Artificial intelligence designs chips better than humans

Artificial intelligence designs chips better than humans

Google experts have developed a series of algorithms and taught them to perceive the design of the chip as a puzzle game in which the parts are the main components of the future chip. The aim of the game is to achieve a certain threshold of quality and efficiency of the finished scheme. The evaluation of the achieved indicators was performed on the basis of a set of 10 thousand ready-made chip designs, which the researchers gave to the algorithms in the form of training material before the start of the experiment.

Researchers at Google have found that artificial intelligence (AI) is capable of developing full-fledged chips and could potentially do so more efficiently than humans. The process of designing a physical layout of a computer chip takes a long time. To improve efficiency, researchers from Google Research have used machine learning technologies, ZDNet reported. While chip design can take months for humans, AI algorithms handle this task in just six hours. At the same time, ready-made schemes turn out to be similar, and in some cases even more effective, than man-made ones.

“Our method generates a physical model of the chip in about six hours. Such tasks usually require months of expert work, ”said researchers from Google Research. According to them, the method not only shortens the development time, but also improves the quality of the final result, as the algorithms allow more accurate solution of problems related to finding the desired location and the combination of components that make up the chip.

Experts from Google Research also note that the method of designing chips with artificial intelligence has already been applied in practice in the development of the latest generation of Google’s tensor processor. Last year, the Internet company announced that it was experimenting with AI in developing its own processors. Artificial Intelligence chief Jeff Dean noted that such an approach would reduce development costs while helping to create more efficient microchip designs. “We commissioned our engineers to experiment with this method and we want to see how they can adapt it to their work processes,” says Dean.

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