Alberto Sangiovanni-Vincentelli

Bio: 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.

Optimal printed circuit board ordering via hart‐nilsson‐raphael‐algorithm

Abstract: A layout problem, the optimal ordering of printed circuit boards equally spaced on a straight line, is investigated. An optimal algorithm whose structure is based on the Hart, Nilsson and Raphael algorithm is presented and its complexity evaluated. Since the intrinsic complexity of the problem is shown to be very high, an approximated algorithm is introduced and its performances are evaluated. Experimental results are discussed to emphasize the efficiency of the approximated algorithm.

Macromodelling for the simulation of large scale analog integrated circuits

Abstract: Circuit stimulation is one of the most valuable tools available to the circuit designer, providing him with accurate information about the electrical behavior of a particular circuit without the need of building it. Unfortunately the CPU time required to perform each simulation grows very quickly with the circuit size, making it problematic to simulate circuits containing more than a few hundred nodes. One of the methods suggested to overcome this hurdle is the use of macromodels for functional blocks such as operational amplifiers or logic gates. All the methods to generate macromodels that have appeared in the literature so far make very stringent assumptions about either the type of circuits to be modeled or the shape of the waveforms involved. The models generated in this way are therefore unsuitable for circuit simulation. This thesis describes a general purpose algorithm that can be used to generate models for an arbitrary circuit without any restrictions on the waveforms. Experimental results indicate that the algorithm requires reasonable amounts of CPU time and that the models are accurate enough to be used for standard circuit simulation.

A semantic-driven synthesis flow for platform-based design

Abstract: In this work, we propose a semantics-driven synthesis flow, in which the semantics and the abstraction level are determined formally by using the concept of a common modeling domain between functionality and architecture. By doing so, a formal synthesis procedure can be defined and algorithms for automatic optimal mapping derived.

Conservative approximations for heterogeneous design

Abstract: Embedded systems are electronic devices that function in the context of a real environment, by sensing and reacting to a set of stimuli. Because of their close interaction with the environment, and to simplify their design, different parts of an embedded system are best described using different notations and different techniques. In this case, we say that the system is heterogeneous.We informally refer to the notation and the rules that are used to specify and verify the elements of heterogeneous system and their collective behavior as a model of computation. In this paper, we focus in particular on abstraction and refinement relationships in the form of conservative approximations. We do so by constructing a framework, called Agent Algebra, where the different models reside and share a common algebraic structure. We compare our techniques to the well established notion of abstract interpretation. We show that, unlike abstract interpretations, conservative approximations preserve refinement verification results from an abstract to a concrete model while avoiding false positives. In addition, we use the inverse of a conservative approximation to identify components that can be used indifferently in several models, thus enabling reuse across domains of computation.

Programmable platform characterization for system level performance analysis

Abstract: It is customary to begin a discussion of Electronic System Level (ESL) design by stating at least one of the following observations [7]: • Time-to-market is a major influence on the design of most electronic systems; • Design complexity has outpaced designer productivity; • Verification effort now dominates design effort; • Custom design costs too much to be practical for most designs; • Register Transfer Level (RTL) design methods will not scale to address the design complexity; • Designers must work at higher levels of design abstraction to overcome design complexity; • Design re-use will be necessary to overcome design complexity. Figure 1 presents a graph relating design complexity to designer productivity with both RTL and ESL design methods. Today, most designers work with RTL design tools and languages. They find themselves in the ‘design gap’ where the system they are trying to create exceeds the capabilities of their design environment. This is not to say that the design gap cannot be crossed. On the contrary, the gap can be overcome with existing design methods but only at a significantly increased cost (both financially [5], [6] and in designer effort). Existing RTL design methods will continue to be employed until the additional cost of design overwhelms the

Gradient-based learning applied to document recognition

Abstract: Multilayer neural networks trained with the back-propagation algorithm constitute the best example of a successful gradient based learning technique. Given an appropriate network architecture, gradient-based learning algorithms can be used to synthesize a complex decision surface that can classify high-dimensional patterns, such as handwritten characters, with minimal preprocessing. This paper reviews various methods applied to handwritten character recognition and compares them on a standard handwritten digit recognition task. Convolutional neural networks, which are specifically designed to deal with the variability of 2D shapes, are shown to outperform all other techniques. Real-life document recognition systems are composed of multiple modules including field extraction, segmentation recognition, and language modeling. A new learning paradigm, called graph transformer networks (GTN), allows such multimodule systems to be trained globally using gradient-based methods so as to minimize an overall performance measure. Two systems for online handwriting recognition are described. Experiments demonstrate the advantage of global training, and the flexibility of graph transformer networks. A graph transformer network for reading a bank cheque is also described. It uses convolutional neural network character recognizers combined with global training techniques to provide record accuracy on business and personal cheques. It is deployed commercially and reads several million cheques per day.

Differential Evolution – A Simple and Efficient Heuristic for Global Optimization over Continuous Spaces

Abstract: A new heuristic approach for minimizing possibly nonlinear and non-differentiable continuous space functions is presented. By means of an extensive testbed it is demonstrated that the new method converges faster and with more certainty than many other acclaimed global optimization methods. The new method requires few control variables, is robust, easy to use, and lends itself very well to parallel computation.

Phd by thesis

Abstract: Deposits of clastic carbonate-dominated (calciclastic) sedimentary slope systems in the rock record have been identified mostly as linearly-consistent carbonate apron deposits, even though most ancient clastic carbonate slope deposits fit the submarine fan systems better. Calciclastic submarine fans are consequently rarely described and are poorly understood. Subsequently, very little is known especially in mud-dominated calciclastic submarine fan systems. Presented in this study are a sedimentological core and petrographic characterisation of samples from eleven boreholes from the Lower Carboniferous of Bowland Basin (Northwest England) that reveals a >250 m thick calciturbidite complex deposited in a calciclastic submarine fan setting. Seven facies are recognised from core and thin section characterisation and are grouped into three carbonate turbidite sequences. They include: 1) Calciturbidites, comprising mostly of highto low-density, wavy-laminated bioclast-rich facies; 2) low-density densite mudstones which are characterised by planar laminated and unlaminated muddominated facies; and 3) Calcidebrites which are muddy or hyper-concentrated debrisflow deposits occurring as poorly-sorted, chaotic, mud-supported floatstones. These

Graph-Based Algorithms for Boolean Function Manipulation

Abstract: In this paper we present a new data structure for representing Boolean functions and an associated set of manipulation algorithms. Functions are represented by directed, acyclic graphs in a manner similar to the representations introduced by Lee [1] and Akers [2], but with further restrictions on the ordering of decision variables in the graph. Although a function requires, in the worst case, a graph of size exponential in the number of arguments, many of the functions encountered in typical applications have a more reasonable representation. Our algorithms have time complexity proportional to the sizes of the graphs being operated on, and hence are quite efficient as long as the graphs do not grow too large. We present experimental results from applying these algorithms to problems in logic design verification that demonstrate the practicality of our approach.

Principles of Model Checking

Abstract: Our growing dependence on increasingly complex computer and software systems necessitates the development of formalisms, techniques, and tools for assessing functional properties of these systems. One such technique that has emerged in the last twenty years is model checking, which systematically (and automatically) checks whether a model of a given system satisfies a desired property such as deadlock freedom, invariants, and request-response properties. This automated technique for verification and debugging has developed into a mature and widely used approach with many applications. Principles of Model Checking offers a comprehensive introduction to model checking that is not only a text suitable for classroom use but also a valuable reference for researchers and practitioners in the field. The book begins with the basic principles for modeling concurrent and communicating systems, introduces different classes of properties (including safety and liveness), presents the notion of fairness, and provides automata-based algorithms for these properties. It introduces the temporal logics LTL and CTL, compares them, and covers algorithms for verifying these logics, discussing real-time systems as well as systems subject to random phenomena. Separate chapters treat such efficiency-improving techniques as abstraction and symbolic manipulation. The book includes an extensive set of examples (most of which run through several chapters) and a complete set of basic results accompanied by detailed proofs. Each chapter concludes with a summary, bibliographic notes, and an extensive list of exercises of both practical and theoretical nature.