Chip floorplanning is the engineering task of designing the physical layout of a computer chip. Despite five decades of research1, chip floorplanning has defied automation, requiring months of intense effort by physical design engineers to produce manufacturable layouts. Here we present a deep reinforcement learning approach to chip floorplanning. In under six hours, our method automatically generates chip floorplans that are superior or comparable to those produced by humans in all key metrics, including power consumption, performance and chip area. To achieve this, we pose chip floorplanning as a reinforcement learning problem, and develop an edge-based graph convolutional neural network architecture capable of learning rich and transferable representations of the chip. As a result, our method utilizes past experience to become better and faster at solving new instances of the problem, allowing chip design to be performed by artificial agents with more experience than any human designer. Our method was used to design the next generation of Google’s artificial intelligence (AI) accelerators, and has the potential to save thousands of hours of human effort for each new generation. Finally, we believe that more powerful AI-designed hardware will fuel advances in AI, creating a symbiotic relationship between the two fields.
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The data supporting the findings of this study are available within the paper and the Extended Data.
The code used to generate these data is available in the following GitHub repository: https://github.com/google-research/circuit_training.
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This project was a collaboration between Google Brain and the Google Chip Implementation and Infrastructure (CI2) Team. We thank M. Bellemare, C. Young, E. Chi, C. Stratakos, S. Roy, A. Yazdanbakhsh, N. Myung-Chul Kim, S. Agarwal, B. Li, S. Bae, A. Babu, M. Abadi, A. Salek, S. Bengio and D. Patterson for their help and support.
The following US patents are related to this work: ‘Generating integrated circuit floorplans using neural networks’ (granted as US10699043) and ‘Domain adaptive reinforcement learning approach to macro placement’ (filed).
Peer review information Nature thanks Jakob Foerster and the other, anonymous, reviewer(s) for their contribution to the peer review of this work.
Publisher’s note Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.
Extended data figures and tables
We allow each method access to the same clustered netlist hypergraph. We use the same hyperparameters (to the extent possible) in all the methods. Once the placement is completed by each method (this includes the legalization step for RePlAce), we snap the macros to the power grids, freeze the macro locations and use a commercial EDA tool to place the standard cells and report the final results.
Extended Data Fig. 2 Zero-shot performance of Edge-GNN versus GCN (graph convolutional neural network)77.
The agent with an Edge-GNN architecture is more robust to over-fitting and yields higher-quality results, as measured by average zero-shot performance on the test blocks shown in Extended Data Fig. 1.
We pre-train the policy network on three different training datasets (the small dataset with 2 blocks is a subset of the medium one with 5 blocks, and the medium dataset is a subset of the large one with 20 blocks). For each policy, at various snapshots during pre-training we report its inference performance on an unseen test block. As the dataset size increases, both the quality of generated placements on the test block and the generalization performance of the policy improve. The policy trained on the largest dataset is most robust to over-fitting.
Left, zero-shot placements from the pre-trained policy; right, placements from the fine-tuned policy. The zero-shot placements are generated at inference time on a previously unseen chip. The pre-trained policy network (with no fine-tuning) reserves a convex hull in the centre of the canvas in which standard cells can be placed, a behaviour that reduces wirelength and that emerges only after many hours of fine-tuning in the policy trained from scratch.
Human expert placements are shown on the left and results from our approach are shown on the right. The white area represents macros and the green area represents standard cells. The figures are intentionally blurred because the designs are proprietary. The wirelength for the human expert design is 57.07 m, whereas ours is 55.42 m. Furthermore, our method achieves these results in 6 h, whereas the manual baseline took several weeks.
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Mirhoseini, A., Goldie, A., Yazgan, M. et al. A graph placement methodology for fast chip design. Nature 594, 207–212 (2021). https://doi.org/10.1038/s41586-021-03544-w
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