Abstract is missing.
- MLCAD Today and Tomorrow: Learning, Optimization and ScalingAndrew B. Kahng. 1 [doi]
- An Adaptive Analytic FPGA Placement Framework based on Deep-LearningAbeer Y. Al-Hyari, Ahmed Shamli, Timothy Martin, Shawki Areibi, Gary Gréwal. 3-8 [doi]
- Design Rule Checking with a CNN Based Feature ExtractorLuis Francisco, Tanmay Lagare, Arpit Jain, Somal Chaudhary, Madhura Kulkarni, Divya Sardana, W. Rhett Davis, Paul D. Franzon. 9-14 [doi]
- Using DNNs and Smart Sampling for Coverage Closure AccelerationRaviv Gal, Eldad Haber, Avi Ziv. 15-20 [doi]
- R2AD: Randomization and Reconstructor-based Adversarial Defense on Deep Neural NetworkMarzieh Ashrafiamiri, Sai Manoj Pudukotai Dinakarrao, Amir Hosein Afandizadeh Zargari, Minjun Seo, Fadi J. Kurdahi, Houman Homayoun. 21-26 [doi]
- DAVE: Deriving Automatically Verilog from EnglishHammond Pearce, Benjamin Tan, Ramesh Karri. 27-32 [doi]
- Accelerating Chip Design with Machine LearningBrucek Khailany. 33 [doi]
- SoC Design Automation with ML - It's Time for ResearchVijay Deep Bhatt, Wolfgang Ecker, Volkan Esen, Zhao Han, Daniela Sanchez Lopera, Rituj Patel, Lorenzo Servadei, Sahil Singla 0003, Sven Wenzek, Vijaydeep Yadav, Elena Zennaro. 35-36 [doi]
- Cost Optimization at Early Stages of Design Using Deep Reinforcement LearningLorenzo Servadei, Jiapeng Zheng, Jose A. Arjona-Medina, Michael Werner, Volkan Esen, Sepp Hochreiter, Wolfgang Ecker, Robert Wille. 37-42 [doi]
- F-LEMMA: Fast Learning-based Energy Management for Multi-/Many-core ProcessorsAn Zou, Karthik Garimella, Benjamin Lee, Christopher D. Gill, Xuan Zhang 0001. 43-48 [doi]
- CALT: Classification with Adaptive Labeling Thresholds for Analog Circuit SizingZhengfeng Wu, Ioannis Savidis. 49-54 [doi]
- Decision Making in Synthesis cross Technologies using LSTMs and Transfer LearningCunxi Yu, Wang Zhou. 55-60 [doi]
- Application of Quantum Machine Learning to VLSI PlacementIsaac Turtletaub, George Li, Mohannad Ibrahim, Paul Franzon. 61-66 [doi]
- From Tuning to Learning: Why the FPGA Physical Design Flow Offers a Compelling Case for ML?Ismail S. Bustany. 67 [doi]
- Data-driven CAD or Algorithm-Driven CAD: Competitors or Collaborators?Rajeev Jain, Pankaj Kukkal. 69 [doi]
- Data-Driven Fast Electrostatics and TDDB Aging AnalysisShaoyi Peng, Wentian Jin, Liang Chen, Sheldon X.-D. Tan. 71-76 [doi]
- HAT-DRL: Hotspot-Aware Task Mapping for Lifetime Improvement of Multicore System using Deep Reinforcement LearningJinwei Zhang, Sheriff Sadiqbatcha, Yuanqi Gao, Michael O'Dea, Nanpeng Yu, Sheldon X.-D. Tan. 77-82 [doi]
- Can Wear-Aware Memory Allocation be Intelligent?Christian Hakert, Kuan-Hsun Chen, Jian-Jia Chen. 83-88 [doi]
- An Enhanced Machine Learning Model for Adaptive Monte Carlo Yield AnalysisRichard Kimmel, Tong Li, David Winston. 89-94 [doi]
- Towards NN-based Online Estimation of the Full-Chip Temperature and the Rate of Temperature ChangeMartin Rapp, Omar Elfatairy, Marilyn Wolf, Jörg Henkel, Hussam Amrouch. 95-100 [doi]
- Design Challenges on Post Moore's Law EraPak Hei Matthew Leung. 101 [doi]
- Machine Learning in EDA: Opportunities and ChallengesElias Fallon. 103 [doi]
- Track-Assignment Detailed Routing Using Attention-based Policy Model With SupervisionHaiguang Liao, Qingyi Dong, Weiyi Qi, Elias Fallon, Levent Burak Kara. 105-110 [doi]
- Compact Models for Initial MOSFET Sizing Based on Higher-order Artificial Neural NetworksHusni M. Habal, Dobroslav Tsonev, Matthias Schweikardt. 111-116 [doi]
- An Efficient and Flexible Learning Framework for Dynamic Power and Thermal Co-ManagementYuan Cao, Tianhao Shen, Li Zhang, Xunzhao Yin, Cheng Zhuo. 117-122 [doi]
- Partial Sharing Neural Networks for Multi-Target Regression on Power and Performance of Embedded MemoriesFelix Last, Ulf Schlichtmann. 123-128 [doi]
- Explaining and Interpreting Machine Learning CAD Decisions: An IC Testing Case StudyPrashanth Krishnamurthy, Animesh Basak Chowdhury, Benjamin Tan, Farshad Khorrami, Ramesh Karri. 129-134 [doi]
- Machine-Learning Enabled Next-Generation Physical Design - An EDA PerspectiveVishal Khandelwal. 135 [doi]
- ML for CAD - Where is the Treasure Hiding?Raviv Gal, David Z. Pan, Haoxing Ren, Manish Pandey, Marilyn Wolf, Avi Ziv. 137 [doi]
- Using Machine Learning Clustering To Find Large Coverage HolesRaviv Gal, Giora Simchoni, Avi Ziv. 139-144 [doi]
- Exploring Logic Optimizations with Reinforcement Learning and Graph Convolutional NetworkKeren Zhu 0001, Mingjie Liu, Hao Chen, Zheng Zhao, David Z. Pan. 145-150 [doi]
- AdaPool: Multi-Armed Bandits for Adaptive Virology Screening on Cyber-Physical Digital-Microfluidic BiochipsMohamed Ibrahim. 151-156 [doi]
- Automatic compiler optimization on embedded software through k-means clusteringMichael Werner, Lorenzo Servadei, Robert Wille, Wolfgang Ecker. 157-162 [doi]
- Transfer Learning for Design-Space Exploration with High-Level SynthesisJihye Kwon, Luca P. Carloni. 163-168 [doi]
- Footprint Classification of Electric Components on Printed Circuit BoardsYun-Jie Ni, Yan-Jhih Wang, Tsung-Yi Ho. 169-174 [doi]