There has been unfounded skepticism in the EDA community about whether our AlphaChip method works as claimed in our Nature paper.
@annadgoldie,
@Azaliamirh, and I wrote a technical response highlighting these issues:
That Chip Has Sailed: A Critique of Unfounded Skepticism Around AI for Chip Design
arxiv.org/abs/2411.10053
Much of this unfounded skepticism is driven by a deeply flawed non-peer-reviewed publication by Cheng et al. (
arxiv.org/abs/2302.11014) that claimed to replicate our approach but failed to follow our methodology in major ways. In particular the authors did no pre-training (despite pre-training being mentioned 37 times in our Nature article), robbing our learning-based method of its ability to learn from other chip designs, then used 20X less compute and did not train to convergence, preventing our method from fully learning even on the chip design being placed. By analogy, this would be like evaluating a version of AlphaGo that had never seen a game of Go before (instead of being pre-trained on millions of games), and then concluding that AlphaGo is not very good at Go.
We also respond to Igor Markov’s “meta-analysis” published in the Nov 2024 issue of CACM. In Markov’s paper (published without disclosing that Markov is a high-level employee at Synopsys, which makes commercial software that competes with our open-source release of AlphaChip), Markov “meta-analyzes” the flawed Cheng et al. paper and another unpublished anonymous PDF (
statmodeling.stat.columbia.e…) with no listed authors on which Markov is a shadow co-author (effectively regurgitating his own unpublished arguments as if they were independent). The Markov article makes veiled accusations, all completely baseless and already found to be without merit by Nature. I am surprised
@Synopsys wants to be associated with this, and I am surprised
@CACMmag saw fit to publish these sorts of allegations with no evidence, nor any technical data other than two flawed, non-peer reviewed articles.
Read
@annadgoldie's post below for more info. ⬇️