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 Control Using Bisimulations: Implementation

Abstract: We consider the synthesis of optimal controls for continuous feedback systems by recasting the problem to a hybrid optimal control problem which is to synthesize optimal enabling conditions for switching between locations in which the control is constant. We provide a single-pass algorithm to solve the dynamic programming problem that arises, with added constraints to ensure non-Zeno trajectories.

A note on bipartite graphs and pivot selection in sparse matrices

Abstract: In this note a bipartite graph representation is proposed for the study of pivot strategies on sparse matrices Using this representation, an algorithm which fullfills the Brayton's condition for Gaussian elimination optimality has been devised

Fast instruction cache simulation strategies in a hardware/software co-design environment

Abstract: Cache memories are one of the main factors that affect software performance, and their use is becoming increasingly common even in embedded systems. Efficient analysis of the effects of parameter variations (cache dimensions, degree of associativity, replacement policy, line size, ...) is at the same time an essential and very time-consuming aspect of embedded system design, whose complexity increases when multi-tasking and real-time aspects must be considered. We propose a new simulation-based methodology, focused on an approximate model of the cache and of the multi-tasking reactive software, that allows one to trade off smoothly between accuracy and simulation speed. In particular, we propose to accurately consider intra-task conflicts, but approximate inter-task conflicts by considering only a finite number of previous task executions. The rationale for this choice can be found in a common pattern in embedded systems, where a "normal" data flow results in a regular intra-task common flow, interrupted from time to time by some urgent event, that pessimistically can be consider as disrupting the cache behavior. The approach is conservative because re-execution of a task after a large amount of time will always be considered as not in cache, and the simulation speed-up is considerable, as shown by theoretical analysis and experimental results.

Analog behavioral models for simulation and synthesis of mixed-signal systems

Abstract: A behavioral simulator is shown to be an essential part of a performance-driven hierarchical top-down design strategy for analog blocks within mixed-signal integrated systems. It is used to accurately estimate the performance of the system while down-mapping the specifications over the hierarchy, in order to avoid time-consuming design iterations. It is also indispensable for the final bottom-up verification after completion of the design, as well as for testing purposes. The authors describe the set-up of the generic behavioral models for this simulator, which describe the functional behavior of the analog blocks, independent of the internal architecture. In addition to the nominal behavior, the models also include the important second-order effects (nonidealities, noise, distortion . . .) as well as the statistical variations of most parameters. This is then illustrated in detail for the statistical minimum-rank model of a Nyquist-rate A/D converter. System-level applications show the effectiveness and accuracy of this model. >

Rapid-Prototyping of Embedded Systems via Reprogrammable Devices

Abstract: This paper describes a flexible board-level rapid-prototyping environment for embedded control applications. The environment is based on an APTIX board populated by Xilinx FPGA devices, a 68HC11 emulator, and APTIX programmable interconnect devices. Given a design consisting of logic and of software running on a micro-controller that implement a set of tasks, the prototype is obtained by programming the FPGA devices, the micro-controller emulator and the APTIX devices. This environment being based on programmable devices offers the flexibility to perform engineering changes, the performance needed to validate complex systems and the hardware setup for field tests. The key point in our approach is the use of results of our previous research on software and hardware synthesis as well as of some commercial tools to provide the designer with fast programming data from a high-level description of the algorithms to be implemented. We demonstrate the effectiveness of the approach by showing a close-to real-life example from the automotive world.

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.