Srinivas Devadas

Bio: 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.

General Decomposition of Sequential Machines: Relationships to State Assignment

Abstract: In this paper, we present new techniques for state assignment of finite state machines based on state machine decomposition algorithms. A finite state machine can be decomposed into smaller interacting machines so as to optimize area and performance of the eventual logic implementation. A recently proposed form of decomposition, which has been shown to be superior to previous decomposition methods, involves identifying subroutines or factors in the original machine and extracting these factors to produce factored and factoring machines. Optimal state assignment corresponds to finding an optimal multiple general decomposition of a finite state machine. We present state assignment techniques targeting two-level and multi-level logic implementations based on factorization algorithms followed by state assignment algorithms. For the two-level case, we prove that one-hot encoding a non-trivially factored machine is guaranteed to produce a better result than one-hot encoding the original machine. Experimental results indicate that this technique of factorization followed by state assignment is superior to previous state assignment techniques for large sequential machines, when targeting either two-level or multi-level implementations.

Bridging the gap between single-template and fragment based protein structure modeling using Spanner

Abstract: Background: As the coverage of experimentally determined protein structures increases, fragment-based structural modeling approaches are expected to play an ever more important role in structural modeling. Here we introduce a structural modeling method by which an initial structural template can be extended by the addition of structural fragments to more closely match an aligned query sequence. A database of pro-tein fragments indexed by their internal coordinates was created and a novel methodology for their retrieval was implemented. After fragment selection and assembly, sidechains are replaced and the all-atom model is refined by restrained energy minimization. We implemented the proposed method in the program Span-ner and benchmarked it using a previously published set of 367 immunoglobulin (Ig) loops, 206 historical query-template pairs and alignments from the Critical Assessment of protein Structure Prediction (CASP) experiment, and 217 structural alignments between remotely homologous query-template pairs. The con-straint-based modeling software MODELLER and previously reported results for RosettaAntibody, were used as references. Results: The error in the modeled structures was assessed by root-mean square deviation (RMSD) from the native structure, as a function of the query-template sequence identity. For the Ig benchmark set, for which a single fragment was used to model each loop, the average RMSD for Spanner (3 +/- 1.5 A) was found to lie midway between that of MODELLER (4 +/- 2 A) and RosettaAntibody (2 +/- 1 A). For the CASP and structural alignment benchmarks, for which gaps represent a small fraction of the modeled residues, the difference between Spanner and MODELLER were much smaller then the standard deviations of either program. The Spanner web server and source code are available at http://sysimm.ifrec.osaka-u.ac.jp/Spanner/. Conclusions: For typical homology modeling, Spanner is at least as good, on average as the template-free constraint-driven approach used by MODELLER. The Ig model results suggest that when gap regions represent a significant fraction of the alignment, Spanner’s efficient use of fragment libraries, along with local sequence and secondary structural information, significantly improve model accuracy without a dra-matic increase in computational cost.

Irredundant sequential machines via optimal logic synthesis

Abstract: It is shown that optimal sequential logic synthesis can produce irredundant, fully testable finite-state machines. Synthesizing a sequential circuit from a state-transition-graph description involves the steps of state minimization, state assignment, and logic optimization. It is also shown that 100% testability can be ensured without the addition of extra logic and without constraints on the state assignment and logic optimization. There is no area/performance penalty associated with this approach. This technique can be used in conjunction with previous approaches to ensure that the synthesized machine is easily testable. Given a state-transition-graph specification, a logic-level automation that is fully testable for all single stuck-at faults in the combinational logic without access to the memory elements can be synthesized. These procedures represent an alternative to a scan-design methodology, without the latter's usual area and performance penalty. >

TaintDroid: An Information-Flow Tracking System for Realtime Privacy Monitoring on Smartphones

Abstract: Today’s smartphone operating systems frequently fail to provide users with visibility into how third-party applications collect and share their private data. We address these shortcomings with TaintDroid, an efficient, system-wide dynamic taint tracking and analysis system capable of simultaneously tracking multiple sources of sensitive data. TaintDroid enables realtime analysis by leveraging Android’s virtualized execution environment. TaintDroid incurs only 32p performance overhead on a CPU-bound microbenchmark and imposes negligible overhead on interactive third-party applications. Using TaintDroid to monitor the behavior of 30 popular third-party Android applications, in our 2010 study we found 20 applications potentially misused users’ private information; so did a similar fraction of the tested applications in our 2012 study. Monitoring the flow of privacy-sensitive data with TaintDroid provides valuable input for smartphone users and security service firms seeking to identify misbehaving applications.

TaintDroid: an information-flow tracking system for realtime privacy monitoring on smartphones

Abstract: Today's smartphone operating systems frequently fail to provide users with adequate control over and visibility into how third-party applications use their private data. We address these shortcomings with TaintDroid, an efficient, system-wide dynamic taint tracking and analysis system capable of simultaneously tracking multiple sources of sensitive data. TaintDroid provides realtime analysis by leveraging Android's virtualized execution environment. TaintDroid incurs only 14% performance overhead on a CPU-bound micro-benchmark and imposes negligible overhead on interactive third-party applications. Using TaintDroid to monitor the behavior of 30 popular third-party Android applications, we found 68 instances of potential misuse of users' private information across 20 applications. Monitoring sensitive data with TaintDroid provides informed use of third-party applications for phone users and valuable input for smartphone security service firms seeking to identify misbehaving applications.

Symbolic Boolean manipulation with ordered binary-decision diagrams

Abstract: Ordered Binary-Decision Diagrams (OBDDs) represent Boolean functions as directed acyclic graphs. They form a canonical representation, making testing of functional properties such as satisfiability and equivalence straightforward. A number of operations on Boolean functions can be implemented as graph algorithms on OBDD data structures. Using OBDDs, a wide variety of problems can be solved through symbolic analysis. First, the possible variations in system parameters and operating conditions are encoded with Boolean variables. Then the system is evaluated for all variations by a sequence of OBDD operations. Researchers have thus solved a number of problems in digital-system design, finite-state system analysis, artificial intelligence, and mathematical logic. This paper describes the OBDD data structure and surveys a number of applications that have been solved by OBDD-based symbolic analysis.

Physical unclonable functions for device authentication and secret key generation

Abstract: Physical Unclonable Functions (PUFs) are innovative circuit primitives that extract secrets from physical characteristics of integrated circuits (ICs). We present PUF designs that exploit inherent delay characteristics of wires and transistors that differ from chip to chip, and describe how PUFs can enable low-cost authentication of individual ICs and generate volatile secret keys for cryptographic operations.