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.

Stochastic Analysis of CAN-Based Real-Time Automotive Systems

Abstract: Many automotive applications, including most of those developed for active safety and chassis systems, must comply with hard real-time deadlines, and are also sensitive to the average latency of the end-to-end computations from sensors to actuators. A characterization of the timing behavior of functions is used to estimate the quality of an architecture configuration in the early stages of architecture selection. In this paper, we extend previous work on stochastic analysis of response times for software tasks to controller area network messages, then compose them with sampling delays to compute probability distributions of end-to-end latencies. We present the results of the analysis on a realistic complex distributed automotive system. The distributions predicted by our method are very close to the probability of latency values measured on a simulated system. However, the faster computation time of the stochastic analysis is much better suited to the architecture exploration process, allowing a much larger number of configurations to be analyzed and evaluated.

Irredundant sequential machines via optimal logic synthesis

Abstract: It is shown that optimal sequential logic synthesis can produce irredundant, fully testable finite-state machines. Synthesizing a sequential circuit from a state transition graph description involves the steps of state minimization, state assignment, and logic optimization. Previous approaches to producing fully and easily testable sequential circuits have involved the use of extra logic and constraints on state assignments and logic optimization. Here it is shown that 100% testability can be ensured without the addition of extra logic and without constraints on the state assignment and logic optimization. Unlike previous synthesis approaches to ensuring fully testable machines, there is no area/performance penalty associated with this approach. This technique can be used in conjunction with previous approaches to ensure that the synthesized machine is easily testable. Given a state-transition-graph specification, a logic-level automaton that is fully testable for all single stuck-at faults in the combinational logic without access to the memory elements is synthesized. >

Observability for hybrid systems

Abstract: The notion of generic final-state asymptotically determinable hybrid system is introduced Then, sufficient conditions for a linear hybrid system to be generic final-state asymptotically determinable are given These conditions show that generic final-state asymptotic determinability can be verified even if each of the continuous subsystems of the hybrid system is not observable More precisely, these conditions are related to the minimum and maximum sojourn time in each location as well as on the dimension and orientation of the unobservable subspaces and on the reset mappings between them

Substrate noise: analysis and optimization for IC design

Abstract: In the past decade, substrate noise has had a constant and significant impact on the design of analog and mixed-signal integrated circuits. Only recently, with advances in chip miniaturization and innovative circuit design, has substrate noise begun to plague fully digital circuits as well. To combat the effects of substrate noise, heavily over-designed structures are generally adopted, thus seriously limiting the advantages of innovative technologies. Substrate Noise: Analysis and Optimization for IC Design addresses the main problems posed by substrate noise from both an IC and a CAD designer perspective. The effects of substrate noise on performance in digital, analog, and mixed-signal circuits are presented, along with the mechanisms underlying noise generation, injection, and transport. Popular solutions to the substrate noise problem and the trade-offs often debated by designers are extensively discussed. Non-traditional approaches as well as semi-automated techniques to combat substrate noise are also addressed. Substrate Noise: Analysis and Optimization for IC Design will be of interest to researchers and professionals interested in signal integrity, as well as to mixed signal and RF designers.

Constraint-driven communication synthesis

Abstract: Constraint-driven communication synthesis enables the automatic design of the communication architecture of a complex system from a library of pre-defined intellectual property (IP) components. The key communication parameters that govern all the point-to-point interactions among system modules are captured as a set of arc constraints in the communication constraint graph. Similarly, the communication features offered by each of the components available in the IP communication library are captured as a set of feature resources together with its cost figures. Then, every communication architecture that can be built using the available components while satisfying all constraints is implicitly considered (as an implementation graph matching the constraint graph) to derive the optimum design solution with respect to the desired cost figure. The corresponding constrained optimization problem is efficiently solved by a novel algorithm that is presented here together with its rigorous theoretical foundations.

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.