Matteo Sonza Reorda

Bio: Matteo Sonza Reorda is an academic researcher from Polytechnic University of Turin. The author has contributed to research in topics: Fault coverage & Automatic test pattern generation. The author has an hindex of 32, co-authored 295 publications receiving 4525 citations. Previous affiliations of Matteo Sonza Reorda include University of California, Riverside & NXP Semiconductors.

A software-based methodology for the generation of peripheral test sets based on high-level descriptions

Abstract: Nowadays, the use of Systems-on-Chip (SoCs) represents a very interesting solution, but also introduces some testing concerns. Up to now, researchers focused many efforts on the development of new software and hardware techniques for testing processors embedded in SoCs. However, the test of the surrounding peripherals has not been the subject of many research works, even if their importance within the entire system may be considerable. In this paper we focus on Software-based Self-Test techniques for testing peripheral components within a SoC and explore the possibility that test generation only relies on high-level metrics. We outline a possible test generation and application flow, and discuss the suitability of different RT-level metrics. By exploiting a sample case study, we quantitatively evaluate the effectiveness of the different metrics and the practical viability of the considered approach. As a major contribution, the paper shows that for peripheral components the relationship between high-level and gate-level metrics is higher than for the general case.

Reliability Assessment of Neural Networks in GPUs: A Framework For Permanent Faults Injections

Abstract: Currently, Deep learning and especially Convolutional Neural Networks (CNNs) have become a fundamental computational approach applied in a wide range of domains, including some safety-critical applications (e.g., automotive, robotics, and healthcare equipment). Therefore, the reliability evaluation of those computational systems is mandatory. The reliability evaluation of CNNs is performed by fault injection campaigns at different levels of abstraction, from the application level down to the hardware level. Many works have focused on evaluating the reliability of neural networks in the presence of transient faults. However, the effects of permanent faults have been investigated at the application level, only, e.g., targeting the parameters of the network. This paper intends to propose a framework, resorting to a binary instrumentation tool to perform fault injection campaigns, targeting different components inside the GPU, such as the register files and the functional units. This environment allows for the first time assessing the reliability of CNNs deployed on a GPU considering the presence of permanent faults.

Fault Injection for Embedded Microprocessor-based Systems

Abstract: Microprocessor-based embedded systems are increasingly used to control safety- critical systems (e.g., air and railway traffic control, nuclear plant control, aircraft and car control). In this case, fault tolerance mechanisms are introduced at the hardware and software level. Debugging and verifying the correct design and implementation of these mechanisms ask for effective environments, and Fault Injection represents a viable solution for their implementation. In this paper we present a Fault Injection environment, named FlexFI, suitable to assess the correctness of the design and implementation of the hardware and software mechanisms existing in embedded microprocessor-based systems, and to compute the fault coverage they provide. The paper describes and analyzes different solutions for implementing the most critical modules, which differ in terms of cost, speed, and intrusiveness in the original

Multiobjective evolutionary algorithms: classifications, analyses, and new innovations

Abstract: : This research organizes, presents, and analyzes contemporary Multiobjective Evolutionary Algorithm (MOEA) research and associated Multiobjective Optimization Problems (MOPs). Using a consistent MOEA terminology and notation, each cited MOEAs' key factors are presented in tabular form for ease of MOEA identification and selection. A detailed quantitative and qualitative MOEA analysis is presented, providing a basis for conclusions about various MOEA-related issues. The traditional notion of building blocks is extended to the MOP domain in an effort to develop more effective and efficient MOEAs. Additionally, the MOEA community's limited test suites contain various functions whose origins and rationale for use are often unknown. Thus, using general test suite guidelines appropriate MOEA test function suites are substantiated and generated. An experimental methodology incorporating a solution database and appropriate metrics is offered as a proposed evaluation framework allowing absolute comparisons of specific MOEA approaches. Taken together, this document's classifications, analyses, and new innovations present a complete, contemporary view of current MOEA "state of the art" and possible future research. Researchers with basic EA knowledge may also use part of it as a largely self-contained introduction to MOEAs.

Algorithms for VLSI Physical Design Automation

Abstract: From the Publisher: This work covers all aspects of physical design. The book is a core reference for graduate students and CAD professionals. For students, concept and algorithms are presented in an intuitive manner. For CAD professionals, the material presents a balance of theory and practice. An extensive bibliography is provided which is useful for finding advanced material on a topic. At the end of each chapter, exercises are provided, which range in complexity from simple to research level.

Genetic Algorithms for the Travelling Salesman Problem: A Review of Representations and Operators

Abstract: This paper is the result of a literature study carried out by the authors. It is a review of the different attempts made to solve the Travelling Salesman Problem with Genetic Algorithms. We present crossover and mutation operators, developed to tackle the Travelling Salesman Problem with Genetic Algorithms with different representations such as: binary representation, path representation, adjacency representation, ordinal representation and matrix representation. Likewise, we show the experimental results obtained with different standard examples using combination of crossover and mutation operators in relation with path representation.

A Taxonomy of Hybrid Metaheuristics

Abstract: Hybrid metaheuristics have received considerable interest these recent years in the field of combinatorial optimization. A wide variety of hybrid approaches have been proposed in the literature. In this paper, a taxonomy of hybrid metaheuristics is presented in an attempt to provide a common terminology and classification mechanisms. The taxonomy, while presented in terms of metaheuristics, is also applicable to most types of heuristics and exact optimization algorithms. As an illustration of the usefulness of the taxonomy an annoted bibliography is given which classifies a large number of hybrid approaches according to the taxonomy.

Interpolation and SAT-based model checking

Abstract: We consider a fully SAT-based method of unbounded symbolic model checking based on computing Craig interpolants. In benchmark studies using a set of large industrial circuit verification instances, this method is greatly more efficient than BDD-based symbolic model checking, and compares favorably to some recent SAT-based model checking methods on positive instances.