MacroPlacement

found major flaws in [1], but “a commitment to open inquiry, intellectual rigor, integrity, and collaboration” must protect legitimate research, even if it is politically inconvenient. On the whole, I don't see how if the authors were academics with Tenure, they'd survive this one. This is entirely NOT how it's meant to work. Surely, it would be severely career limiting? [1] Azalia Mirhoseini, Anna Goldie, Mustafa Yaz- gan et al., “A Graph Placement Methodology for Fast Chip Design,” Nature 594 (2021), pp. 207-212. arXiv:2004.10746 [5] Sungmin Bae, Amir Yazdanbaksh, Satrajit Chatterjee, Mingyu Woo, Igor. L. Markov, et al., “Stronger Baselines for Evaluating Deep Reinforcement Learning in Chip Placement”, March, 2022.13 https://statmodeling.stat.columbia.edu/wp-content/uploads/20... [6] MacroPlacement Repo. https://github.com/TILOS-AI-Institute/MacroPlacement [7] Chung-Kuan Cheng, Andrew B. Kahng, Sayak Kundu, Yucheng Wang, Zhiang Wang, “As- sessment of Reinforcement Learning for Macro Placement”, ISPD 2023, arXiv:2302:11014

h diverse designs. Given that the Circuit Training approach requires 41x to 646x wall time and an even higher factor of compute time vs. other standard techniques, the 5x speedup with pre-training is insufficient to close that very large gap. Furthermore, pre-training does not improve results vs. training from scratch, unless you are pre-training on previous versions of the same design but even then a 1.7% improvement in the circuit training results is not adequate to demonstrate improvement vs. standard techniques as can be seen by examining the results from Professor Kahng's group's work. This concern about pre-training has been answered in multiple forums, including the discussion in ISPD'23 Session 8, when Mr. Washauer-Baker raised the same issue. It's unfortunate to see Gabriel Warshauer-Baker, Anna Goldie's spouse, making repeated personal attacks on Professor Kahng. Please focus on the scientific discussion.