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!

Something missing or broken? PRs welcome!

Creators & Guests

Host

Chris Benson

Host

Daniel Whitenack

Guest

Anna Goldie

Guest

Azalia Mirhoseini

What is Practical AI?

Making artificial intelligence practical, productive & accessible to everyone. Practical AI is a show in which technology professionals, business people, students, enthusiasts, and expert guests engage in lively discussions about Artificial Intelligence and related topics (Machine Learning, Deep Learning, Neural Networks, GANs, MLOps, AIOps, LLMs & more).

The focus is on productive implementations and real-world scenarios that are accessible to everyone. If you want to keep up with the latest advances in AI, while keeping one foot in the real world, then this is the show for you!