Venue: NeurIPS 2022 Conference

Reviews:

final_score: rating: 4: Borderline reject: Technically solid paper where reasons to reject, e.g., limited evaluation, outweigh reasons to accept, e.g., good evaluation. Please use sparingly.
review_content: summary: The paper proposes an RL-based model for mixed-size macro placement. The standard cells re placed via gradient-based GPU acceleration and a one-shot conditional generative routing model is devised to perform one-shot routing to the pins within each net. The paper is the first joint placement and routing network without involving any traditional heuristic solver.  
strengths_and_weaknesses: Strengths:
- Applying generative models for placement and routing is an interesting topic. Even though, generative models have been successfully applied to various domains, however, this work is novel because no generative models have been devised and applied to the pin routing problem before. 

Weaknesses:
- The paper makes a false claim that this paper is the first one to address placement and routing end-to-end. But there is a prior work jointly optimizing placement and routing in NeurIPS 2021 [1]. Please correct the statement and add the citation in the related work. 

- The paper looks like an extension of an existing work combing placement and routing in an end-to-end fashion. The real novelty is in its generative model devised for the routing problem. This could lead to the bad logical order of the paper, where routing comes first while placement comes last. The EM algorithm is section 3.3 is almost a one sentence summary. If the EM algorithm is also part of the originality of this paper, the authors of this paper should provide more details and formulation for section 3.3. Also, logically, you should consider moving section 3.3 earlier. 

- There is a very limited set of comparisons to baselines. Only DeepPlace is compared against the proposed method. Table 1 wire length results look even worse than DeepPlace. Please explain why a worse wire length results would justify publishing the paper?

[1] "On Joint Learning for Solving Placement and Routing in Chip Design", https://papers.nips.cc/paper/2021/file/898aef0932f6aaecda27aba8e9903991-Paper.pdf
questions: The reviewer feels this paper is only half-baked, not being sufficient for a publication. I would suggest improving several items before a resubmission:

1. Address clearly which part is novel in this paper and which part is just an extension of an existing work. Add the joint placement and routing work in your related work and compare with their work properly. 

2. Improve the evaluation section by first, improving the wire length and density results to SoTA baselines like DeepPlace and Graph Placer [2]. 

3. Ablation on the decomposition of gains and help readers understand what part of the design contributes most to the improved wire length and density. For example, try to show the generative routing actually improves the performance compared to a more general approach using deep RL. 

[2] https://arxiv.org/abs/2004.10746
limitations: Yes.
review_title: null

final_score: rating: 6: Weak Accept: Technically solid, moderate-to-high impact paper, with no major concerns with respect to evaluation, resources, reproducibility, ethical considerations.
review_content: summary: The authors propose a pipeline of chip placement and routing based on machine learning methods. The pipeline consists of the following parts.
1. An enhanced RL method for macro placement
2. A conditional generative network for routing
3. A net order learning method
strengths_and_weaknesses: Strengths
1. The paper is well organized and developed.
2. The method and experiments are reasonable and sound.

Weaknesses
1. There are too many hyperparameters in the proposed methods. It may be difficult for others to employ this method. What is the robustness of the method? How sensitive is the method if we modify these hyperparameters?
2. The results in Table 1 are mixed. Namely, DeepPlace and the proposed method both present Pareto optimal results. It is better to show that the proposed method can achieve lower wirelength and smaller overlap at the same time.
3. In Lines 108, and 109, the authors mention that "the pure neural architecture of our model implies our model has the potential to enjoy higher capacity by using a larger model for further improvement". This claim should be discussed in detail and verified in experiments.
questions: 1. The authors claim that "the method is the first joint placement and routing network without involving any traditional heuristic solver" in the abstract. What are traditional heuristic solvers? I think there still exist heuristic methods in optimization-based standard cell placement (e.g., DREAMPlace), legalization, and detailed placement. The claim may be too strong. Moreover, I think the overall enhanced objective shown in Equation (3) still belongs to heuristic methods since it lacks of strict proof and does not guarantee solution quality.
2. Table 4 only shows the results of 4 benchmarks. I do not find other results in appendix. Could the authors conduct experiments on all the benchmarks and report the results in the main body or appendix?
3. Similarly, Table 3 only shows the results of 2 benchmarks. What about other benchmarks? If the authors do not report them, readers may think that the results are not ideal.
4. The proposed routing method is "one-shot". I am confused by this term. Could the authors explain why they use this term?
limitations: The authors discuss the limitation and the social impact in A.2 and A.3, which is a great discussion.
review_title: null

final_score: rating: 8: Strong Accept: Technically strong paper, with novel ideas, excellent impact on at least one area, or high-to-excellent impact on multiple areas, with excellent evaluation, resources, and reproducibility, and no unaddressed ethical considerations.
review_content: summary: This paper proposes a pure neural pipeline which is the first (to my knowledge) joint placement and routing network in literature without involving traditional heuristic solvers. Specially, a conditional generative routing network is utilized to perform routing in one shot and placement is performed by a policy gradient based RL method for macros while considering their sizes. To tackle the net order issue during routing, a net order agent is proposed to decide which net to be routed next. Experimental results on both placement and routing benchmarks show that the proposed joint networks outperform traditional separate pipeline, as well as those network with heuristic solvers.
strengths_and_weaknesses: Strengths:
+ This is an interesting work at the intersection between machine learning and EDA. I applaud the authors’ courage and efforts in this direction. The paper is clearly motivated and well written with informative background and technical description thus I believe both communities can learn from this work and this is a promising area worth for further efforts. 
+ The most impressive novelty lies in the achievement of the first pure neural pipeline for placement and routing owing to a critical design of generative routing. This is contrast to the previous work [Cheng and Yan NeurIPS’21] that adopts traditional routing solvers in network.
+ There are additional techniques proposed in this paper, including handling the mixed-size macro and adaptive ordering of the net routing. The combination of these additional techniques makes the proposed network more effective.
+ Experimental results are relatively comprehensive and well delivered. The performance is convincing on the public benchmark ISPD 2005.

Weaknesses:
- Despite the shown better performance in terms of wirelength, the proposed generative routing model has much longer runtime compared with classical heuristic solvers. I can image and understand that the classic heuristic solvers can be very fast by tailored and industry-level optimized C/C++ codes. The authors shall better discuss or provide more results on this weakness.

Overall this paper has obvious merits regarding with novelty, and a weakness in terms of the routing speed. I am personally tolerant to the weakness because 1) deep learning for placement and routing is still in its infancy and there is still much space to improve the efficiency especially considering the classic routing algorithms are often carefully optimized using C/C++ at industry level; 2) generative models may be a promising direction (in one shot) for incorporating machine learning to the routing problem as the current idea of using reinforcement learning for routing can be even more unaffordable. Hence, I vote for accepting this paper. 
questions: 1. Why is the runtime of generative router notably slower than classical routers? Is there any way to improve the efficiency of the generative router?
limitations: The limitation is about the speed for generative routing (see my question part).
review_title: null

final_score: rating: 6: Weak Accept: Technically solid, moderate-to-high impact paper, with no major concerns with respect to evaluation, resources, reproducibility, ethical considerations.
review_content: summary: This paper proposed a neural network-based pipeline for placement and routing of macros and standard cells in chip design. For placement, this paper proposed an RL-based model for learning mixed-size macro placement to reduce frequent collisions in previous methods. For routing, this paper proposed a conditional generative routing network with adaptive routing order to improve previous methods with fixed routing order. Experiments show that the proposed approach has lower overlap area, smaller wire-length and lower routing congestion than previous methods.
strengths_and_weaknesses: Strengths:
+ The chip design problem is an important and challenging one.
+ The proposed approach is technically sound and brings performance improvement over previous approaches.

Weaknesses:
- The main novelty of the proposed approach is at the routing aspect. The extension of placement module over DeepPlace is very subtle by adopting a different state definition, and it's not clear how such minor extension leads to better performance observed in experiments.
- Most evaluations of the proposed approach are divided into placement module and routing module as if two separate approaches are proposed. As the main targeted contribution is a joint design, I suggest to perform more evaluations of the proposed approach as a whole to compare with the best combination of existing placers and routers.
- Error bars are not added for Table 1, Table 3 and Table 4.
questions: 1. Why incorporating macro size into the state can bring a significant reduction of overlapping areas? 
2. Does the fine grained state information in placer requires higher training costs?
3. Due to the combinatorial nature of this problem, what is the scalability of the proposed approach? 
4. An estimate of the maximal number of circuit components can be tackled in a reasonable amount of time?
limitations: Yes.
review_title: null

Meta-review: metareview: This paper proposes a neural network pipeline for chip design that utilizes reinforcement learning for mixed-size macro placement and a conditional generative routing network that can perform routing in one shot. While the reviewers had some reservations regarding baselines, sensitivity to hyperparameters, and the resulting wirelength compared to DeepPlace, these have largely been addressed in the rebuttals and discussion. It was generally felt that this approach is quite novel and effective. It would be good to put the extra results in the final draft.
confidence: Less certain
Decision: Accept