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Google claims it is using A.I. to design chips faster than humans

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Google claims that it has developed artificial intelligence software that can design computer chips faster than humans can. The tech giant said in a paper in the journal Nature on Wednesday that a chip that would take humans months to design can be dreamed up by its new AI in less than six hours. The AI has already been used to develop the next iteration of Google's tensor processing unit chips, which are used to run AI-related tasks, Google said. "Our method has been used in production to design the next generation of Google TPU," wrote the authors of the paper, led by Google's co-heads of machine learning for systems, Azalia Mirhoseini and Anna Goldie. To put it another way, Google is using AI to design chips that can be used to create even more sophisticated AI systems.


What Google's AI-designed chip tells us about the nature of intelligence

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In a paper published in the peer-reviewed scientific journal Nature last week, scientists at Google Brain introduced a deep reinforcement learning technique for floorplanning, the process of arranging the placement of different components of computer chips. The researchers managed to use the reinforcement learning technique to design the next generation of Tensor Processing Units, Google's specialized artificial intelligence processors. The use of software in chip design is not new. But according to the Google researchers, the new reinforcement learning model "automatically generates chip floorplans that are superior or comparable to those produced by humans in all key metrics, including power consumption, performance and chip area." And it does it in a fraction of the time it would take a human to do so. The AI's superiority to human performance has drawn a lot of attention.


What Google's AI-designed chip tells us about the nature of intelligence

#artificialintelligence

This article is part of our reviews of AI research papers, a series of posts that explore the latest findings in artificial intelligence. In a paper published in the peer-reviewed scientific journal Nature last week, scientists at Google Brain introduced a deep reinforcement learning technique for floorplanning, the process of arranging the placement of different components of computer chips. The researchers managed to use the reinforcement learning technique to design the next generation of Tensor Processing Units, Google's specialized artificial intelligence processors. The use of software in chip design is not new. But according to the Google researchers, the new reinforcement learning model "automatically generates chip floorplans that are superior or comparable to those produced by humans in all key metrics, including power consumption, performance and chip area."


AI system outperforms humans in designing floorplans for microchips

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Success or failure in designing microchips depends heavily on steps known as floorplanning and placement. These steps determine where memory and logic elements are located on a chip. The locations, in turn, strongly affect whether the completed chip design can satisfy operational requirements such as processing speed and power efficiency. So far, the floorplanning task, in particular, has defied all attempts at automation. It is therefore performed iteratively and painstakingly, over weeks or months, by expert human engineers.


Google AI Designs Computer Chips Faster Than Humans Can

International Business Times

Search engine giant Google has recently claimed that it developed a new artificial intelligence (AI) software capable of designing microchips much faster than humans can. In a paper recently published in the journal Nature, the company claimed that it is utilizing machine learning in designing its next generation of Google tensor processing unit chips. According to Google, a work that takes months for humans to finish can now be accomplished by its AI in six hours or less. For years, the company has been working to figure out how machine learning can be used to design and create chips. However, this latest effort appears to be the first one utilized in developing a commercial product.