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.

Methodology and Tools for Next Generation Cyber-Physical Systems: The iCyPhy Approach

Abstract: The realization of complex, cyber-physical “systems of systems” can substantially benefit from model-based hierarchical and compositional methodologies to make their design possible let alone optimal. In this paper, we introduce the methodology being developed within the industrial Cyber-Physical (iCyPhy) research consortium, which addresses the complexity and heterogeneity of cyber-physical systems by formalizing the design process in a hierarchical and compositional way, and provides a unifying framework where different modeling, analysis and synthesis tools can seamlessly interconnect. We use assume-guarantee contracts and their algebra (e.g. composition, conjunction, refinement) to provide formal support to the entire design flow. The design is carried out as a sequence of refinement steps from a high-level specification (top-down phase) to an implementation built out of a library of components at the lower level (bottom-up phase). At each step, the design is refined by combining synthesis from requirements, optimization and simulation-based design space exploration methods. We illustrate our approach on design examples of embedded controllers for aircraft power distribution and air management systems.

A One-Class Support Vector Machine Calibration Method for Time Series Change Point Detection

Abstract: It is important to identify the change point of a system's health status, which usually signifies an incipient fault under development. The One-Class Support Vector Machine (OC-SVM) is a popular machine learning model for anomaly detection and hence could be used for identifying change points; however, it is sometimes difficult to obtain a good OC-SVM model that can be used on sensor measurement time series to identify the change points in system health status. In this paper, we propose a novel approach for calibrating OC-SVM models. The approach uses a heuristic search method to find a good set of input data and hyperparameters that yield a well-performing model. Our results on the C-MAPSS dataset demonstrate that OC-SVM can also achieve satisfactory accuracy in detecting change point in time series with fewer training data, compared to state-of-the-art deep learning approaches. In our case study, the OC-SVM calibrated by the proposed model is shown to be useful especially in scenarios with limited amount of training data.

Simulation based deadlock analysis for system level designs

Abstract: In the design of highly complex, heterogeneous, and concurrent systems, deadlock detection and resolution remains an important issue. In this paper, we systematically analyze the synchronization dependencies in concurrent systems modeled in the Metropolis design environment, where system functions, high level architectures and function-architecture mappings can be modeled and simulated. We propose a data structure called the dynamic synchronization dependency graph, which captures the runtime (blocking) dependencies. A loop-detection algorithm is then used to detect deadlocks and help designers quickly isolate and identify modeling errors that cause the deadlock problems. We demonstrate our approach through a real world design example, which is a complex functional model for video processing and a high level model of function-architecture mapping.

Refinement preserving approximations for the design and verification of heterogeneous systems

Abstract: Embedded systems are electronic devices that function in the context of a real environment, by sensing and reacting to a set of stimuli. Because of their close interaction with the environment, and to simplify their design, different parts of an embedded system are best described using different notations and different techniques. In this case, we say that the system is heterogeneous. We informally refer to the notation and the rules that are used to specify and verify the elements of heterogeneous systems and their collective behavior as a model of computation. In this paper, we consider different classes of relationships between models of computation and discuss their preservation properties with respect to the model's refinement relation and composition operator. In particular, we focus on abstraction and refinement relationships in the form of abstract interpretations and introduce the notion of conservative approximation. We show that, unlike abstract interpretations, conservative approximations preserve refinement verification results from an abstract to a concrete model while avoiding false positives. We also characterize the relationship between abstract interpretations and conservative approximations, and derive necessary and sufficient conditions to obtain a conservative approximation from a pair of abstract interpretations. In addition, we use the inverse of a conservative approximation to identify components that can be used indifferently in several models, thus enabling reuse across models of computation. The concepts described in this paper are illustrated with examples from continuous time and discrete time models of computation.

Structural detection of symmetries in Boolean functions

Abstract: Functional symmetries provide significant benefits for multiple tasks in synthesis and verification. Many applications require the manual specification of symmetries using special language features such as symmetric data types. Methods for automatically detecting symmetries are based on functional analysis, e.g. using BDDs, or structural methods. The latter search for circuit graph automorphisms which imply functional symmetry. We present a method for finding symmetries of Boolean functions based on a two-step approach. First, the circuit structure is modified to maximize its structural regularity and thus the number of inherent automorphisms. The next step implements a fast algorithm for detecting the automorphism generators of the circuit graph. The generators provide a compact representation of all automorphisms, which in turn encode a subset of the functional symmetries. Because of its pure structural nature, our approach avoids the complexity issues inherent to methods using BDDs, yet it still works automatically and independently from the input specification format. However, the described method may not detect all functional symmetries, however, our experiments demonstrate that it can find the majority of the symmetries present in practical circuits.

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.