4 ms·

In the conclusion of the article, you said: "While I concede that there are things the ISPD authors could have done better, their conclusion is still sound. The

In the conclusion of the article, you said: "While I concede that there are things the ISPD authors could have done better, their conclusion is still sound. The Nature authors do not address the fact that CMP and AutoDMP outperform CT with far less runtime and compute requirements." One key argument in the rebuttal against the ISPD article is that the resources used in their comparison were significantly smaller. To me, this point alone seems sufficient to question the validity of the ISPD work's conclusions. What are your thoughts on this? Additionally, I noticed that the neutral tone of this comment is quite a departure from the strongly critical tone of your article toward the AlphaChip work (words like "arrogance", "disdain", "hyperbole", "belittling", "hostile" for AlphaChip authors, as opposed to "excellent" for a Synopsys VP.) Could you share where this difference in tone originates?

vighneshiyer 2y ago
> One key argument in the rebuttal against the ISPD article is that the resources used in their comparison were significantly smaller. To me, this point alone seems sufficient to question the validity of the ISPD work's conclusions. What are your thoughts on this? I believe this is a fair criticism, and it could be a reason why the ISPD Tensorboard shows divergence during training for some RTL designs. The ISPD authors provide their own justification for their substitution of training time for compute resources in page 11 of their paper (https://arxiv.org/pdf/2302.11014 https://arxiv.org/pdf/2302.11014). I do not think it changes the ISPD work's conclusions however since they demonstrate that CMP and AutoDMP outperform CT wrt QoR and runtime even though they use much fewer compute resources. If more compute resources are used and CT becomes competitive wrt QoR, then it will still lag behind in runtime. Furthermore, Google has not produced evidence that AlphaChip, with their substantial compute resources, outperforms commercial placers (or even AutoDMP). In the recent rebuttal from Google (https://arxiv.org/pdf/2411.10053 https://arxiv.org/pdf/2411.10053), the only claim on page 8 says Google VLSI engineers preferred RL over humans and commercial placers on a blind study conducted in 2020. Commercial mixed placers, if configured correctly, have become very good over the past 4 years, so perhaps another blind study is warranted. > Additionally, I noticed that the neutral tone of this comment is quite a departure from the strongly critical tone of your article I will openly admit my bias is against the AlphaChip work. I referred to the Nature authors as 'arrogant' and 'disdainful' with respect to their statement that EDA CAD engineers are just being bitter ML-haters when they criticize the AlphaChip work. I referred to Jeff Dean as 'belittling' and 'hostile' and using 'hyperbole' with respect to his statements against Igor Markov, which I think is unbecoming of him. I referred to Shankar as 'excellent' with respect to his shrewd business acumen.
- make3 2y ago
  [flagged]
  - BoingBoomTschak 2y ago
    > EQ Using a fantasy concept invented by a science journalist doesn't help your posts, you know. Protip: it's just empathy + regular intelligence.
    - make3 2y ago
      yes you figured out what I meant good job
    - HeatrayEnjoyer 2y ago
      EQ is not a fantasy concept.
- nemonemo 2y ago
  Thank you for your thoughtful response. Acknowledging potential biases openly in a public forum is never easy, and in my view, it adds credibility to your words compared to leaving such matters as implicit insinuations. That said, on page 8, the paper says that 'standard licensing agreements with commercial vendors prohibit public comparison with their offerings.' Given this inherent limitation, what alternative approach could have been taken to enable a more meaningful comparison between CT and CMP?
  - vighneshiyer 2y ago
    So I'm not sure what Google is referring to here. As you can see in the ISPD paper (https://vlsicad.ucsd.edu/Publications/Conferences/396/c396.pdf https://vlsicad.ucsd.edu/Publications/Conferences/396/c396.p...) on page 5, they openly compare Cadence CMP with AutoDMP and other algorithims quantitatively. The only obfuscation is with the proprietary GF12 technology, where they can't provide absolute numbers, but only relative ones. Comparison against commercial tools is actually a common practice in academic EDA CAD papers, although usually the exact tool vendor is obfuscated. CAD tool vendors have actually gotten more permissive about sharing tool data and scripts in public over the past few years. However, PDKs have always been under NDAs and are still very restrictive. Perhaps the Cadence license agreement signed by a corporation is different than the one signed by a university. In such a case, they could partner with a university. But I doubt their license agreement prevents any public comparison. For example, see the AutoDMP paper from NVIDIA (https://d1qx31qr3h6wln.cloudfront.net/publications/AutoDMP.pdf https://d1qx31qr3h6wln.cloudfront.net/publications/AutoDMP.p...) where on page 7 they openly benchmark their tool against Cadence Innovus. My suspicion is they wish to keep details about the TPU blocks they evaluated under tight wraps.

vighneshiyer 2y ago

> One key argument in the rebuttal against the ISPD article is that the resources used in their comparison were significantly smaller. To me, this point alone seems sufficient to question the validity of the ISPD work's conclusions. What are your thoughts on this? I believe this is a fair criticism, and it could be a reason why the ISPD Tensorboard shows divergence during training for some RTL designs. The ISPD authors provide their own justification for their substitution of training time for compute resources in page 11 of their paper (https://arxiv.org/pdf/2302.11014 https://arxiv.org/pdf/2302.11014). I do not think it changes the ISPD work's conclusions however since they demonstrate that CMP and AutoDMP outperform CT wrt QoR and runtime even though they use much fewer compute resources. If more compute resources are used and CT becomes competitive wrt QoR, then it will still lag behind in runtime. Furthermore, Google has not produced evidence that AlphaChip, with their substantial compute resources, outperforms commercial placers (or even AutoDMP). In the recent rebuttal from Google (https://arxiv.org/pdf/2411.10053 https://arxiv.org/pdf/2411.10053), the only claim on page 8 says Google VLSI engineers preferred RL over humans and commercial placers on a blind study conducted in 2020. Commercial mixed placers, if configured correctly, have become very good over the past 4 years, so perhaps another blind study is warranted. > Additionally, I noticed that the neutral tone of this comment is quite a departure from the strongly critical tone of your article I will openly admit my bias is against the AlphaChip work. I referred to the Nature authors as 'arrogant' and 'disdainful' with respect to their statement that EDA CAD engineers are just being bitter ML-haters when they criticize the AlphaChip work. I referred to Jeff Dean as 'belittling' and 'hostile' and using 'hyperbole' with respect to his statements against Igor Markov, which I think is unbecoming of him. I referred to Shankar as 'excellent' with respect to his shrewd business acumen.

make3 2y ago
[flagged]
- BoingBoomTschak 2y ago
  > EQ Using a fantasy concept invented by a science journalist doesn't help your posts, you know. Protip: it's just empathy + regular intelligence.
  - make3 2y ago
    yes you figured out what I meant good job
  - HeatrayEnjoyer 2y ago
    EQ is not a fantasy concept.
nemonemo 2y ago
Thank you for your thoughtful response. Acknowledging potential biases openly in a public forum is never easy, and in my view, it adds credibility to your words compared to leaving such matters as implicit insinuations. That said, on page 8, the paper says that 'standard licensing agreements with commercial vendors prohibit public comparison with their offerings.' Given this inherent limitation, what alternative approach could have been taken to enable a more meaningful comparison between CT and CMP?
- vighneshiyer 2y ago
  So I'm not sure what Google is referring to here. As you can see in the ISPD paper (https://vlsicad.ucsd.edu/Publications/Conferences/396/c396.pdf https://vlsicad.ucsd.edu/Publications/Conferences/396/c396.p...) on page 5, they openly compare Cadence CMP with AutoDMP and other algorithims quantitatively. The only obfuscation is with the proprietary GF12 technology, where they can't provide absolute numbers, but only relative ones. Comparison against commercial tools is actually a common practice in academic EDA CAD papers, although usually the exact tool vendor is obfuscated. CAD tool vendors have actually gotten more permissive about sharing tool data and scripts in public over the past few years. However, PDKs have always been under NDAs and are still very restrictive. Perhaps the Cadence license agreement signed by a corporation is different than the one signed by a university. In such a case, they could partner with a university. But I doubt their license agreement prevents any public comparison. For example, see the AutoDMP paper from NVIDIA (https://d1qx31qr3h6wln.cloudfront.net/publications/AutoDMP.pdf https://d1qx31qr3h6wln.cloudfront.net/publications/AutoDMP.p...) where on page 7 they openly benchmark their tool against Cadence Innovus. My suspicion is they wish to keep details about the TPU blocks they evaluated under tight wraps.