Chip designers can secure their ICs against piracy and overproduction by employing logic locking and obfuscation. However, there are numerous attacks that can examine the logic-locked netlist with the assistance of an activated IC and extract the correct key using a SAT solver. In addition, when it comes to fabrication, the imposed area overhead is a challenge that needs careful attention to preserve the design goals. Thus, to assign a logic locking method that can provide security against diverse attacks and at the same time add minimal area overhead, a comprehensive understanding of the circuit structure is needed. Towards this goal, in this paper, we first build a multi-label dataset by running different attacks on benchmarks locked with existing logic locking methods and various key sizes to capture the provided level of security and overhead for each benchmark. Then we propose and analyze CoLA, a convolutional neural network model that is trained on this dataset and thus is able to map circuits to secure low-overhead locking schemes by analyzing extracted features of the benchmark circuits. Considering various resynthesized versions of the same circuits empowers CoLA to learn features beyond the structure view alone. We use a quantization method that can lower the computation overhead of feature extraction in the classification of new, unseen data, hence speeding up the locking assignment process. Results on over 10,000 data show high accuracy both in the training and validation phases.
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Secure Logic Locking with Strain-Protected Nanomagnet Logic
Prevention of integrated circuit counterfeiting through logic locking faces the fundamental challenge of securing an obfuscation key against both physical and algorithmic threats. Previous work has focused on strengthening the logic encryption to protect the key against algorithmic attacks, but failed to provide adequate physical security. In this work, we propose a logic locking scheme that leverages the non-volatility of the nanomagnet logic (NML) family to achieve both physical and algorithmic security. Polymorphic NML minority gates protect the obfuscation key against algorithmic attacks, while a strain-inducing shield surrounding the nanomagnets provides physical security via a self-destruction mechanism.
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- Award ID(s):
- 1954589
- NSF-PAR ID:
- 10324549
- Date Published:
- Journal Name:
- 2021 58th ACM/IEEE Design Automation Conference (DAC)
- Page Range / eLocation ID:
- 127 to 132
- Format(s):
- Medium: X
- Sponsoring Org:
- National Science Foundation
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- Free Publicly Accessible Full Text
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Accepted Manuscript1.0
- Conference Paper:
- https://doi.org/10.1109/DAC18074.2021.9586258
