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.

Hardware/Software Co-Synthesis and Estimation

Abstract: Our proposed overall co-synthesis flow is shown in Figure 7.1. The CDFG is built after performing the FFG task representation architecture independent optimizations, as well as the architecture dependent AFFG optimizations, followed by the optimal mapping step. After the AFFG is mapped onto an optimized CDFG we proceed with reactive synthesis. In the next section, we describe the CDFG representation, and the hardware and software co-synthesis techniques of the Polis co-design tool. A design environment based on hardware / software co-synthesis allows the designer to specify the system in a high level formal language (e.g. Esterel [16] front-end that our flow uses) by describing the functionality of each block and how blocks are connected together.

An edge‐oriented adjacency list for undirected graphs

Abstract: A new data structure, called the Edge-Oriented Adjacency List (EOAL), for representing undirected graphs is presented. It provides more information on the edges and requires less storage space than the conventional adjacency list. Furthermore, it is superior to the conventional adjacency list in both insertion and deletion operations.

Aura II: Combining Negative Thinking and Branch-and-Bound in Unate Covering Problems

Abstract: Recently a novel technique has been published to augment traditional Branch-and-Bound (B&B) while solving exactly a discrete optimization problem [Goldberg et al., 1997]. This technique is based on the negative thinking paradigm and has been applied to develop AURA, a Unate Covering Problem (UCP) solver which reportedly was able to deal efficiently with some time–consuming benchmark problems. However, on average AURA was not able to compete with SCHERZO, a classical UCP solver based on several new bounding techniques proposed by O. Coudert in his breakthrough paper [Coudert, 1996]. This fact left open the question on the practical impact of the negative thinking paradigm. The present work is meant to settle this question. The paper discusses the details of AURA II, a new implementation of the negative thinking paradigm for UCP which combines the best of SCHERZO and AURA. Experimental results show the dramatic impact of the negative thinking paradigm in searching the solution space and propose AURA II as the most efficient available tool for unate covering.

Advances in encoding for logic synthesis.

Abstract: In thispaper we survey the recent literature onencoding problems arising from logicsynthesis of combinational and synchronous sequential circuits. Encoding problems consist ofassigning Boolean codes to inputand outputsymbolic variables, so thata cost function measuring the optimality of a two-level or multi-level implementation is minimized. A successful paradigm involves optimizing thesymbolicrepresentation (symbolic minimization), and thentransforming theoptimizedsymbolic description intoacompatible two-valued representation, by satisfying encoding constraints (bit-wise logic relations) imposed on the binary codes that replace the symbols. The inputencoding problem is well understood. Efficient algorithms are available for two-level implementations and are under development for multi-level implementations. The output encoding problem has seen important contributions, butmoreworkneeds to be done and efficient algorithms developed.

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.