The Dark Side: Security Concerns in Machine Learning for EDA
- URL: http://arxiv.org/abs/2203.10597v1
- Date: Sun, 20 Mar 2022 16:44:25 GMT
- Title: The Dark Side: Security Concerns in Machine Learning for EDA
- Authors: Zhiyao Xie and Jingyu Pan and Chen-Chia Chang and Yiran Chen
- Abstract summary: Many unprecedented efficient EDA methods have been enabled by machine learning (ML) techniques.
While ML demonstrates its great potential in circuit design, the dark side about security problems is seldomly discussed.
This paper gives a comprehensive and impartial summary of all security concerns we have observed in ML for EDA.
- Score: 29.20366952640125
- License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
- Abstract: The growing IC complexity has led to a compelling need for design efficiency
improvement through new electronic design automation (EDA) methodologies. In
recent years, many unprecedented efficient EDA methods have been enabled by
machine learning (ML) techniques. While ML demonstrates its great potential in
circuit design, however, the dark side about security problems, is seldomly
discussed. This paper gives a comprehensive and impartial summary of all
security concerns we have observed in ML for EDA. Many of them are hidden or
neglected by practitioners in this field. In this paper, we first provide our
taxonomy to define four major types of security concerns, then we analyze
different application scenarios and special properties in ML for EDA. After
that, we present our detailed analysis of each security concern with
experiments.
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