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a meta-analysis of three papers: our peer-reviewed Nature paper, the non-peer-reviewed ISPD paper, and Markov's own unpublished paper (though he does not disclose that he co-authored it).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.

翻译：2020年，我们提出了一种能够生成超人类芯片布局的深度强化学习方法，相关成果随后发表于《自然》期刊并在GitHub上开源。AlphaChip激发了AI应用于芯片设计领域的研究热潮，已在Alphabet旗下先进芯片中实现部署，并被外部芯片制造商扩展应用。尽管如此，ISPD 2023会议上一篇未经同行评审的特邀论文质疑了其性能声明，但该论文并未按照《自然》论文所述方法运行我们的系统。例如：未对强化学习方法进行预训练（剥夺其从先验经验学习的能力）、使用显著减少的计算资源（强化学习经验收集器数量减少20倍且GPU数量减半）、未训练至收敛状态（违反机器学习标准实践）、以及采用无法代表现代芯片的测试案例进行评估。近期，Igor Markov发表了一篇涵盖三篇论文的元分析：我们经过同行评审的《自然》论文、未经评审的ISPD论文以及Markov本人未公开的论文（尽管他未披露其合著者身份）。尽管AlphaChip已获得广泛采用并产生重要影响，我们仍发布此回应，以确保无人因误解而放弃在这一重要领域的创新探索。