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Google Researchers Create AI-ception with an AI Chip That Speeds Up AI

Using a reinforcement-learning algorithm, the AI has learnt to optimize the placement of components on a computer chip.

Reinforcement learning algorithms may be the next best thing since sliced bread for engineers looking to improve chip placement. 

Researchers from Google have created a new algorithm that has learned how to optimize the placement of the components in a computer chip, so as to make it more efficient and less power-hungry. 

SEE ALSO: WILL AI AND GENERATIVE DESIGN STEAL OUR ENGINEERING JOBS?

Artificial Intelligence

Typically, engineers can spend up to 30 hours configuring a single floor plan of chip placement, or chip floor planning. This complicated 3D design problem requires the configuration of hundreds, or even thousands, of components across a number of layers in a constrained area. Engineers will manually design configurations to minimize the number of wires used between components as a proxy for efficiency. 

Because this is time-consuming, these chips are designed to only last between two and five years. However, as machine-learning algorithms keep improving year upon year, a need for new chip architectures has also arisen. 

Facing these challenges, Google researchers Anna Goldie and Azalia Mirhoseini, have looked into reinforcement learning. These types of algorithms use positive and negative feedback in order to learn new and complicated tasks. Thus, the algorithm is either "rewarded" or "punished" depending on how well it learns a task. Following this, it then creates tens to hundreds of thousands of new designs. Ultimately, it creates an optimal strategy on how to place these chip components.

After their tests, the researchers checked their designs with the electronic design automation software and discovered that their method's floor planning was much more effective than the ones human engineers designed. Moreover, the system was able to teach its human workers a new trick or two.

Progress in AI has been largely interlinked with progress is computer chip design. The researchers' hope is that their new algorithm will assist in speeding up the chip design process and pave the way for new and improved architectures, which would ultimately accelerate AI.

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