SlowGuess/NeurIPS_2023_OCR huggingface dataset:
Table 1: Congestion prediction result of CircuitNet dataset
Method	pearson	spearman	kendall
Gpdl [24]	0.5032	0.5143	0.3787
Gpdl with LinkNet [6]	0.5078	0.4905	0.3623
Gpdl with MANet [12]	0.4856	0.5167	0.3797
Gpdl with DeepLabv3 [7]	0.5091	0.5211	0.3834
Gpdl with Deeplabv3plus [8]	0.5153	0.5160	0.3807
Gpdl with UNet [32]	0.5770	0.5103	0.3780
Gpdl with UNet++ [46]	0.6085	0.5202	0.3855
CircuitGNN [40]	0.3287	0.4483	0.3688
CircuitFormer (ours)	0.6374	0.5282	0.3935
is provided by the developers of the CircuitNet dataset. However, the format they provide is not easily compatible with CircuitGNN, especially when dealing with large-scale designs. Consequently, a significant amount of time is required for data conversion in such cases. Therefore, when comparing with CircuitGNN, we excluded the superblue design from ISPD2015, which contains over one million nodes. The experimental results are presented in Table 3, similarly, we also showcase the performance of CNN-based models.
Table 2: Congestion prediction result of ISPD2015 dataset
Method	pearson	spearman	kendall
Gpdl [24]	0.4345	0.1297	0.0978
Gpdl with UNet [32]	0.4173	0.1430	0.1084
Gpdl with UNet++ [46]	0.3877	0.1575	0.1189
CircuitFormer (ours)	0.4456	0.1797	0.1360
Table 3: Congestion prediction result of ISPD2015 dataset without superblue
Method	pearson	spearman	kendall
Gpdl [24]	0.5202	0.1880	0.1423
Gpdl with UNet [32]	0.5253	0.1481	0.1124
Gpdl with UNet++ [46]	0.4593	0.1862	0.1423
CircuitGNN [40]	0.3940	0.1912	0.1614
CircuitFormer (ours)	0.6534	0.2244	0.1710
4.4 Result of DRC violation prediction
Table 4 shows that in the DRC violation prediction task, our work achieved the best performance in terms of Spearman and Kendall, but the Pearson correlation coefficient is relatively low. We speculate that this is because hand-crafted methods use the congestion map as an input feature, which contains higher-level information than the raw data. In contrast, our method only uses the original positional information as input to maintain consistency.
We conducted a series of ablation experiments to demonstrate the roles of different components in our model.
Comparison with other point cloud perception methods. Table 5 shows a comparison between our method and other point cloud perception methods, where we replace our encoder with them. We compared point-based methods, such as Point-Transformer [43] and Msg-Transformer [13], and voxel-based (or grid-based for 2D circuit) methods, such as PointPillars [22] and DSVT [37]. The results show that the voxel-based method outperforms the point-based method in both tasks, which
(a) Input
(b) UNet++
(c) CircuitFormer (ours)
Figure 3: Visualization of congestion maps for CircuitNet dataset
(d) Ground-truth
Table 4: DRC violation prediction result of CircuitNet dataset
Method	pearson	spearman	kendall
RouteNet [38]	0.4206	0.2462	0.1990
RouteNet with LinkNet [6]	0.4165	0.2213	0.1788
RouteNet with MANet [12]	0.3420	0.2395	0.1916
RouteNet with DeepLabv3 [7]	0.3760	0.2480	0.1989
RouteNet with Deeplabv3plus [8]	0.4224	0.2699	0.2185
RouteNet with UNet [32]	0.4415	0.2746	0.2225
RouteNet with UNet++ [46]	0.4698	0.2782	0.2261
CircuitGNN [40]	0.1171	0.2366	0.2095
CircuitFormer (ours)	0.3707	0.3094	0.2471
may be due to the fact that the voxel-based method directly partitions the circuit design into grids, which is consistent with the labeling modality, whereas the point-based method needs to project the point-wise information to the grid-wise prediction, which may result in information loss. Additionally, the point-based method requires point sampling and cannot utilize information from all points. Our method also outperforms PointPillars, which groups points within the same grid and extracts features using MLP and Maxpooling like PointNet [30]. Similar to RUDY, this method can only extract local features within the grid and cannot exchange features across grids. It highlights the importance of information exchange across grids.
Table 5: Comparison with other point cloud perception methods
Method	congestion	DRC violation
pearson	spearman	kendall	pearson	spearman	kendall
Msg-Transformer	0.3634	0.4033	0.2930	0.1216	0.2155	0.1686
Point-Transformer	0.4086	0.4599	0.2930	0.1307	0.2186	0.1721
PointPillar	0.6151	0.5234	0.3887	0.3617	0.3067	0.2447
DSVT	0.6035	0.5121	0.3802	0.2380	0.3213	0.2557
CircuitFormer (ours)	0.6374	0.5282	0.3935	0.3707	0.3094	0.2471
Label distribution smooth. Table 6 investigated the improvement brought by the label distribution smooth module on imbalanced prediction tasks.
Preprocessing time. In Fig. 4 we show the relationship between the preprocessing time of various methods and the size of the circuit design. GNN-based methods, represented by CircuitGNN, require generating a computation graph based on the geometric and topological information of the circuit design during the preprocessing stage. The time of this process is proportional to the size of the circuit design. CNN-based methods require hand-crafted methods to extract geometric information, which involves traversing the position of each node and pin. This process is proportional to the number of
Table 6: Results on the influence of label distribution smoothing
Method	Prediction Task	pearson	spearman	kendall
CircuitFormer (w/o. LDS)	congestion	0.6360	0.5143	0.3833
CircuitFormer	congestion	0.6374	0.5282	0.3935
CircuitFormer (w/o. LDS)	DRC violation	0.3552	0.2944	0.2354
CircuitFormer	DRC violation	0.3707	0.3094	0.2471
nodes and pins. In contrast, our method requires no preprocessing and takes the raw position of each node as input directly. The time tests were conducted on AMD EPYC 7502P 32-Core 2.5GHz CPU.
Figure 4: The relationship between preprocessing time and edge number.
Balancing model performance and runtime memory. To delve deeper into the relationship between the performance of our model and its runtime memory, and to provide more options to the community, we conducted ablation experiments on the encoder of our model. As shown in Fig. 1, our encoder consists of four stages of GA modules. We tested the performance and runtime memory of models with different numbers of stages. Specifically, we test runtime memory on RISCY-FPU-a, which is the largest-scale design in the CircuitNet dataset and consists of 77,707 nodes. The results are summarized in Table 7.
Table 7: The performance of models with different numbers of stages on the CircuitNet congestion prediction task, and their runtime memory during the processing of RISCY-FPU-a, which consists of 77,707 nodes.
Method	pearson	spearman	kendall	Runtime Memory/MB
CircuitFormer (4-stage)	0.6374	0.5282	0.3935	2475
CircuitFormer (3-stage)	0.5793	0.4977	0.3683	1519
CircuitFormer (2-stage)	0.5322	0.4972	0.3668	1047
CircuitFormer (1-stage)	0.5260	0.4853	0.3582	811
CircuitGNN [40]	0.3287	0.4483	0.3688	1501
Gpdl [24]	0.5032	0.5143	0.3787	1887
Gpdl with UNet++ [46]	0.6085	0.5202	0.3855	2209
Decoder. We conducted ablation experiments on the decoder selection to explore its impact on the model's performance.
Table 8: The ablation experiments on the decoder of congestion prediction task
Decoder	pearson	spearman	kendall	Lantency/ms
UNet++	0.3574	0.4622	0.3407	26.21
ResNet-18 and UNet++	0.6374	0.5282	0.3935	32.02
ResNet-34 and UNet++	0.6082	0.5260	0.3911	34.71
ResNet-50 and UNet++	0.6406	0.5243	0.3887	36.64
ResNet-18 and UNet	0.5237	0.5050	0.3731	29.90
ResNet-18 and DeepLabv3plus	0.5278	0.5222	0.3878	28.43
ResNet-18 and LinkNet	0.5080	0.4816	0.3553	29.34
We present a new perspective on circuit design, treating circuit components as point clouds. Based on this, we propose an improved multi-scale grid-based attention module based on VoxelSet [16], which achieves SOTA results on CircuitNet and ISPD2015 datasets. Furthermore, our work can serve as a general-purpose feature extractor for chip design and can be adapted to a wider range of downstream tasks. This is the first work to transform circuit design evaluation into a point cloud perception task, presenting a novel approach to addressing the issue. It has established a bridge between the relatively mature point cloud perception methods and the still developing EDA methods, enabling us to apply methods and conclusions that have been proven effective in point cloud perception tasks to EDA tasks.
However, it should be noted that although our approach can model the relationship between points through the attention mechanism, it still does not use the prior knowledge of topology directly. Graphormer [41] encodes the topological relationships between nodes into the attention matrix, which enables the fusion of topological and geometric information in the transformer. How to incorporate the prior topological information into our model will be our future research direction.
Last but not least, the methods we use are simple and basic ones, while the GNN-based or CNN-based methods we compare against have been iterated in EDA for several years. However, thanks to the active Transformer community, there are many more advanced methods available. For example, LongNet [10] and RetNet [34] not only reduce the computational complexity to linear but also improve accuracy. With the rise of Large Language Model (LLM), the use of Transformers for handling long sequential data (corresponding to large-scale circuit designs in the EDA field) has become a trend. There has been a wealth of research dedicated to improving the performance of Transformers while reducing computational complexity. We hope to bring this trend into the EDA domain through our work, leveraging the advancements in Transformers to enhance circuit design tasks.
Acknowledgement: This project was supported by National Natural Science Foundation of China (NSFC No. 62276108).
[1] Mohamed Baker Alawieh, Wuxi Li, Yibo Lin, Love Singhal, Mahesh A Iyer, and David Z Pan. High-definition routing congestion prediction for large-scale fpgas. In 2020 25th Asia and South Pacific Design Automation Conference (ASP-DAC), pages 26-31. IEEE, 2020.
[2] Srinadh Bhojanapalli, Chulhee Yun, Ankit Singh Rawat, Sashank Reddi, and Sanjiv Kumar. Low-rank bottleneck in multi-head attention models. In International Conference on Machine Learning, pages 864-873. PMLR, 2020.
[3] Mateusz Buda, Atsuto Maki, and Maciej A Mazurowski. A systematic study of the class imbalance problem in convolutional neural networks. Neural networks, 106:249-259, 2018.
[4] Ismail S Bustany, David Chinnery, Joseph R Shinnerl, and Vladimir Yutsis. Ispid 2015 benchmarks with fence regions and routing blockages for detailed-routing-driven placement. In Proceedings of the 2015 Symposium on International Symposium on Physical Design, pages 157-164, 2015.
[5] Zhuomin Chai, Yuxiang Zhao, Wei Liu, Yibo Lin, Runsheng Wang, and Ru Huang. Circuitnet: An open-source dataset for machine learning in vlsi cad applications with improved domain-specific evaluation
[43] Hengshuang Zhao, Li Jiang, Jiaya Jia, Philip HS Torr, and Vladlen Koltun. Point transformer. In Proceedings of the IEEE/CVF international conference on computer vision, pages 16259-16268, 2021.
[44] Yin Zhou and Oncel Tuzel. Voxelnet: End-to-end learning for point cloud based 3d object detection. In Proceedings of the IEEE conference on computer vision and pattern recognition, pages 4490-4499, 2018.
[45] Zhonghua Zhou, Ziran Zhu, Jianli Chen, Yuzhe Ma, Bei Yu, Tsung-Yi Ho, Guy Lemieux, and Andre Ivanov. Congestion-aware global routing using deep convolutional generative adversarial networks. In 2019 ACM/IEEE 1st Workshop on Machine Learning for CAD (MLCAD), pages 1-6. IEEE, 2019.
[46] Zongwei Zhou, Md Mahfuzur Rahman Siddiquee, Nima Tajbakhsh, and Jianming Liang. Unet++: A nested u-net architecture for medical image segmentation. In Deep Learning in Medical Image Analysis and Multimodal Learning for Clinical Decision Support: 4th International Workshop, DLMIA 2018, and 8th International Workshop, ML-CDS 2018, Held in Conjunction with MICCAI 2018, Granada, Spain, September 20, 2018, Proceedings 4, pages 3-11. Springer, 2018.