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Forensic engineering techniques for VLSI CAD tools
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Source Annual ACM IEEE Design Automation Conference archive
Proceedings of the 37th Annual Design Automation Conference table of contents
Los Angeles, California, United States
Pages: 581 - 586  
Year of Publication: 2000
ISBN:1-58113-187-9
Authors
Darko Kirovski  Computer Science Department, University of California, Los Angeles
David Liu  Computer Science Department, University of California, Los Angeles
Jennifer Wong  Computer Science Department, University of California, Los Angeles
Miodrag Potkonjak  Computer Science Department, University of California, Los Angeles
Sponsors
SIGDA: ACM Special Interest Group on Design Automation
EDAC : Electronic Design Automation Consortium
IEEE-CAS : Circuits & Systems
Publisher
ACM  New York, NY, USA
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Downloads (6 Weeks): 6,   Downloads (12 Months): 27,   Citation Count: 6
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ABSTRACT

The proliferation of the Internet has affected the business model of almost all semiconductor and VLSI CAD companies that rely on intellectual property (IP) as their main source of revenues. The fact that IP has become more accessible and easily transferable, has influenced the emergence of copyright infringement as one of the most common obstructions to e-commerce of IP. In this paper, we propose a generic forensic engineering technique that addresses a number of copyright infringement scenarios. Given a solution SP to a particular optimization problem instance P and a finite set of algorithms A applicable to P, the goal is to identify with a certain degree of confidence the algorithm Ai which has been applied to P in order to obtain SP. We have applied forensic analysis principles to two problem instances commonly encountered in VLSI CAD: graph coloring and boolean satisfiability. We have demonstrated that solutions produced by strategically different algorithms can be associated with their corresponding algorithms with high accuracy.


REFERENCES

Note: OCR errors may be found in this Reference List extracted from the full text article. ACM has opted to expose the complete List rather than only correct and linked references.

 
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Collaborative Colleagues:
Darko Kirovski: colleagues
David Liu: colleagues
Jennifer Wong: colleagues
Miodrag Potkonjak: colleagues