then builds a power consumption model off of those 100 signals and monitors them to predict the entire chip’s performance in real-time.

Because this learning process is autonomous and data driven, it can be implemented on most computer processor architecture – even those yet to be invented, the University said. APOLLO does not require any human designer expertise to do its job, the algorithm could help human designers do theirs.

“After the AI selects its 100 signals, you can look at the algorithm and see what they are,” Xie said. “A lot of the selections make intuitive sense, but even if they don’t, they can provide feedback to designers by informing them which processes are most strongly correlated with power consumption and performance.”

With the help of Arm Research, APOLLO has been validated on some of today’s highest performing processors, but according to the researchers the algorithm still needs testing and comprehensive evaluations on more platforms before it would be adopted by commercial computer manufacturers.