---
title: "AlphaChip (controversy)"
chunk: 2/3
source: "https://en.wikipedia.org/wiki/AlphaChip_(controversy)"
category: "reference"
tags: "science, encyclopedia"
date_saved: "2026-05-05T03:15:15.897368+00:00"
instance: "kb-cron"
---

=== Internal dispute at Google and legal proceedings ===
In 2022, Satrajit Chatterjee, a Google engineer involved in reviewing the AlphaChip work, raised concerns internally and drafted an alternative analysis, (Stronger Baselines) arguing that established methods outperformed the RL approach under fair comparison. In March 2022, Google declined to publish this analysis and terminated Chatterjee's employment.
Chatterjee filed a wrongful dismissal lawsuit, alleging that representations related to the AlphaChip research involved fraud and scientific misconduct.  According to court documents, Chatterjee's study was conducted "in the context of a large potential Google Cloud deal".  He noted that it "would have been unethical to imply that we had revolutionary technology when our tests showed otherwise" and claimed Google was deliberately withholding material information. Furthermore, the committee that reviewed his paper and disapproved its publication was allegedly chaired by subordinates of Jeff Dean, a senior co-author of the Nature paper. Google’s subsequent motion to dismiss was denied, holding that Chatterjee had plausibly alleged retaliation for refusing to engage in conduct he believed would violate state or federal law.

=== External controversy ===
The external questions can be summarized in four main points: (a) Are the claims supported by the evidence provided?  (b) Did the paper provide enough information to allow the results to be independently reproduced and verified?  If so, are the results an improvement over existing academic and commercial tools?  (c) Were the comparisons in the paper done fairly and with full disclosure? (d) Were academic standards followed? Each of these is discussed below.

==== Are the claims supported by the evidence provided? ====
The Nature paper described the reduction in design-process time as going from "days or weeks" to "hours", but did not provide per-design time breakdowns or specify the number of engineers, their level of expertise, or the baseline tools and workflow against which this comparison was made. It was also unclear whether the "days or weeks" baseline included time spent on other tasks such as functional design changes.  The paper also evaluated the method on fewer benchmarks (five) than is common in the field, and showed mixed results across different evaluation goals
While the approach was described as improving circuit area, this claim seems unsupported, as the RL optimization did not alter the overall circuit area, as it adjusted only the locations of fixed-shape non-overlapping circuit components within a fixed rectangular layout boundary.

==== Comparison with existing methods, and replicating the algorithm ====
Because macro placement is largely geometric and its fundamental algorithms are not tied to a specific process node, competing approaches can be evaluated on public benchmarks (tests) across technologies, rather than primarily on proprietary internal designs. This is standard procedure when comparing academic placers, see .
In contrast, Google has only reported results only on internal proprietary designs, and as of 2026 has not offered comparisons with prior methods on common benchmarks.
Researchers at the University of California, San Diego (UC San Diego), led by professors Chung-Kuan Cheng and Andrew B. Kahng, have re-implemented the AlphaChip algorithm, working from the description in the paper and the released source code.  In 2023, they placed a wide variety of public domain designs using five different placers: their AlphaChip replicate, classic simulated annealing (as described in Stronger Baselines), a leading academic placer (RePlace), a commercial placer (CMP from Cadence), and human placement.  In these results, the AlphaChip algorithm did not outperform existing techniques.  AlphaChip raised numerous objections to the this comparison, and Kahng et. al. in turn replied.
After taking the objections into account, they re-did the placements, fully routed them (to avoid any reliance on proxy objectives), and measured the resulting wire length.  A portion of their extensive comparisons is shown here; in no cases did the AlphaChip replicate give a shorter wire length than the existing commercial placer.

They conclude that the reinforcement-learning approach described in the Nature publication did not consistently outperform established placement methods and typically required significantly greater computational effort.

==== Fair comparisons in computational optimization ====
The main argument here is that the reported runtime and quality comparisons between the reinforcement learning (RL) method and prior placement tools did not assess equivalent tasks under comparable conditions.
The claimed six-hour runtime bound per circuit example did not account for pre-training. In the described experiments, RL policies were trained on twenty circuit blocks and then evaluated on five additional blocks, but the reported runtime reflected only the evaluation phase. The evaluation reported in the paper relied on computing resources that were larger than those used by other tools.

==== Academic integrity ====
In October 2024, sixteen methodological concerns were grouped into categories and itemized as "initial doubts" in a detailed critique by chip design researcher and former University of Michigan professor Igor L. Markov in Communications of the ACM, from an arXiv preprint in 2023. The critique described multiple questionable research practices in the evaluation of AlphaChip, particularly around selective reporting of benchmarks and outcomes (cherry-picking), selective use of metrics, and selective choice of baselines.  As of 2026, this paper was prefaced with an ACM "EXPRESSION OF CONCERN: An investigation is underway regarding the content and transparency of disclosure for this article."