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Alberto Sangiovanni-Vincentelli
Researcher at University of California, Berkeley
Publications - 946
Citations - 47259
Alberto Sangiovanni-Vincentelli is an academic researcher from University of California, Berkeley. The author has contributed to research in topics: Logic synthesis & Finite-state machine. The author has an hindex of 99, co-authored 934 publications receiving 45201 citations. Previous affiliations of Alberto Sangiovanni-Vincentelli include National University of Singapore & Lawrence Berkeley National Laboratory.
Papers
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Book ChapterDOI
Using Multiple Levels of Abstractions in Embedded Software Design
TL;DR: This paper outlines a framework that is to use for studying the problems of abstraction and refinement in the context of embedded software for hybrid systems.
Proceedings ArticleDOI
Fast hardware-software co-simulation using VHDL models
TL;DR: A technique for hardware-software co-simulation that is almost cycle-accurate, and does nor require the use of interprocess communication for a C language interface for the software components is described.
Journal ArticleDOI
Via assignment problem in multilayer printed circuit board
TL;DR: The via assignment problem is given a graph theoretic formulation and some related optimization problems are proven to belong to a particular class of hard combinatorial problem: the class of nondeterministic polynomial (NP)-complete problems.
B L F-M V An Interchange Format for Design Verification and Synthesis
Robert K. Brayton,Massimiliano Chiodo,Ramin Hojati,Timothy Kam,Kolar L. Kodandapani,Robert P. Kurshan,Sharad Malik,Alberto Sangiovanni-Vincentelli,Ellen M. Sentovich,Thomas R. Shiple,Kanwar Jit Singh Gill,Hong Yue Wang +11 more
Journal ArticleDOI
Negative thinking in branch-and-bound: the case of unate covering
Eugene Goldberg,Luca P. Carloni,Tiziano Villa,Robert K. Brayton,Alberto Sangiovanni-Vincentelli +4 more
TL;DR: The motivation is that when searching the space of solutions by a standard branch-and-bound technique, often a good solution is reached quickly and then improved only a few times before the optimum is found: hence, most of the solution space is explored to certify optimality, with no improvement in the cost function.