**MacroPlacement** is an open, transparent effort to provide a public, baseline implementation of [Google Brain's Circuit Training](https://github.com/google-research/circuit_training)(Morpheus) deep RL-based placement method. We will provide (1) testcases in open enablements, along with multiple EDA tool flows; (2) implementations of missing or binarized elements of Circuit Training; (3) reproducible example macro placement solutions produced by our implementation; and (4) post-routing results obtained by full completion of the synthesis-place-and-route flow using both proprietary and open-source tools.
## **Our Latest Progress**
-In [Our Progress: A Chronology](https://tilos-ai-institute.github.io/MacroPlacement/Docs/OurProgress/) documentation you can find the latest updates.
-The [Proxy Cost](https://tilos-ai-institute.github.io/MacroPlacement/Docs/ProxyCost/) documentation contains implementation details of wirelength, density and congestion cost that [Circuit Trainig](https://github.com/google-research/circuit_training) is using.
-[Our Progress: A Chronology](https://tilos-ai-institute.github.io/MacroPlacement/Docs/OurProgress/) provides latest updates and is periodically synched to [this Google Doc](https://docs.google.com/document/d/1HHZNcid5CZvvRqj_njzF7hBhtNSpmRn3fCYniWNYBiY/edit).
-Our [Proxy Cost](https://tilos-ai-institute.github.io/MacroPlacement/Docs/ProxyCost/) documentation gives implementation details to enable reproduction of the wirelength, density and congestion costs used by [Circuit Training](https://github.com/google-research/circuit_training).
## **Table of Contents**
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