Awesome AI for EDA

A curated paper list of existing Artificial Intelligence (AI) for

Electronic Design Automation (EDA) studies.


The list is under construction.

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1. Correlated Multi-Objective Multi-Fidelity Optimization for HLS Directives Design [paper]
Qi Sun, Tinghuan Chen, Siting Liu, Jianli Chen, Hao Yu, and Bei Yu
TODAES 2022
2. Machine Learning Based Routing Congestion Prediction in FPGA High-Level Synthesis [paper]
Jieru Zhao, Tingyuan Liang, Sharad Sinha, and Wei Zhang
DATE 2019
3. A Parallel Bandit-Based Approach for Autotuning FPGA Compilation [paper]
Chang Xu, Gai Liu, Ritchie Zhao, Stephen Yang, Guojie Luo, and Zhiru Zhang
FPGA 2017
4. On learning-based methods for design-space exploration with high-level synthesis [paper]
Hung-Yi Liu and Luca P Carloni
DAC 2013


Operator Sequence Scheduling

1. AlphaSyn: Logic Synthesis Optimization with Efficient Monte Carlo Tree Search [paper]
Zehua Pei, Fangzhou Liu, Zhuolun He, Guojin Chen, Haisheng Zheng, Keren Zhu, and Bei Yu
ICCAD 2023
2. Practical Multi-armed Bandits in Boolean Optimization [paper] [code] [talk]
Cunxi Yu
ICCAD 2020
3. DRiLLS: Deep Reinforcement Learning for Logic Synthesis [paper] [code]
Abdelrahman Hosny, Soheil Hashemi, Mohamed Shalan, and Sherief Reda
ASP-DAC 2020

1. Machine-Learning-Driven Architectural Selection of Adders and Multipliers in Logic Synthesis [paper]
Jiawen Cheng, Yong Xiao, Yun Shao, Guanghai Dong, Songlin Lyu, and Wenjian Yu
TODAES 2023

Synthesis Results Estimation

1. SNS's Not a Synthesizer: A Deep-Learning-Based Synthesis Predictor [paper] [code]
Ceyu Xu, Chris Kjellqvist, and Lisa Wu Wills
ISCA 2022
2. Bulls-Eye: Active Few-shot Learning Guided Logic Synthesis [paper]
Animesh Basak Chowdhury, Benjamin Tan, Ryan Carey, Tushit Jain, Ramesh Karri, and Siddharth Garg
TCAD 2022
3. Batch Sequential Black-Box Optimization with Embedding Alignment Cells for Logic Synthesis [paper]
Chang Feng, Wenlong Lyu, Zhitang Chen, Junjie Ye, Mingxuan Yuan, and Jianye Hao
ICCAD 2022
4. Decision making in synthesis cross technologies using LSTMs and transfer learning [paper] [talk]
Cunxi Yu and Wang Zhou
MLCAD 2020
5. Exploring Logic Optimizations with Reinforcement Learning and Graph Convolutional Network [paper]
Keren Zhu, Mingjie Liu, Hao Chen, Zheng Zhao, and David Z. Pan
MLCAD 2020
6. Developing synthesis flows without human knowledge [paper] [code] [slides]
Cunxi Yu, Houping Xiao, and Giovanni De Micheli
DAC 2018


1. Functionality matters in netlist representation learning [paper]
Ziyi Wang, Chen Bai, Zhuolun He, Guangliang Zhang, Qiang Xu, Tsung-Yi Ho, Bei Yu, and Yu Huang
DAC 2022
2. Graph Learning-Based Arithmetic Block Identification [paper]
Zhuolun He, Ziyi Wang, Chen Bail, Haoyu Yang, and Bei Yu
ICCAD 2021
3. Aadam: A Fast, Accurate, and Versatile Aging-Aware Cell Library Delay Model using Feed-Forward Neural Network [paper]
Seyed Milad Ebrahimipour, Behnam Ghavami, Hamid Mousavi, Mohsen Raji, Zhenman Fang, and Lesley Shannon
ICCAD 2020
4. Efficient Performance Trade-off Modeling for Analog Circuit based on Bayesian Neural Network [paper]
Zhengqi Gao, Jun Tao, Fan Yang, Yangfeng Su, Dian Zhou, and Xuan Zeng
ICCAD 2019

Reliability

1. Mixed-Type Wafer Failure Pattern Recognition [paper]
Hao Geng, Qi Sun, Tinghuan Chen, Qi Xu, Tsung-Yi Ho, and Bei Yu
ASP-DAC 2023
2. Deep H-GCN: Fast analog IC aging-induced degradation estimation [paper]
Tinghuan Chen, Qi Sun, Canhui Zhan, Changze Liu, Huatao Yu, and Bei Yu
TCAD 2021
3. Analog IC aging-induced degradation estimation via heterogeneous graph convolutional networks [paper]
Tinghuan Chen, Qi Sun, Canhui Zhan, Changze Liu, Huatao Yu, and Bei Yu
ASP-DAC 2021

Security

1. Rethink before Releasing Your Model: ML Model Extraction Attack in EDA [paper]
Chen-Chia Chang, Jingyu Pan, Zhiyao Xie, Jiang Hu, and Yiran Chen
ASP-DAC 2023

Parasitic Extraction

1. CNN-Cap: Effective Convolutional Neural Network-Based Capacitance Models for Interconnect Capacitance Extraction [paper]
Dingcheng Yang, Haoyuan Li, Wenjian Yu, Yuanbo Guo, and Wenjie Liang
TODAES 2023
2. CNN-Cap: Effective Convolutional Neural Network Based Capacitance Models for Full-Chip Parasitic Extraction [paper]
Dingcheng Yang, Wenjian Yu, Yuanbo Guo, and Wenjie Liang
ICCAD 2021

Circuit Simulation

1. Adaptive Stepping PTA for DC Analysis Based on Reinforcement Learning [paper]
Yichao Dong, Dan Niu, Zhou Jin, Chuan Zhang, Qi Li, and Changyin Sun
TCAS-II 2023
2. OSSP-PTA: An Online Stochastic Stepping Policy for PTA on Reinforcement Learning [paper]
Dan Niu, Yichao Dong, Zhou Jin, Chuan Zhang, Qi Li, and Changyin Sun
TCAD 2023
3. Accelerating Sparse LU Factorization with Density-aware Adaptive Matrix Multiplication for Circuit Simulation [paper]
Tengcheng Wang, Wenhao Li, Haojie Pei, Yuying Sun, Zhou Jin, and Weifeng Liu
DAC 2023
4. BoA-PTA: A Bayesian Optimization Accelerated PTA Solver for SPICE Simulation [paper]
Wei W. Xing, Xiang Jin, Tian Feng, Dan Niu, Weisheng Zhao, and Zhou Jin
TODAES 2022
5. Accelerating Nonlinear DC Circuit Simulation with Reinforcement Learning [paper]
Zhou Jin, Haojie Pei, Yichao Dong, Xiang Jin, Xiao Wu, Wei W. Xing, and Dan Niu
DAC 2022
6. Application of Deep Learning in Back-End Simulation: Challenges and Opportunities [paper]
Yufei Chen, Haojie Pei, Xiao Dong, Zhou Jin, and Cheng Zhuo
ASP-DAC 2022
7. Machine-Learning-Driven Matrix Ordering for Power Grid Analysis [paper]
Ganqu Cui, Wenjian Yu, Xin Li, Zhiyu Zeng, and Ben Gu
DATE 2019


1. SpecPart: A Supervised Spectral Framework for Hypergraph Partitioning Solution Improvement [paper]
Ismail Bustany, Andrew B. Kahng, Ioannis Koutis, Bodhisatta Pramanik, and Zhiang Wang
DAC 2022
2. LayouTransformer: Generating Layout Patterns with Transformer via Sequential Pattern Modeling [paper]
Liangjian Wen, Yi Zhu, Lei Ye, Guojin Chen, Bei Yu, Jianzhuang Liu, and Chunjing Xu
ICCAD 2022
3. Floorplanning with Graph Attention [paper]
Yiting Liu, Ziyi Ju, Zhengming Li, Mingzhi Dong, Hai Zhou, Jia Wang, Fan Yang, Xuan Zeng, and Li Shang
DAC 2022
4. GraphPlanner: Floorplanning with Graph Neural Network [paper]
Yiting Liu, Ziyi Ju, Zhengming Li, Mingzhi Dong, Hai Zhou, Jia Wang, Fan Yang, Xuan Zeng, and Li Shang
TODAES 2022
5. TP-GNN: A Graph Neural Network Framework for Tier Partitioning in Monolithic 3D ICs [paper]
Yi-Chen Lu, Sai Surya Kiran Pentapati, Lingjun Zhu, Kambiz Samadi, and Sung Kyu Lim
DAC 2020


1. PROS 2.0: A Plug-In for Routability Optimization and Routed Wirelength Estimation Using Deep Learning [paper]
Jingsong Chen, Jian Kuang, Guowei Zhao, Dennis J. -H. Huang, and Evangeline F. Y. Young
TCAD 2023
2. MacroRank: Ranking Macro Placement Solutions Leveraging Translation Equivariancy [paper]
Yifan Chen, Jing Mai, Xiaohan Gao, Muhan Zhang, and Yibo Lin
ASPDAC 2023
3. DREAM-GAN: Advancing DREAMPlace towards Commercial-Quality using Generative Adversarial Learning [paper]
Yi-Chen Lu, Haoxing Ren, Hao-Hsiang Hsiao, and Sung Kyu Lim
ISPD 2023
4. ChiPFormer: Transferable Chip Placement via Offline Decision Transformer [paper]
Yao Lai, Jinxin Liu, Zhentao Tang, Bin Wang, Jianye Hao, and Ping Luo
ICML 2023
5. AutoDMP: Automated DREAMPlace-based Macro Placement [paper] [code]
Anthony Agnesina, Puranjay Rajvanshi, Tian Yang, Geraldo Pradipta, Austin Jiao, Ben Keller, Brucek Khailany, and Haoxing Ren
ISPD 2023
6. Mitigating Distribution Shift for Congestion Optimization in Global Placement [paper]
Su Zheng, Lancheng Zou, Siting Liu, Yibo Lin, Bei Yu, and Martin Wong
DAC 2023
7. Lay-Net: Grafting Netlist Knowledge on Layout-Based Congestion Prediction [paper]
Su Zheng, Lancheng Zou, Peng Xu, Siting Liu, Bei Yu, and Martin Wong
ICCAD 2023
8. The Policy-gradient Placement and Generative Routing Neural Networks for Chip Design [paper]
Ruoyu Cheng, Xianglong Lyu, Yang Li, Junjie Ye, Jianye HAO, and Junchi Yan
NeurIPS 2022
9. MaskPlace: Fast Chip Placement via Reinforced Visual Representation Learning [paper]
Yao Lai, Yao Mu, and Ping Luo
NeurIPS 2022
10. LHNN: Lattice Hypergraph Neural Network for VLSI Congestion Prediction [paper]
Bowen Wang, Guibao Shen, Dong Li, Jianye Hao, Wulong Liu, Yu Huang, Hongzhong Wu, Yibo Lin, Guangyong Chen, and Pheng Ann Heng
DAC 2022
11. Pin Accessibility and Routing Congestion Aware DRC Hotspot Prediction Using Graph Neural Network and U-Net [paper]
Kyeonghyeon Baek, Hyunbum Park, Suwan Kim, Kyumyung Choi, and Taewhan Kim
ICCAD 2022
12. A Stochastic Approach to Handle Non-Determinism in Deep Learning-Based Design Rule Violation Predictions [paper]
Rongjian Liang, Hua Xiang, Jinwook Jung, Jiang Hu, and Gi-Joon Nam
ICCAD 2022
13. Global placement with deep learning-enabled explicit routability optimization [paper]
Siting Liu, Qi Sun, Peiyu Liao, Yibo Lin, and Bei Yu
DATE 2021
14. On Joint Learning for Solving Placement and Routing in Chip Design [paper]
Ruoyu Cheng and Junchi Yan
NeurIPS 2021
15. Automatic Routability Predictor Development Using Neural Architecture Search [paper]
Chen-Chia Chang, Jingyu Pan, Tunhou Zhang, Zhiyao Xie, Jiang Hu, Weiyi Qi, Chun-Wei Lin, Rongjian Liang, Joydeep Mitra, Elias Fallon, and Yiran Chen
ICCAD 2021
16. PROS: A Plug-in for Routability Optimization Applied in the State-of-the-art Commercial EDA Tool Using Deep Learning [paper]
Jingsong Chen, Jian Kuang, Guowei Zhao, Dennis J.-H. Huang, and Evangeline F.Y. Young
ICCAD 2021
17. Generalizable Cross-Graph Embedding for GNN-based Congestion Prediction [paper]
Amur Ghose, Vincent Zhang, Yingxue Zhang, Dong Li, Wulong Liu, and Mark Coates
ICCAD 2021
18. High-Definition Routing Congestion Prediction for Large-Scale FPGAs [paper]
Mohamed Baker Alawieh, Wuxi Li, Yibo Lin, Love Singhal, Mahesh A. Iyer, and David Z. Pan
ASP-DAC 2020
19. DRC Hotspot Prediction at Sub-10nm Process Nodes Using Customized Convolutional Network [paper]
Rongjian Liang, Hua Xiang, Diwesh Pandey, Lakshmi Reddy, Shyam Ramji, Gi-Joon Nam, and Jiang Hu
ISPD 2020
20. DREAMPlace: Deep Learning Toolkit-Enabled GPU Acceleration for Modern VLSI Placement [paper]
Yibo Lin, Shounak Dhar, Wuxi Li, Haoxing Ren, Brucek Khailany, and David Z. Pan
DAC 2019
21. Painting on placement: Forecasting routing congestion using conditional generative adversarial nets [paper]
Cunxi Yu and Zhiru Zhang
DAC 2019
22. Pin accessibility prediction and optimization with deep learning-based pin pattern recognition [paper]
Tao-Chun Yu, Shao-Yun Fang, Hsien-Shih Chiu, Kai-Shun Hu, Philip Hui-Yuh Tai, Cindy Chin-Fang Shen, and Henry Sheng
DAC 2019
23. RouteNet: Routability prediction for mixed-size designs using convolutional neural network [paper]
Zhiyao Xie, Yu-Hung Huang, Guan-Qi Fang, Haoxing Ren, Shao-Yun Fang, Yiran Chen, and Jiang Hu
ICCAD 2018
24. Routability Optimization for ndustrial Designs at Sub-14nm Process Nodes Using Machine Learning [paper]
Wei-Ting J. Chan, Pei-Hsin Ho, Andrew B. Kahng, and Prashant Saxena
ISPD 2017


1. BufFormer: A Generative ML Framework for Scalable Buffering [paper]
Rongjian Liang, Siddhartha Nath, Anand Rajaram, Jiang Hu, and Haoxing Ren
ASPDAC 2023
2. A Clock Tree Prediction and Optimization Framework Using Generative Adversarial Learning [paper]
Yi-Chen Lu, Jeehyun Lee, Anthony Agnesina, Kambiz Samadi, and Sung Kyu Lim
TCAD 2022
3. A Reinforced Learning Solution for Clock Skew Engineering to Reduce Peak Current and IR Drop [paper]
Sayed Aresh Beheshti-Shirazi, Ashkan Vakil, Sai Manoj P. D., Ioannis Savidis, Houman Homayoun, and Avesta Sasan
GLSVLSI 2021
4. Designing of an Optimization Technique for the Prediction of CTS Outcomes using Neural Network [paper]
Shuchi Nagaria and Sujay Deb
iSES 2020
5. GAN-CTS: A Generative Adversarial Framework for Clock Tree Prediction and Optimization [paper]
Yi-Chen Lu, Jeehyun Lee, Anthony Agnesina, Kambiz Samadi, and Sung Kyu Lim
ICCAD 2019


1. Concurrent Sign-off Timing Optimization via Deep Steiner Points Refinement [paper]
Siting Liu, Ziyi Wang, Fangzhou Liu, Yibo Lin, Bei Yu, and Martin Wong
DAC 2023
2. DPRoute: Deep Learning Framework for Package Routing [paper]
Yeu-Haw Yeh, Simon Yi-Hung Chen, Hung-Ming Chen, Deng-Yao Tu, Guan-Qi Fang, Yun-Chih Kuo, and Po-Yang Chen
ASPDAC 2023
3. Reinforcement Learning Guided Detailed Routing for FinFET Custom Circuits [paper]
Hao Chen, Kai-Chieh Hsu, Walker J. Turner, Po-Hsuan Wei, Keren Zhu, David Z. Pan, and Haoxing Ren
ISPD 2023
4. Towards Collaborative Intelligence: Routability Estimation Based on Decentralized Private Data [paper]
Jingyu Pan, Chen-Chia Chang, Zhiyao Xie, Ang Li, Minxue Tang, Tunhou Zhang, Jiang Hu, and Yiran Chen
DAC 2022
5. Asynchronous reinforcement learning framework for net order exploration in detailed routing [paper]
Tong Qu, Yibo Lin, Zongqing Lu, Yajuan Su, and Yayi Wei
DATE 2021
6. On Joint Learning for Solving Placement and Routing in Chip Design [paper]
Ruoyu Cheng and Junchi Yan
NeurIPS 2021
7. Late breaking results: A neural network that routes ics [paper]
Dmitry Utyamishev and Inna Partin-Vaisband
DAC 2020


1. Restructure-Tolerant Timing Prediction via Multimodal Fusion [paper]
Ziyi Wang, Siting Liu, Yuan Pu, Song Chen, Tsung-Yi Ho, and Bei Yu
DAC 2023
2. Graph-Learning-Driven Path-Based Timing Analysis Results Predictor from Graph-Based Timing Analysis [paper]
Yuyang Ye, Tinghuan Chen, Yifei Gao, Hao Yan, Bei Yu, and Longxing Shi
ASP-DAC 2023
3. Fast and Accurate Wire Timing Estimation Based on Graph Learning [paper]
Yuyang Ye, Tinghuan Chen, Yifei Gao, Hao Yan, Bei Yu, and Longxing Shi
DATE 2023
4. Estimating Code Vulnerability to Timing Errors Via Microarchitecture-Aware Machine Learning [paper]
Styliani Tompazi, Ioannis Tsiokanos, Jesus Martinez del Rincon, and Georgios Karakonstantis
IEEE Des Test 2023
5. Machine-Learning Based Delay Prediction for FPGA Technology Mapping [paper]
Hailiang Hu, Jiang Hu, Fan Zhang, Bing Tian, and Ismail Bustany
SLIP 2023
6. A timing engine inspired graph neural network model for pre-routing slack prediction [paper]
Zizheng Guo, Mingjie Liu, Jiaqi Gu, Shuhan Zhang, David Z Pan, and Yibo Lin
DAC 2022
7. DEVoT: Dynamic Delay Modeling of Functional Units Under Voltage and Temperature Variations [paper]
Dongning Ma, Xinqiao Zhang, Ke Huang, Yu Jiang, Wanli Chang, and Xun Jiao
TCAD 2022
8. Leveraging Machine Learning for Gate-Level Timing Estimation Using Current Source Models and Effective Capacitance [paper]
Dimitrios Garyfallou, Anastasis Vagenas, Charalampos Antoniadis, Yehia Massoud, and George Stamoulis
GLSVLSI 2022
9. ARETE: Accurate Error Assessment via Machine Learning-Guided Dynamic-Timing Analysis [paper]
Ioannis Tsiokanos, Styliani Tompazi, Giorgis Georgakoudis, Lev Mukhanov, and Georgios Karakonstantis
TC 2022
10. Generative Self-Supervised Learning for Gate Sizing: Invited [paper]
Siddhartha Nath, Geraldo Pradipta, Corey Hu, Tian Yang, Brucek Khailany, and Haoxing Ren
DAC 2022
11. Net2: A Graph Attention Network Method Customized for Pre-Placement Net Length Estimation [paper]
Zhiyao Xie, Rongjian Liang, Xiaoqing Xu, Jiang Hu, Yixiao Duan, and Yiran Chen
arXiv 2020
12. Routing-free crosstalk prediction [paper]
Rongjian Liang, Zhiyao Xie, Jinwook Jung, Vishnavi Chauha, Yiran Chen, Jiang Hu, Hua Xiang, and Gi-Joon Nam
ICCAD 2020
13. Fast and Accurate Wire Timing Estimation on Tree and Non-Tree Net Structures [paper]
Hsien-Han Cheng, Iris Hui-Ru Jiang, and Oscar Ou
DAC 2020
14. Accurate Operation Delay Prediction for FPGA HLS Using Graph Neural Networks [paper]
Ecenur Ustun, Chenhui Deng, Debjit Pal, Zhijing Li, and Zhiru Zhang
ICCAD 2020
15. TEVoT: Timing Error Modeling of Functional Units under Dynamic Voltage and Temperature Variations [paper]
Xun Jiao, Dongning Ma, Wanli Chang, and Yu Jiang
DAC 2020
16. DEFCON: Generating and Detecting Failure-prone Instruction Sequences via Stochastic Search [paper]
Ioannis Tsiokanos, Lev Mukhanov, Giorgis Georgakoudis, Dimitrios S. Nikolopoulos, and Georgios Karakonstantis
DATE 2020
17. Machine Learning-Based Pre-Routing Timing Prediction with Reduced Pessimism [paper]
Erick Carvajal Barboza, Nishchal Shukla, Yiran Chen, and Jiang Hu
DAC 2019
18. Accurate Wirelength Prediction for Placement-Aware Synthesis through Machine Learning [paper]
Daijoon Hyun, Yuepeng Fan, and Youngsoo Shin
DATE 2019
19. Using Machine Learning to Predict Path-Based Slack from Graph-Based Timing Analysis [paper]
Andrew B. Kahng, Uday Mallappa, and Lawrence Saul
ICCD 2018
20. CLIM: A Cross-Level Workload-Aware Timing Error Prediction Model for Functional Units [paper]
Xun Jiao, Abbas Rahimi, Yu Jiang, Jianguo Wang, Hamed Fatemi, Jose Pineda de Gyvez, and Rajesh K. Gupta
TC 2018
21. SI for free: machine learning of interconnect coupling delay and transition effects [paper]
Andrew B. Kahng, Mulong Luo, and Siddhartha Nath
SLIP 2015
22. A Deep Learning Methodology to Proliferate Golden Signoff Timing [paper]
Seung-Soo Han, Andrew B Kahng, Siddhartha Nath, Ashok S Vydyanathan, and ECE Departments
DATE 2014
23. Learning-based approximation of interconnect delay and slew in signoff timing tools [paper]
Andrew B. Kahng, Seokhyeong Kang, Hyein Lee, Siddhartha Nath, and Jyoti Wadhwani
SLIP 2013


1. Hotspot Detection via Attention-Based Deep Layout Metric Learning [paper]
Hao Geng, Haoyu Yang, Lu Zhang, Fan Yang, Xuan Zeng, and Bei Yu
TCAD 2022
2. Efficient Hotspot Detection via Graph Neural Network [paper]
Shuyuan Sun, Yiyang Jiang, Fan Yang, Bei Yu, and Xuan Zeng
DATE 2022
3. Low-Cost Lithography Hotspot Detection with Active Entropy Sampling and Model Calibration [paper]
Yifeng Xiao, Miaodi Su, Haoyu Yang, Jianli Chen, Jun Yu, and Bei Yu
DAC 2021
4. Efficient Layout Hotspot Detection via Binarized Residual Neural Network Ensemble [paper]
Yiyang Jiang, Fan Yang, Bei Yu, Dian Zhou, and Xuan Zeng
TCAD 2021
5. Bridging the Gap Between Layout Pattern Sampling and Hotspot Detection via Batch Active Learning [paper]
Haoyu Yang, Shuhe Li, Cyrus Tabery, Bingqing Lin, and Bei Yu
TCAD 2021
6. Hotspot Detection via Multi-task Learning and Transformer Encoder [paper]
Binwu Zhu, Ran Chen, Xinyun Zhang, Fan Yang, Xuan Zeng, Bei Yu, and Martin D.F. Wong
ICCAD 2021

Reliability

1. Efficient Learning Strategies for Machine Learning-Based Characterization of Aging-Aware Cell Libraries [paper]
Florian Klemme and Hussam Amrouch
TCSI 2022
2. Scalable Machine Learning to Estimate the Impact of Aging on Circuits Under Workload Dependency [paper]
Florian Klemme and Hussam Amrouch
TCSI 2022
3. Machine Learning for On-the-Fly Reliability-Aware Cell Library Characterization [paper]
Florian Klemme and Hussam Amrouch
TCSI 2021

Power

1. Learning Based Spatial Power Characterization and Full-Chip Power Estimation for Commercial TPUs [paper]
Jincong Lu, Jinwei Zhang, Wentian Jin, Sachin Sachdeva, and Sheldon X. -D. Tan
ASPDAC 2023
2. Microarchitecture Power Modeling via Artificial Neural Network and Transfer Learning [paper]
Jianwang Zhai, Yici Cai, and Bei Yu
ASPDAC 2023

Ir Drop

1. Decoupling Capacitor Insertion Minimizing IR-Drop Violations and Routing DRVs [paper]
Daijoon Hyun, Younggwang Jung, Insu Cho, and Youngsoo Shin
ASP-DAC 2023
2. Vector-Based Dynamic IR-drop Prediction Using Machine Learning [paper]
Jia-Xian Chen, Shi-Tang Liu, Yu-Tsung Wu, Mu-Ting Wu, Chieo-Mo Li, Norman Chang, Ying-Shiun Li, and Wen-Tze Chuang
ASP-DAC 2022
3. MAVIREC: ML-Aided Vectored IR-Drop Estimation and Classification [paper]
Vidya A. Chhabria, Yanqing Zhang, Haoxing Ren, Ben Keller, Brucek Khailany, and Sachin S. Sapatnekar
ASP-DAC 2021
4. Thermal and IR Drop Analysis Using Convolutional Encoder-Decoder Networks [paper]
Vidya A. Chhabria, Vipul Ahuja, Ashwath Prabhu, Nikhil Patil, Palkesh Jain, and Sachin S. Sapatnekar
ASP-DAC 2021
5. Dynamic IR-Drop ECO Optimization by Cell Movement with Current Waveform Staggering and Machine Learning Guidance [paper]
Xuan-Xue Huang, Hsien-Chia Chen, Sheng-Wei Wang, Iris Hui-Ru Jiang, Yih-Chih Chou, and Cheng-Hong Tsai
ICCAD 2020
6. XGBIR: An XGBoost-based IR Drop Predictor for Power Delivery Network [paper]
Chi-Hsien Pao, An-Yu Su, and Yu-Min Lee
DATE 2020
7. Fast IR Drop Estimation with Machine Learning [paper]
Zhiyao Xie, Hai Li, Xiaoqing Xu, Jiang Hu, and Yiran Chen
ICCAD 2020
8. PowerNet: Transferable Dynamic IR Drop Estimation via Maximum Convolutional Neural Network [paper]
Zhiyao Xie, Haoxing Ren, Brucek Khailany, Ye Sheng, Santosh Santosh, Jiang Hu, and Yiran Chen
ASP-DAC 2020
9. GridNet: Fast Data-Driven EM-induced IR Drop Prediction and Localized Fixing for on-Chip Power Grid Networks [paper]
Han Zhou, Wentian Jin, and Sheldon X.-D. Tan
ICCAD 2020
10. IncPIRD: Fast Learning-Based Prediction of Incremental IR Drop [paper]
Chia-Tung Ho and Andrew B. Kahng
ICCAD 2019
11. Machine-Learning-Based Dynamic IR Drop Prediction for ECO [paper]
Yen-Chun Fang, Heng-Yi Lin, Min-Yan Sui, Chien-Mo Li, and Eric Jia-Wei Fang
ICCAD 2018

Gate Sizing

1. Heterogeneous Graph Neural Network-Based Imitation Learning for Gate Sizing Acceleration [paper]
Xinyi Zhou, Junjie Ye, Chak-Wa Pui, Kun Shao, Guangliang Zhang, Bin Wang, Jianye Hao, Guangyong Chen, and Pheng-Ann Heng
ICCAD 2022
2. TransSizer: A Novel Transformer-Based Fast Gate Sizer [paper]
Siddhartha Nath, Geraldo Pradipta, Corey Hu, Tian Yang, Brucek Khailany, and Haoxing Ren
ICCAD 2022
3. Rl-sizer: Vlsi gate sizing for timing optimization using deep reinforcement learning [paper]
Yi-Chen Lu, Siddhartha Nath, Vishal Khandelwal, and Sung Kyu Lim
DAC 2021
4. GCN-RL circuit designer: Transferable transistor sizing with graph neural networks and reinforcement learning [paper]
Hanrui Wang, Kuan Wang, Jiacheng Yang, Linxiao Shen, Nan Sun, Hae-Seung Lee, and Song Han
DAC 2020


Lithography

1. Physics-Informed Optical Kernel Regression Using Complex-valued Neural Fields [paper]
Guojin Chen, Zehua Pei, Haoyu Yang, Yuzhe Ma, Bei Yu, and Martin Wong
DAC 2023
2. DeePEB: A Neural Partial Differential Equation Solver for Post Exposure Baking Simulation in Lithography [paper] [code]
Qipan Wang, Xiaohan Gao, Yibo Lin, Runsheng Wang, and Ru Huang
ICCAD 2022
3. Generic Lithography Modeling with Dual-Band Optics-Inspired Neural Networks [paper]
Haoyu Yang, Zongyi Li, Kumara Sastry, Saumyadip Mukhopadhyay, Mark Kilgard, Anima Anandkumar, Brucek Khailany, Vivek Singh, and Haoxing Ren
DAC 2022
4. Robustify ML-Based Lithography Hotspot Detectors [paper]
Jingyu Pan, Chen-Chia Chang, Zhiyao Xie, Jiang Hu, and Yiran Chen
ICCAD 2022

Mask Optimization

1. Enabling Scalable AI Computational Lithography with Physics-Inspired Models [paper]
Haoyu Yang and Haoxing Ren
ASPDAC 2023
2. Data-Driven Approaches for Process Simulation and Optical Proximity Correction [paper]
Hao-Chiang Shao, Chia-Wen Lin, and Shao-Yun Fang
ASPDAC 2023
3. AdaOPC: A Self-Adaptive Mask Optimization Framework For Real Design Patterns [paper]
Wenqian Zhao, Xufeng Yao, Ziyang Yu, Guojin Chen, Yuzhe Ma, Bei Yu, and Martin Wong
ICCAD 2022
4. DevelSet: Deep Neural Level Set for Instant Mask optimization [paper]
Guojin Chen, Ziyang Yu, Hongduo Liu, Yuzhe Ma, and Bei Yu
ICCAD 2021
5. DAMO: Deep Agile Mask Optimization for Full Chip Scale [paper]
Guojin Chen, Wanli Chen, Yuzhe Ma, Haoyu Yang, and Bei Yu
ICCAD 2020
6. GAN-OPC: Mask Optimization with Lithography-Guided Generative Adversarial Nets [paper]
Haoyu Yang, Shuhe Li, Yuzhe Ma, Bei Yu, and Evangeline F. Y. Young
DAC 2018

Layout Generation

1. DiffPattern: Layout Pattern Generation via Discrete Diffusion [paper]
Zixiao Wang, Yunheng Shen, Wenqian Zhao, Yang Bai, Guojin Chen, Farzan Farnia, and Bei Yu
DAC 2023


1. AnalogCoder: Analog Circuit Design via Training-Free Code Generation [paper]
Yao Lai, Sungyoung Lee, Guojin Chen, Souradip Poddar, Mengkang Hu, David Z Pan, and Ping Luo
arXiv 2024
2. TAG: Learning Circuit Spatial Embedding from Layouts [paper]
Keren Zhu, Hao Chen, Walker J. Turner, George F. Kokai, Po-Hsuan Wei, David Z. Pan, and Haoxing Ren
ICCAD 2022
3. Are Analytical Techniques Worthwhile for Analog IC Placement? [paper]
Yishuang Lin, Yaguang Li, Donghao Fang, Meghna Madhusudan, Sachin S. Sapatnekar, Ramesh Harjani, and Jiang Hu
DATE 2022
4. Universal Symmetry Constraint Extraction for Analog and Mixed-Signal Circuits with Graph Neural Networks [paper]
Hao Chen, Keren Zhu, Mingjie Liu, Xiyuan Tang, Nan Sun, and David Z. Pan
DAC 2021
5. Towards Decrypting the Art of Analog Layout: Placement Quality Prediction via Transfer Learning [paper]
Mingjie Liu, Keren Zhu, Jiaqi Gu, Linxiao Shen, Xiyuan Tang, Nan Sun, and David Z. Pan
DATE 2020
6. WellGAN: Generative-Adversarial-Network-Guided Well Generation for Analog/Mixed-Signal Circuit Layout [paper]
Biying Xu, Yibo Lin, Xiyuan Tang, Shaolan Li, Linxiao Shen, Nan Sun, and David Z. Pan
DAC 2019

Analog Layout Routing

1. TRouter: Thermal-driven PCB Routing via Non-Local Crisscross Attention Networks [paper]
Tinghuan Chen, Silu Xiong, Huan He, and Bei Yu
TCAD 2023
2. GeniusRoute: A new analog routing paradigm using generative neural network guidance [paper]
Keren Zhu, Mingjie Liu, Yibo Lin, Biying Xu, Shaolan Li, Xiyuan Tang, Nan Sun, and David Z Pan
ICCAD 2019

Electromigration Analysis

1. HierPINN-EM: Fast Learning-Based Electromigration Analysis for Multi-Segment Interconnects Using Hierarchical Physics-Informed Neural Network [paper]
Wentian Jin, Liang Chen, Subed Lamichhane, Mohammadamir Kavousi, and Sheldon X-D Tan
ICCAD 2022
2. A Space-Time Neural Network for Analysis of Stress Evolution Under DC Current Stressing [paper]
Tianshu Hou, Ngai Wong, Quan Chen, Zhigang Ji, and Hai-Bao Chen
TCAD 2022

Analog Layout Synthesis

1. MTL-Designer: An Integrated Flow for Analysis and Synthesis of Microstrip Transmission Line [paper]
Qipan Wang, Ping Liu, Liguo Jiang, Mingjie Liu, Yibo Lin, Runsheng Wang, and Ru Huang
DAC 2023
2. Closing the Design Loop: Bayesian Optimization Assisted Hierarchical Analog Layout Synthesis [paper] [code] [slides]
Mingjie Liu, Keren Zhu, Xiyuan Tang, Biying Xu, Wei Shi, Nan Sun, and David Z. Pan
DAC 2019

Analog Simulation

1. A Deep Learning Approach for Efficient Electromagnetic Analysis of On-Chip Inductor with Dummy Metal Fillings [paper]
Xiangliang Li, Yijie Tang, Peng Zhao, Shichang Chen, Kuiwen Xu, and Gaofeng Wang
Electronics 2022
2. Applications of physics-informed neural networks in power systems-a review [paper]
Bin Huang and Jianhui Wang
Transactions on Power Systems 2022

Analog Layout Placement

1. Fully Automated Machine Learning Model Development for Analog Placement Quality Prediction [paper]
Chen-Chia Chang, Jingyu Pan, Zhiyao Xie, Yaguang Li, Yishuang Lin, Jiang Hu, and Yiran Chen
ASPDAC 2023
2. APOSTLE: Asynchronously Parallel Optimization for Sizing Analog Transistors Using DNN Learning [paper]
Ahmet Faruk Budak, David Smart, Brian Swahn, and David Z. Pan
ASPDAC 2023
3. Generative-Adversarial-Network-Guided Well-Aware Placement for Analog Circuits [paper]
Keren Zhu, Hao Chen, Mingjie Liu, Xiyuan Tang, Wei Shi, Nan Sun, and David Z. Pan
ASP-DAC 2022
4. Layout Symmetry Annotation for Analog Circuits with Graph Neural Networks [paper]
Xiaohan Gao, Chenhui Deng, Mingjie Liu, Zhiru Zhang, David Z. Pan, and Yibo Lin
ASPDAC 2021
5. A Customized Graph Neural Network Model for Guiding Analog IC Placement [paper]
Yaguang Li, Yishuang Lin, Meghna Madhusudan, Arvind Sharma, Wenbin Xu, Sachin S. Sapatnekar, Ramesh Harjani, and Jiang Hu
ICCAD 2020

Thermal Simulation

1. A Thermal Machine Learning Solver For Chip Simulation [paper]
Rishikesh Ranade, Haiyang He, Jay Pathak, Norman Chang, Akhilesh Kumar, and Jimin Wen
ML4CAD 2022
2. Fast Thermal Analysis for Chiplet Design based on Graph Convolution Networks [paper]
Liang Chen, Wentian Jin, and Sheldon X-D Tan
ASP-DAC 2022


1. DeepTPI: Test Point Insertion with Deep Reinforcement Learning [paper]
Zhengyuan Shi, Min Li, Sadaf Khan, Liuzheng Wang, Naixing Wang, Yu Huang, and Qiang Xu
ITC 2022
2. Neural Fault Analysis for SAT-based ATPG [paper]
Junhua Huang, Hui-Ling Zhen, Naixing Wang, Hui Mao, Mingxuan Yuan, and Yu Huang
ITC 2022
3. High Performance Graph Convolutional Networks with Applications in Testability Analysis [paper]
Yuzhe Ma, Haoxing Ren, Brucek Khailany, Harbinder Sikka, Lijuan Luo, Karthikeyan Natarajan, and Bei Yu
DAC 2019


1. CircuitNet: An Open-Source Dataset for Machine Learning Applications in Electronic Design Automation (EDA) [paper] [code]
Zhuomin Chai, Yuxiang Zhao, Yibo Lin, Wei Liu, Runsheng Wang, and Ru Huang
SCIENCE CHINA Information Sciences 2022


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cited as

@article{chen2023ai4eda,
  title   = {Awesome AI for EDA: A curated paper list of Artificial Intelligence for Electronic Design Automation studies.},
  author  = {Chen, Guojin and Mai, Jing and Lin, Yibo and Yu, Bei},
  journal = {ai4eda.github.io},
  year    = {2023},
  url     = {https://ai4eda.github.io/}
}

Last updated on 2024-06-19 .