Attacking a CNN-based Layout Hotspot Detector Using Group Gradient Method

Haoyu Yang, Shifan Zhang, Kang Liu, Siting Liu, Benjamin Tan, Ramesh Karri, Siddharth Garg, Bei Yu, Evangeline F.Y. Young

Research output: Chapter in Book/Report/Conference proceedingConference contribution

Abstract

Deep neural networks are being used in disparate VLSI design automation tasks, including layout printability estimation, mask optimization, and routing congestion analysis. Preliminary results show the power of deep learning as an alternate solution in state-of-theart design and sign-off flows. However, deep learning is vulnerable to adversarial attacks. In this paper, we examine the risk of state-ofthe- art deep learning-based layout hotspot detectors under practical attack scenarios. We show that legacy gradient-based attacks do not adequately consider the design rule constraints. We present an innovative adversarial attack formulation to attack the layout clips and propose a fast group gradient method to solve it. Experiments show that the attack can deceive the deep neural networks using small perturbations in clips which preserve layout functionality while meeting the design rules. The source code is available at https://github.com/phdyang007/dlhsd/tree/dct_as_conv.

Original languageEnglish (US)
Title of host publicationProceedings of the 26th Asia and South Pacific Design Automation Conference, ASP-DAC 2021
PublisherInstitute of Electrical and Electronics Engineers Inc.
Pages885-891
Number of pages7
ISBN (Electronic)9781450379991
DOIs
StatePublished - Jan 18 2021
Event26th Asia and South Pacific Design Automation Conference, ASP-DAC 2021 - Virtual, Online, Japan
Duration: Jan 18 2021Jan 21 2021

Publication series

NameProceedings of the Asia and South Pacific Design Automation Conference, ASP-DAC

Conference

Conference26th Asia and South Pacific Design Automation Conference, ASP-DAC 2021
CountryJapan
CityVirtual, Online
Period1/18/211/21/21

ASJC Scopus subject areas

  • Electrical and Electronic Engineering
  • Computer Science Applications
  • Computer Graphics and Computer-Aided Design

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