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Locking-based intellectual property (IP) protection for integrated circuits (ICs) being manufactured at untrusted facilities has been largely defeated by the satisfiability (SAT) attack, which can retrieve the secret key needed for instantiating proprietary functionality on locked circuits. As a result, redaction-based methods have gained popularity as a more secure way of protecting hardware IP. Among these methods, transistor-level programming (TRAP) prohibits the outright use of SAT attacks due to the mismatch between the logic-level at which SAT attack operates and the switch-level at which the TRAP fabric is programmed. Herein, we discuss the challenges involved in launching SAT attacks on TRAP and we propose solutions which enable expression of TRAP in propositional logic modeling in a way that accurately reflects switch-level circuit capabilities. Results obtained using a transistor-level SAT attack tool-set that we developed and are releasing corroborate that SAT attacks can be launched against TRAP. However, the increased complexity of switch-level circuit modeling prevents the attack from realistically compromising all but the most trivial IP-protected designs.
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On the hazards of relating representations and inductive biases
The success of models of human behavior based on Bayesian inference over logical formulas or programs is taken as evidence that people employ a "language-of-thought" that has similarly discrete and compositional structure. We argue that this conclusion problematically crosses levels of analysis, identifying representations at the algorithmic level based on inductive biases at the computational level.
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- Award ID(s):
- 2204152
- PAR ID:
- 10500620
- Publisher / Repository:
- Cambridge University Press
- Date Published:
- Journal Name:
- Behavioral and Brain Sciences
- Volume:
- 46
- ISSN:
- 0140-525X
- 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
- Journal Article:
- https://doi.org/10.1017/S0140525X23002042
