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
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Journal ArticleDOI
A text-compression-based method for code size minimization in embedded systems
TL;DR: This work addresses the problem of code-size minimization in VLSI systems with embedded DSP processors with data-compression methods, and describes two methods that have different performance characteristics and different degrees of freedom in compressing the code.
Proceedings ArticleDOI
Synthesis and optimization procedures for robustly delay-fault testable combinational logic circuits
Srinivas Devadas,Kurt Keutzer +1 more
TL;DR: This paper applies recently developed necessary and sufficient conditions for robust path-delay-fault testability to develop synthesis procedures which produce two-level and multilevel circuits with high degrees of robust path delay fault testability.
Proceedings ArticleDOI
Estimation of power dissipation in CMOS combinational circuits
TL;DR: It is shown that a simplified model of power dissipation relates maximizing dissipation to maximizing gate output activity, appropriately weighted to account for differing load capacitances, and algorithms for solving the generated weighted max-satisfiability problem are presented.
Journal ArticleDOI
Optimum and heuristic algorithms for an approach to finite state machine decomposition
TL;DR: Optimum and heuristic algorithms for the general decomposition of finite state machines such that the sum total of the number of product terms in the one-hot-coded and logic-minimized submachines is minimum or minimal are presented.
Proceedings ArticleDOI
Solving covering problems using LPR-based lower bounds
Stan Liao,Srinivas Devadas +1 more
TL;DR: It is shown that a combination of traditional reductions (essentiality and dominance) and incremental computation of LPR-based lower bounds can exactly solve difficult covering problems orders of magnitude faster than traditional methods.