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.

Finite state machine decomposition by transition pairing

Abstract: The authors develop a method based on the premise that optimal state assignment corresponds to finding an optimal general decomposition of a finite state mechanism (FSM). They discuss the use of this approach for encoding state transition graphs extracted from logic-level descriptions. The notion of transition pairing is used to decompose a given FSM into several submachines such that the state assignment problem for the submachines is simpler than the original problem, attempting to avoid compromising the optimality of the solution. A novel decomposition algorithm that can decompose a FSM into an arbitrary number of submachines and a novel constraint satisfaction algorithm to encode the different submachines are given. Experimental results validate the use of decomposition-based techniques to solve the encoding problem. >

Performance-oriented decomposition of sequential circuits

Abstract: A synthesis procedure which partitions a given finite state machine (FSM) into an interconnection of submachines in order to improve the timing performance of the circuit is presented. A decomposition via factorization which decomposes a given machine of any size by extracting and merging groups of states having similar I/O transition edges and representing them in separate submachines is discussed. These submachines are interconnected in a parallel, but interactive, fashion to realize the original terminal behavior. Timing improvements are achieved by optimally identifying and sharing common logic blocks in the eventual realization of a given FSM. The complete procedure operates solely at the state transition graph level, thus constituting a high-level, or global timing optimization step in the synthesis of FSMs. Results which illustrate the efficacy of this procedure are presented. Based on a set of benchmark examples, the factorization procedure, on the average, decreases the critical-path delays of the original FSMs by 26% while keeping the area increase in the decomposed FSMs within 33% under both two-level and multilevel logic implementations. >

Thread Migration Prediction for Distributed Shared Caches

Abstract: Chip-multiprocessors (CMPs) have become the mainstream parallel architecture in recent years; for scalability reasons, designs with high core counts tend towards tiled CMPs with physically distributed shared caches. This naturally leads to a Non-Uniform Cache Access (NUCA) design, where on-chip access latencies depend on the physical distances between requesting cores and home cores where the data is cached. Improving data locality is thus key to performance, and several studies have addressed this problem using data replication and data migration. In this paper, we consider another mechanism, hardware-level thread migration. This approach, we argue, can better exploit shared data locality for NUCA designs by effectively replacing multiple round-trip remote cache accesses with a smaller number of migrations. High migration costs, however, make it crucial to use thread migrations judiciously; we therefore propose a novel, on-line prediction scheme which decides whether to perform a remote access (as in traditional NUCA designs) or to perform a thread migration at the instruction level. For a set of parallel benchmarks, our thread migration predictor improves the performance by 24% on average over the shared-NUCA design that only uses remote accesses.

Learning biophysically-motivated parameters for alpha helix prediction.

Abstract: Our goal is to develop a state-of-the-art protein secondary structure predictor, with an intuitive and biophysically-motivated energy model. We treat structure prediction as an optimization problem, using parameterizable cost functions representing biological "pseudo-energies". Machine learning methods are applied to estimate the values of the parameters to correctly predict known protein structures. Focusing on the prediction of alpha helices in proteins, we show that a model with 302 parameters can achieve a Q α value of 77.6% and an SOV α value of 73.4%. Such performance numbers are among the best for techniques that do not rely on external databases (such as multiple sequence alignments). Further, it is easier to extract biological significance from a model with so few parameters. The method presented shows promise for the prediction of protein secondary structure. Biophysically-motivated elementary free-energies can be learned using SVM techniques to construct an energy cost function whose predictive performance rivals state-of-the-art. This method is general and can be extended beyond the all-alpha case described here.

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.