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.

An Effective Approach for the Diagnosis of Transition-Delay Faults in SoCs, based on SBST and Scan Chains

Abstract: In this paper, a Software-Based Diagnosis (SBD) procedure suitable for SoCs is proposed to tackle the diagnosis of transition-delay faults. The illustrated methodology takes advantage of an initial Software-Based Self-Test (SBST) test set and of the scan-chains included in the final SoC design release. In principle, the proposed methodology consists in partitioning the considered SBST test set in several slices, and then proceeding to the evaluation of the diagnostic ability owned by each slice with the aim of discarding diagnosis-ineffective test programs portions. The proposed methodology is aimed to provide precise feedback to the failure analysis process focusing the systematic timing failures characteristic of new technologies. Experimental results show the effectiveness and feasibility of the proposed approach on a suitable SoC test vehicle including an 8-bit microcontroller, 4 SRAM memories and an arithmetic core, manufactured by STMicroelectronics, whose purpose is to provide precise information to the failure analysis process. The reached diagnostic resolution is up to the 99.75%, compared to the 93.14% guaranteed by the original SBST procedure.

Exploiting symbolic techniques within genetic algorithms for power optimization

Abstract: Proposes an optimization algorithm for reducing the power dissipation in a sequential circuit. The encoding of the different states in a finite-state machine is modified to obtain a functionally equivalent circuit that exhibits a reduced power dissipation. The algorithm is based on a newly-proposed power estimation function that is able to quickly give an accurate estimate of the dissipated power without actually synthesizing the circuit. Given this estimate, a genetic algorithm provides a state re-encoding for the circuit. The estimation function is computed in a very efficient way by exploiting some symbolic computations with binary decision diagrams. The algorithm is experimentally shown to provide good results from the power optimization point of view, at a limited cost in terms of area increase, when compared with similar approaches.

A PVM tool for automatic test generation on parallel and distributed systems

Abstract: The use of parallel architectures for the solution of CPU and memory critical problems in the Electronic CAD area has been limited up to now by several factors, like the lack of efficient algorithms, the reduced portability of the code, and the cost of hardware. However, portable message-passing libraries are now available, and the same code runs on high-cost supercomputers, as well as on common workstation networks. The paper presents an effective ATPG system for large sequential circuits developed using the PVM library and based on a Genetic Algorithm. The tool, named GATTO*, runs on a DEC Alpha AXP farm and a CM-5. Experimental results are provided.

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.