回应对AI芯片设计性能质疑，重申AlphaChip真实效能与广泛应用。
That Chip Has Sailed: A Critique of Unfounded Skepticism Around AI for Chip Design
- 复现实验中未按原方法预训练，资源仅1/20且未收敛，导致结果不可比。
- 原始论文在Nature发表后已获谷歌等公司部署，广泛验证其实际价值。
- 适合关注AI赋能芯片设计的工程师与研究者，警惕不实质疑误导创新。
2020年，我们提出一种深度强化学习方法，可生成超越人类水平的芯片布局，并在《自然》发表且开源至GitHub。AlphaChip激发了大量关于AI芯片设计的研究，已被谷歌旗下多个先进芯片采用，并被外部芯片厂商扩展应用。然而，2023年ISPD上一篇非同行评审的邀请论文质疑其性能，却未按《自然》原文复现方法——未进行预训练、计算资源减少20倍、仅使用一半GPU、未训练至收敛，且测试用例不具现代芯片代表性。近期，Igor Markov对三篇论文（包括我们的《自然》论文、ISPD论文及他本人未公开合著的论文）进行了元分析。尽管AlphaChip已有广泛部署与影响，我们仍发布此回应，以防止任何人因错误质疑而放弃该关键领域的创新。
原文摘要 · Abstract (English)
In 2020, we introduced a deep reinforcement learning method capable of generating superhuman chip layouts, which we then published in Nature and open-sourced on GitHub. AlphaChip has inspired an explosion of work on AI for chip design, and has been deployed in state-of-the-art chips across Alphabet and extended by external chipmakers. Even so, a non-peer-reviewed invited paper at ISPD 2023 questioned its performance claims, despite failing to run our method as described in Nature. For example, it did not pre-train the RL method (removing its ability to learn from prior experience), used substantially fewer compute resources (20x fewer RL experience collectors and half as many GPUs), did not train to convergence (standard practice in machine learning), and evaluated on test cases that are not representative of modern chips. Recently, Igor Markov published a meta-analysis of three papers: our peer-reviewed Nature paper, the non-peer-reviewed ISPD paper, and Markov's own unpublished paper (though he does not disclose that he co-authored it). Although AlphaChip has already achieved widespread adoption and impact, we publish this response to ensure that no one is wrongly discouraged from innovating in this impactful area.
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