Google says it has developed a way of using deep reinforcement learning (RL) to create computer chip floorplanning in just six hours -- a complicated feat that typically requires humans months to achieve. The chips Google's AI develops are on par or superior than those humans can create, the team explained in its paper published in the journal Nature on Wednesday, June 9. In a first for one of its commercial products, Google's research is being used for the company's upcoming tensor processing unit (TPU) chips, which are optimized for AI computation. So Google's AI method to design chips can eventually be used to improve and quicken the future development of AI. "Our method was used to design the next generation of Google's artificial intelligence (AI) accelerators, and has the potential to save thousands of hours of human effort for each new generation," the team said. The major breakthrough is that Google's AI method can be used for chip "floorplanning," which, as the paper said "Despite five decades of research, chip floorplanning has defied automation, requiring months of intense effort by physical design engineers to produce manufacturable layouts."

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."

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.

Google has developed an artificial intelligence that it says is capable of creating computer chips in'under six hours,' according to a new study. The research, published in Nature, notes that humans can take'months' to design specialized chips for its tensor processing units - a type of chip used in AI - but the reinforcement learning (RL) algorithm is better and faster than humans at creating more complex AI. 'The RL agent becomes better and faster at floorplanning optimization as it places a greater number of chip netlists,' the researchers wrote in the study. 'We show that our method can generate chip floorplans that are comparable or superior to human experts in under six hours, whereas humans take months to produce acceptable floorplans for modern accelerators.' Google developed an artificial intelligence that it says is capable of creating computer chips in'under six hours' The new process was used in Google's latest TPU chip, Anna Goldie, one of the study's co-authors said The chip floorplan is where parts such as CPUs, GPUs and memory have been placed on the silicon.

In a recent Google AI blog post, lead Jeff Dean, scientists at Google Research and the Google chip implementation and infrastructure team described an AI technology that can design computer chips in less than six hours. The team explained the process in a published paper where it talked about a learning-based approach to chip design that can learn from experience and improve over time, becoming better at generating architectures for unseen components. They claim that this technology can complete designing computer chips in under six hours on average, which is significantly faster than the weeks it takes human experts in the loop. According to the company, the new technology advances the state of the art in that it implies the placement of on-chip transistors can be largely automated. If made publicly available, the Google researchers' technique could enable cash-strapped startups to develop their chips for AI and other specialised purposes.

In a preprint paper coauthored by Google AI lead Jeff Dean, scientists at Google Research and the Google chip implementation and infrastructure team describe a learning-based approach to chip design that can learn from past experience and improve over time, becoming better at generating architectures for unseen components. They claim it completes designs in under six hours on average, which is significantly faster than the weeks it takes human experts in the loop. While the work isn't entirely novel -- it builds upon a technique proposed by Google engineers in a paper published in March -- it advances the state of the art in that it implies the placement of on-chip transistors can be largely automated. If made publicly available, the Google researchers' technique could enable cash-strapped startups to develop their own chips for AI and other specialized purposes. Moreover, it could help to shorten the chip design cycle to allow hardware to better adapt to rapidly evolving research.