AppGNN: Approximation-Aware Functional Reverse Engineering Using Graph Neural Networks | |
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Author | |
Abstract |
Neural Network Resiliency - The globalization of the Integrated Circuit (IC) market is attracting an ever-growing number of partners, while remarkably lengthening the supply chain. Thereby, security concerns, such as those imposed by functional Reverse Engineering (RE), have become quintessential. RE leads to disclosure of confidential information to competitors, potentially enabling the theft of intellectual property. Traditional functional RE methods analyze a given gate-level netlist through employing pattern matching towards reconstructing the underlying basic blocks, and hence, reverse engineer the circuit’s function. |
Year of Publication |
2022
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Date Published |
oct
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Publisher |
ACM
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Conference Location |
San Diego California
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ISBN Number |
978-1-4503-9217-4
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URL |
https://dl.acm.org/doi/10.1145/3508352.3549471
|
DOI |
10.1145/3508352.3549471
|
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