@@ -168,6 +168,7 @@ We used Innovus version 21.1 since it was the latest version of our place-and-ro
- We have now run further experiments using older versions of CMP and Innovus. The macro placements produced by CMP across versions 19.1, 20.1 and 21.1 lead to the same qualitative conclusions. Details are given [here](./Docs/OurProgress#Question16).
**15. What are the outcomes of CT when the training is continued until convergence?**
To put this question in perspective, training “until convergence” is not described in any of the guidelines provided by the CT GitHub repo for reproducing the results in the Nature paper. For the [ISPD 2023 paper](https://vlsicad.ucsd.edu/Publications/Conferences/396/c396.pdf), we adhere to the guidelines given in the [CT GitHub repo](https://github.com/google-research/circuit_training/blob/main/docs/ARIANE.md#train-job), use the same number of iterations for Ariane as Google engineers demonstrate in the [CT GitHub repo](https://github.com/google-research/circuit_training/blob/main/docs/ARIANE.md#train-job), and obtain results that closely align with Google's outcomes for Ariane. (See FAQs #4 and #13.)
CT code **does not guarantee** convergence. This said, we have run CT training for an extended number (= 600, which is three times our default [value of 200](https://github.com/google-research/circuit_training/blob/main/docs/ARIANE.md#train-job)) of iterations, for each of Ariane, BlackParrot and MemPool Group, on NG45. For MemPool Group, CT diverges (tensorboard [link](https://tensorboard.dev/experiment/w4txHNhAReCOV77LqvqkgQ/#scalars)).
