activity
20182021
most citedMachine Learning for Electronic Design Automation: A Survey

18 citations · 23 across the 3 of their papers we have counts for

collaborators

8 papers

eess.SP202118 cited

Machine Learning for Electronic Design Automation: A Survey

Guyue Huang, Jingbo Hu, Yifan He +13

With the down-scaling of CMOS technology, the design complexity of very large-scale integrated (VLSI) is increasing. Although the application of machine learning (ML) techniques in…

cs.AR2020

DAMO: Deep Agile Mask Optimization for Full Chip Scale

Guojin Chen, Wanli Chen, Yuzhe Ma +2

Continuous scaling of the VLSI system leaves a great challenge on manufacturing and optical proximity correction (OPC) is widely applied in conventional design flow for manufactura…

cs.LG2019

VLSI Mask Optimization: From Shallow To Deep Learning

Haoyu Yang, Wei Zhong, Yuzhe Ma +4

VLSI mask optimization is one of the most critical stages in manufacturability aware design, which is costly due to the complicated mask optimization and lithography simulation. Re…

cs.OH20195 cited

CAD Tool Design Space Exploration via Bayesian Optimization

Yuzhe Ma, Ziyang Yu, Bei Yu

The design complexity is increasing as the technology node keeps scaling down. As a result, the electronic design automation (EDA) tools also become more and more complex. There ar…

cs.LG2019

Are Adversarial Perturbations a Showstopper for ML-Based CAD? A Case Study on CNN-Based Lithographic Hotspot Detection

Kang Liu, Haoyu Yang, Yuzhe Ma +5

There is substantial interest in the use of machine learning (ML) based techniques throughout the electronic computer-aided design (CAD) flow, particularly those based on deep lear…

cs.LG2018

Recent Advances in Convolutional Neural Network Acceleration

Qianru Zhang, Meng Zhang, Tinghuan Chen +3

In recent years, convolutional neural networks (CNNs) have shown great performance in various fields such as image classification, pattern recognition, and multi-media compression.…