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.

Circuit structure relations to redundancy and delay: the KMS algorithm revisited

Abstract: K. Keutzer et al. (see IEEE Trans. on Comput.-Aided Des., vol.10, no.4, p.427-35 (1991)) have presented an algorithm, known as the KMS algorithm, that derives an equivalent irredundant circuit implementation from a given redundant high-performance circuit, with no increase in delay measured using viability analysis. The authors resolve the main bottlenecks in the KMS algorithm, arising due to an iterative loop of timing analysis, gate duplications, and redundancy removal. A circuit structure property based on path lengths is related to testability and delay. Based on this relationship, an efficient implementation of the KMS algorithm is presented. It consists of the transformation of any Boolean network to an equivalent circuit structure on which a single redundancy removal achieves the same effect as the original KMS algorithm. >

A Computational Approach for the Diagnosability of Dynamical Circuits

Abstract: Based on a discrete-time circuit description, a necessary and sufficient condition is derived for the local diagnosability of the class of dynamical circuits whose branch relations are analytic functions of their arguments. Both the single-fault case and the case where all the parameters are assumed to be faulty are dealt with. The condition is a rank test on a matrix that is determined from a single circuit simulation and sensitivity computation. The technique is illustrated with an example.

Symbolic two-level minimization

Abstract: In this paper, we present a symbolic minimization procedure to obtain optimal two-level implementations of finite-state machines. Encoding based on symbolic minimization consists of optimizing the symbolic representation, and then transforming the optimized symbolic description into a compatible two-valued representation by satisfying encoding constraints (bitwise logic relations) imposed on the binary codes that replace the symbols. Our symbolic minimization procedure captures the sharing of product terms due to ORing effects in the output part of a two-level implementation of the symbolic cover. Face, dominance, and disjunctive constraints are generated. Product terms are accepted in a symbolic minimized cover only when they induce compatible encoding constraints. At the end, a set of codes that satisfy all constraints is computed. The quality of this synthesis procedure is shown by the fact that the cardinality of the cover obtained by symbolic minimization and of the cover obtained by replacing the codes in the initial cover and then minimizing it with ESPRESSO are very close. Experiments show that in some cases, our procedure improves on the best results of state-of-art tools.

A Methodology for Constraint-Driven Synthesis of On-Chip Communications

Abstract: We present a methodology and an optimization framework for the synthesis of on-chip communication through the assembly of components such as interfaces, routers, buses, and links, from a target library. Models for functionality, cost, and performance of each element are captured in the library together with their composition rules. We develop a mathematical framework to model communication at different levels of abstraction from the point-to-point input specification to the library elements and the final implementation.

Medium Access Control Analytical Modeling and Optimization in Unslotted IEEE 802.15.4 Wireless Sensor Networks

Abstract: Accurate analytical expressions of delay and packet reception probabilities, and energy consumption of duty-cycled wireless sensor networks with random medium access control (MAC) are instrumental for the efficient design and optimization of these resource-constrained networks. Given a clustered network topology with unslotted IEEE 802.15.4 and preamble sampling MAC, a novel approach to the modeling of the delay, reliability, and energy consumption is proposed. The challenging part in such a modeling is the random MAC and sleep policy of the receivers, which prevents to establish the exact time of data packet transmission. The analysis gives expressions as function of sleep time, listening time, traffic rate and MAC parameters. The analytical results are then used to optimize the duty cycle of the nodes and MAC protocol parameters. The approach provides a significant reduction of the energy consumption compared to existing solutions in the literature. Monte Carlo simulations by ns2 assess the validity of the analysis.

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.