google-research/circuit_training

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1706 stars · 273 forks observed · 2026-08-28

What it is AI-extracted, prompt v1, taxonomy v1, 2026-08-30, confidence not recorded

AlphaChip (circuit_training) is Google's open-source framework for generating chip floorplans using distributed deep reinforcement learning, reproducing the Nature 2021 graph placement methodology. It is built on TF-Agents and TensorFlow 2.x, supporting distributed training across multiple GPUs and hundreds of data-collection actors.

Use cases

• generate chip floorplans with deep reinforcement learning
• place netlists with hundreds of macros and millions of standard cells
• optimize wirelength, congestion, and density in chip layouts
• reproduce the AlphaChip Nature 2021 paper methodology
• run distributed RL training across multiple GPUs for placement
• research AI methods for chip design and EDA

When to choose

• you need RL-based macro placement and floorplanning for chip design
• you want to reproduce or extend the AlphaChip methodology from research
• you need distributed training across many GPUs and actors for placement
• you work in EDA research exploring AI for chip layout

When to avoid

• you need a general-purpose EDA flow or full place-and-route tool
• your netlists are not in clustered format or lack macro/stdcell preparation
• you need a lightweight CPU-only solution without TensorFlow
• you want a turnkey commercial chip design tool rather than a research framework

Facets

library · maturity active

machine-learning reinforcement-learning simulation machine-learning hardware gpu-computing python chip-design floorplanning alphachip eda distributed-training tf-agents reinforcement-learning linux gpu

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markdown · JSON · MCP: product_card(name="google-research/circuit_training")