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.

Using the minimum description length principle to infer reduced ordered decision graphs

Abstract: We propose an algorithm for the inference of decision graphs from a set of labeled instances. In particular, we propose to infer decision graphs where the variables can only be tested in accordance with a given order and no redundant nodes exist. This type of graphs, reduced ordered decision graphs, can be used as canonical representations of Boolean functions and can be manipulated using algorithms developed for that purpose. This work proposes a local optimization algorithm that generates compact decision graphs by performing local changes in an existing graph until a minimum is reached. The algorithm uses Rissanen's minimum description length principle to control the tradeoff between accuracy in the training set and complexity of the description. Techniques for the selection of the initial decision graph and for the selection of an appropriate ordering of the variables are also presented. Experimental results obtained using this algorithm in two sets of examples are presented and analyzed.

Development of Building Automation and Control Systems

Abstract: In this paper, we proposed a design flow for BAC systems that enables integrating heterogeneous input models, conducts automatic design space exploration, and performs software synthesis on distributed platforms while guaranteeing correctness and reducing communication load. We believe these capabilities can enable the building designers to better adopt model-based design methodologies, and facilitate them to improve design productivity, optimize system performance, and reduce cost.

Efficient power estimation techniques for HW/SW systems

Abstract: We present a power estimation framework for hardware/software System-On-Chip (SOC) designs based on concurrent and synchronized execution of a hardware simulator and an instruction set simulator. Concurrent execution of the simulators for different parts of the system is necessary to obtain accurate input and execution traces, and hence accurate power estimates. However, as in the case of hardware/software co-simulation, the communication and synchronization between the various simulators causes significant overhead. We describe two speedup techniques for addressing this issue-energy caching and power macromodeling-that present interesting accuracy vs. efficiency tradeoffs.

Synthesis for manufacturability: a sanity check

Abstract: As we move towards nanometer technology, manufacturing problems become overwhelmingly difficult to solve. Presently, optimization for manufacturability is performed at a post-synthesis stage and has been shown capable of reducing manufacturing cost up to 10%. As in other cases, raising the abstraction layer where optimization is applied is expected to yield substantial gains. This paper focuses on a new approach to design for manufacturability: logic synthesis for manufacturability. This methodology consists of replacing the traditional area-driven technology mapping with a new manufacturability-driven one. We leverage existing logic synthesis tools to test our method. The results obtained by using STMicroelectronics 0.13 /spl mu/m library confirm that this approach is a promising solution for designing circuits with lower manufacturing cost, while retaining performance. Finally, we show that our synthesis for manufacturability can achieve even larger cost reduction when yield-optimized cells are added to the library, thus enabling a wider area-yield tradeoff exploration.

Quasi-Static Scheduling of Embedded Software Using Equal Conflict Nets

Abstract: Embedded system design requires the use of efficient scheduling policies to execute on shared resources, e.g. the processor, algorithms that consist of a set of concurrent tasks with complex mutual dependencies. Scheduling techniques are called static when the schedule is computed at compile time, dynamic when some or all decisions are made at run-time. The choice of the scheduling policy mainly depends on the specification of the system to be designed. For specifications containing only data computation, it is possible to use a fully static scheduling technique, while for specifications containing data-dependent control structures, like the if-then-else or while-do constructs, the dynamic behaviour of the system cannot be completely predicted at compile time and some scheduling decisions are to be made at run-time. For such applications we propose a Quasi-static scheduling (QSS) algorithm that generates a schedule in which run-time decisions are made only for data-dependent control structures. We use Equal Conflict (EC) nets as underlying model, and define quasi-static schedulability for EC nets. We solve QSS by reducing it to a decomposition of the net into conflict-free components. The proposed algorithm is complete, in that it can solve QSS for any EC net that is quasi-statically schedulable.

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.