S
Srinivas Devadas
Researcher at Massachusetts Institute of Technology
Publications - 498
Citations - 35003
Srinivas Devadas is an academic researcher from Massachusetts Institute of Technology. The author has contributed to research in topics: Sequential logic & Combinational logic. The author has an hindex of 88, co-authored 480 publications receiving 31897 citations. Previous affiliations of Srinivas Devadas include University of California, Berkeley & Cornell University.
Papers
More filters
Proceedings ArticleDOI
Implicit enumeration techniques applied to asynchronous circuit verification
TL;DR: The authors give a procedure to extract the complete set of possible flow tables from a gate-level description of an asynchronous circuit under the bounded wire delay model and construct a product flow table to check for machine equivalence.
Journal Article
Can we trust the chips of the future
Mohammad Tehranipour,Srinivas Devadas,K. Gotze,Farinaz Koushanfar,Miodrag Potkonjak,Ingrid Verbauwhede,David Yeh +6 more
TL;DR: This roundtable is based on the topic of hardware security and trust, which was the focus of the IEEE International Symposium on Hardware-Oriented Security and Trust (HOST 2011) held with the 2011 Design Automation Conference.
Patent
Error correction for physical uncloneable function
TL;DR: In this paper, a chip and a method for correcting variation in output of a PUF circuit is provided, wherein the method comprises: creating a set of challenge-response pairs by passing challenges c 1,..., c n through a PU-F circuit to obtain responses r 1,..., r n ; producing redundancy information from the responses; on subsequent use of the challengeresponse pairs, providing the redundancy information to an ECC module and correcting in the ECC response received from the PU-FC circuit to produce r' 1,,, r' n.
Proceedings ArticleDOI
Using Application-Level Thread Progress Information to Manage Power and Performance
TL;DR: This work presents ThreadBeats, a simple application-level annotation framework that directly and accurately conveys thread progress information to hardware, and designs DVFS controllers that exploit Thread beats information for two purposes: improving performance by equalizing thread progress and minimizing runtime under a power budget constraint.
Posted Content
Trapdoor Computational Fuzzy Extractors and Stateless Cryptographically-Secure Physical Unclonable Functions
TL;DR: In this paper, the authors presented a fuzzy extractor whose security can be reduced to the hardness of Learning Parity with Noise (LPN) and can efficiently correct a constant fraction of errors in a biometric source with a noise-avoiding trapdoor.