Reinforcement learning for chip design

Daniel and Chris have a fascinating discussion with Anna Goldie and Azalia Mirhoseini from Google Brain about the use of reinforcement learning for chip floor planning - or placement - in which many new designs are generated, and then evaluated, to find an optimal component layout. Anna and Azalia also describe the use of graph convolutional neural networks in their approach.

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Featuring:

• Anna Goldie – GitHub, LinkedIn, X
• Azalia Mirhoseini – LinkedIn, X
• Chris Benson – Website, GitHub, LinkedIn, X
• Daniel Whitenack – Website, GitHub, X

Show Notes:

• Their research paper
• Google Brain
• Google is using AI to design chips that will accelerate AI | MIT Technology Review
• Practical AI episode #47: GANs, RL, and transfer learning oh my!

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