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ORCIDMLCAD 2020: Virtual Event, Iceland
- Ulf Schlichtmann, Raviv Gal, Hussam Amrouch, Hai (Helen) Li:
MLCAD '20: 2020 ACM/IEEE Workshop on Machine Learning for CAD, Virtual Event, Iceland, November 16-20, 2020. ACM 2020, ISBN 978-1-4503-7519-1
Keynote Talk I
- Andrew B. Kahng:
MLCAD Today and Tomorrow: Learning, Optimization and Scaling. 1
Session 1: DNN for CAD
- Abeer Y. Al-Hyari, Ahmed Shamli, Timothy Martin, Shawki Areibi, Gary Gréwal:
An Adaptive Analytic FPGA Placement Framework based on Deep-Learning. 3-8 - Luis Francisco, Tanmay Lagare, Arpit Jain, Somal Chaudhary, Madhura Kulkarni, Divya Sardana, W. Rhett Davis
, Paul D. Franzon:
Design Rule Checking with a CNN Based Feature Extractor. 9-14 - Raviv Gal, Eldad Haber, Avi Ziv:
Using DNNs and Smart Sampling for Coverage Closure Acceleration. 15-20 - Marzieh Ashrafiamiri, Sai Manoj Pudukotai Dinakarrao, Amir Hosein Afandizadeh Zargari, Minjun Seo, Fadi J. Kurdahi, Houman Homayoun:
R2AD: Randomization and Reconstructor-based Adversarial Defense on Deep Neural Network. 21-26 - Hammond Pearce, Benjamin Tan, Ramesh Karri:
DAVE: Deriving Automatically Verilog from English. 27-32
Plenary I
- Brucek Khailany:
Accelerating Chip Design with Machine Learning. 33
Keynote Talk II
- Vijay Deep Bhatt, Wolfgang Ecker, Volkan Esen, Zhao Han, Daniela Sanchez Lopera, Rituj Patel, Lorenzo Servadei, Sahil Singla, Sven Wenzek, Vijaydeep Yadav, Elena Zennaro:
SoC Design Automation with ML - It's Time for Research. 35-36
Session 2: Design Methodology and Optimization
- Lorenzo Servadei, Jiapeng Zheng, Jose A. Arjona-Medina, Michael Werner, Volkan Esen, Sepp Hochreiter, Wolfgang Ecker, Robert Wille:
Cost Optimization at Early Stages of Design Using Deep Reinforcement Learning. 37-42 - An Zou, Karthik Garimella, Benjamin Lee, Christopher D. Gill, Xuan Zhang:
F-LEMMA: Fast Learning-based Energy Management for Multi-/Many-core Processors. 43-48 - Zhengfeng Wu, Ioannis Savidis:
CALT: Classification with Adaptive Labeling Thresholds for Analog Circuit Sizing. 49-54 - Cunxi Yu, Wang Zhou:
Decision Making in Synthesis cross Technologies using LSTMs and Transfer Learning. 55-60 - Isaac Turtletaub, George Li, Mohannad Ibrahim, Paul D. Franzon:
Application of Quantum Machine Learning to VLSI Placement. 61-66
Plenary II
- Ismail S. Bustany:
From Tuning to Learning: Why the FPGA Physical Design Flow Offers a Compelling Case for ML? 67
Keynote Talk III
- Rajeev Jain, Pankaj Kukkal:
Data-driven CAD or Algorithm-Driven CAD: Competitors or Collaborators? 69
Session 3: ML for Reliability Improvement
- Shaoyi Peng, Wentian Jin, Liang Chen, Sheldon X.-D. Tan:
Data-Driven Fast Electrostatics and TDDB Aging Analysis. 71-76 - Jinwei Zhang, Sheriff Sadiqbatcha, Yuanqi Gao, Michael O'Dea, Nanpeng Yu, Sheldon X.-D. Tan:
HAT-DRL: Hotspot-Aware Task Mapping for Lifetime Improvement of Multicore System using Deep Reinforcement Learning. 77-82 - Christian Hakert, Kuan-Hsun Chen, Jian-Jia Chen:
Can Wear-Aware Memory Allocation be Intelligent? 83-88 - Richard Kimmel, Tong Li, David Winston:
An Enhanced Machine Learning Model for Adaptive Monte Carlo Yield Analysis. 89-94 - Martin Rapp, Omar Elfatairy, Marilyn Wolf, Jörg Henkel, Hussam Amrouch:
Towards NN-based Online Estimation of the Full-Chip Temperature and the Rate of Temperature Change. 95-100
Plenary III
- Pak Hei Matthew Leung:
Design Challenges on Post Moore's Law Era. 101
Keynote Talk IV
- Elias Fallon:
Machine Learning in EDA: Opportunities and Challenges. 103
Session 4: Intelligent Modeling
- Haiguang Liao, Qingyi Dong, Weiyi Qi, Elias Fallon, Levent Burak Kara:
Track-Assignment Detailed Routing Using Attention-based Policy Model With Supervision. 105-110 - Husni M. Habal, Dobroslav Tsonev, Matthias Schweikardt:
Compact Models for Initial MOSFET Sizing Based on Higher-order Artificial Neural Networks. 111-116 - Yuan Cao, Tianhao Shen, Li Zhang, Xunzhao Yin, Cheng Zhuo:
An Efficient and Flexible Learning Framework for Dynamic Power and Thermal Co-Management. 117-122 - Felix Last, Ulf Schlichtmann:
Partial Sharing Neural Networks for Multi-Target Regression on Power and Performance of Embedded Memories. 123-128 - Prashanth Krishnamurthy, Animesh Basak Chowdhury, Benjamin Tan, Farshad Khorrami, Ramesh Karri:
Explaining and Interpreting Machine Learning CAD Decisions: An IC Testing Case Study. 129-134
Plenary IV
- Vishal Khandelwal:
Machine-Learning Enabled Next-Generation Physical Design - An EDA Perspective. 135
Panel
- Raviv Gal, David Z. Pan, Haoxing Ren, Manish Pandey, Marilyn Wolf, Avi Ziv:
ML for CAD - Where is the Treasure Hiding? 137
Session 5: ML for Systems
- Raviv Gal, Giora Simchoni, Avi Ziv:
Using Machine Learning Clustering To Find Large Coverage Holes. 139-144 - Keren Zhu, Mingjie Liu, Hao Chen, Zheng Zhao, David Z. Pan:
Exploring Logic Optimizations with Reinforcement Learning and Graph Convolutional Network. 145-150 - Mohamed Ibrahim:
AdaPool: Multi-Armed Bandits for Adaptive Virology Screening on Cyber-Physical Digital-Microfluidic Biochips. 151-156 - Michael Werner, Lorenzo Servadei, Robert Wille, Wolfgang Ecker:
Automatic compiler optimization on embedded software through k-means clustering. 157-162 - Jihye Kwon, Luca P. Carloni:
Transfer Learning for Design-Space Exploration with High-Level Synthesis. 163-168 - Yun-Jie Ni, Yan-Jhih Wang, Tsung-Yi Ho:
Footprint Classification of Electric Components on Printed Circuit Boards. 169-174
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