Re:Jeff Dean Responds to EDA Industry about AlphaChip
by xpe on Sunday December 01, 2024 @09:28AM
(#42288606)
> "scientific publication"
These air quotes suggests the commenter above doesn't think the paper qualifies a scientific publication. Such a characterization is unfair.
When I read the Nature article titled "Addendum: A graph placement methodology for fast chip design" [1], I see writing that more than meets the bar for a scientific publication. For example:
> Since publication, we have open-sourced a software repository [21] to fully reproduce the methods described in our paper. External researchers can use this repository to pre-train on a variety of chip blocks and then apply the pre-trained model to new blocks, as was done in our original paper. As part of this addendum, we are also releasing a model checkpoint pre-trained on 20 TPU blocks [22]. For best results, however, we continue to recommend that developers pre-train on their own in-distribution blocks [18], and provide a tutorial on how to perform pre-training with our open-source repository [23].
[18]: Yue, S. et al. Scalability and generalization of circuit training for chip floorplanning. In Proc. 2022 International Symposium on Physical Design 65–70 (2022).
[21]: Guadarrama, S. et al. Circuit Training: an open-source framework for generating chip floor plans with distributed deep reinforcement learning. GitHub https://github.com/google-research/circuit_training (2021).
These air quotes suggests the commenter above doesn't think the paper qualifies a scientific publication. Such a characterization is unfair.
When I read the Nature article titled "Addendum: A graph placement methodology for fast chip design" [1], I see writing that more than meets the bar for a scientific publication. For example:
> Since publication, we have open-sourced a software repository [21] to fully reproduce the methods described in our paper. External researchers can use this repository to pre-train on a variety of chip blocks and then apply the pre-trained model to new blocks, as was done in our original paper. As part of this addendum, we are also releasing a model checkpoint pre-trained on 20 TPU blocks [22]. For best results, however, we continue to recommend that developers pre-train on their own in-distribution blocks [18], and provide a tutorial on how to perform pre-training with our open-source repository [23].
[1]: https://www.nature.com/articles/s41586-024-08032-5
[18]: Yue, S. et al. Scalability and generalization of circuit training for chip floorplanning. In Proc. 2022 International Symposium on Physical Design 65–70 (2022).
[21]: Guadarrama, S. et al. Circuit Training: an open-source framework for generating chip floor plans with distributed deep reinforcement learning. GitHub https://github.com/google-research/circuit_training (2021).
[23]: Guadarrama, S. et al. Pre-training. GitHub https://github.com/google-research/circuit_training/blob/mai... (2021).