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Yuzhe Ma
Researcher at The Chinese University of Hong Kong
Publications - 61
Citations - 1135
Yuzhe Ma is an academic researcher from The Chinese University of Hong Kong. The author has contributed to research in topics: Computer science & Artificial neural network. The author has an hindex of 9, co-authored 48 publications receiving 460 citations. Previous affiliations of Yuzhe Ma include Nvidia & New York University.
Papers
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Journal ArticleDOI
Recent advances in convolutional neural network acceleration
TL;DR: In this paper, the authors present a taxonomy of CNN acceleration methods in terms of three levels, i.e. structure level, algorithm level, and implementation level, for CNN architectures compression, algorithm optimization and hardware-based improvement.
Posted Content
Recent Advances in Convolutional Neural Network Acceleration
TL;DR: This paper summarizes the acceleration methods that contribute to but not limited to CNN by reviewing a broad variety of research papers, and proposes a taxonomy in terms of three levels, i.e. structure level, algorithm level, and implementation level, for acceleration methods.
Journal ArticleDOI
Machine Learning for Electronic Design Automation: A Survey
Guyue Huang,Jingbo Hu,Yifan He,Jialong Liu,Mingyuan Ma,Zhaoyang Shen,Juejian Wu,Yuanfan Xu,Hengrui Zhang,Kai Zhong,Xuefei Ning,Yuzhe Ma,Haoyu Yang,Bei Yu,Huazhong Yang,Yu Wang +15 more
TL;DR: In this paper, the application of machine learning (ML) techniques in electronic design automata has been discussed, and the design complexity of very large-scale integrated is increasing with the down-scaling of CMOS technology.
Journal ArticleDOI
Layout Hotspot Detection With Feature Tensor Generation and Deep Biased Learning
TL;DR: A deep learning framework for high performance and large scale hotspot detection that uses feature tensor generation to extract representative layout features that fit well with convolutional neural networks while keeping the spatial relationship of the original layout pattern with minimal information loss.
Proceedings ArticleDOI
GAN-OPC: mask optimization with lithography-guided generative adversarial nets
TL;DR: A generative adversarial network (GAN) model is developed that can create quasi-optimal masks for given target circuit patterns and fewer normal OPC steps are required to generate high quality masks.