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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
Time-Domain Non-Monte Carlo Noise Simulation
TL;DR: In this paper, the authors proposed a non-Monte Carlo noise analysis technique for non-stationary stochastic processes in the time domain, which is not restricted to circuits with a time-invariant or quasi-periodic steady-state with WSS or cyclostationary noise sources.
Proceedings ArticleDOI
Communication and Co-Simulation Infrastructure for Heterogeneous System Integration
TL;DR: A communication infrastructure for an integrated design framework that enables co-design and co-simulation of heterogeneous design components specified at different abstraction levels and in different languages is presented.
Proceedings ArticleDOI
Dynamic bound generation for constraint-driven routing
TL;DR: A technique to update dynamically the bounds used during constraint-driven routing, so that nets requiring an implementation with large parasitics can take advantage of the margin made available to them by other parameters maintained within their own bounds.
Proceedings ArticleDOI
Free MDD-based software optimization techniques for embedded systems
TL;DR: This paper describes a heuristic procedure that performs well in practice, and is based on FMDD cost estimation applied to recursive cofactoring, and shows that the new variable ordering method obtains often.
Journal ArticleDOI
Adaptive Body Area Networks Using Kinematics and Biosignals.
TL;DR: In this paper, an adaptive wireless body area network (WBAN) scheme is presented that reconfigures the network by learning from body kinematics and biosignals, which can be exploited by reusing accelerometer data and scheduling packet transmissions at optimal times.