: 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.

Multiobjective evolutionary algorithms: classifications, analyses, and new innovations

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.

Algorithms for VLSI Physical Design Automation

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.

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

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.

A Taxonomy of Hybrid Metaheuristics

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.

Interpolation and SAT-based model checking

As microprocessors are increasingly used in safety-critical applications, there is a growing demand for effective fault-tolerance techniques that can mitigate the effects of soft errors while reducing intrusiveness and minimizing the impact on performance and power consumption To this purpose, approaches that are based on monitoring the microprocessor operation through an external interface in a non-intrusive manner have recently been proposed In this paper we focus on the use of the trace interface for on-line monitoring This interface provides detailed information about the instructions executed by the processor and can be reused to support error detection and correction in several ways, including multi-processors in hardware redundancy, time redundancy and control-flow checking

Fault-Tolerance Techniques for Soft-Core Processors Using the Trace Interface

A pipelined processor with a high-level behavioral HDL description is presented in this paper. It generates a set of effective test programs by using a simulator, which is able to evaluate with respect to an RTL coverage metric. The proposed optimizer is based on a technique called microGP, an evolutionary system able to automatically device and optimizes the program written in an assembly language. Quantitative coverage measurement presented will guide the test-program generation. The approach is fully automatic and broadly applicable. The minimal test set with the programmable coverage is attained.

Automatic test generation for verifying microprocessors

Graphics Processing Units (GPUs) are increasingly adopted in several domains where reliability is fundamental, such as self-driving cars and autonomous systems. Unfortunately, GPU devices have been shown to have a high error rate, while the constraints imposed by real-time safety-critical applications make traditional (and costly) replication-based hardening solutions inadequate. This work proposes an effective methodology to identify the architectural vulnerable sites in GPUs modules, i.e., the locations that, if corrupted, most affect the correct instructions execution. We first identify, through an innovative method based on Register-Transfer Level (RTL) fault injection experiments, the architectural vulnerabilities of a GPU model. Then, we mitigate the fault impact via selective hardening applied to the flip-flops that have been identified as critical. We evaluate three hardening strategies: Triple Modular Redundancy (TMR), Triple Modular Redundancy against SETs ( $\Delta $ TMR), and Dual Interlocked Storage Cells (Dice flip-flops). The results gathered on a publicly available GPU Model (FlexGripPlus) considering functional units, pipeline registers, and warp scheduler controller show that our method can tolerate from 85% to 99% of faults in the pipeline registers, from 50% to 100% of faults in the functional units and up to 10% of faults in the warp scheduler, with a reduced hardware overhead (in the range of 58% to 94% when compared with traditional TMR). Finally, we adapt the methodology to perform a complementary evaluation targeting permanent faults and identify critical sites prone to propagate fault effects across the GPU. We found that a considerable percentage (65% to 98%) of flip-flops that are critical for transient faults are also critical for permanent faults.

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An Effective Method to Identify Microarchitectural Vulnerabilities in GPUs

High power consumption during test may lead to yield loss and premature aging. In particular, excessive peak power consumption during at-speed delay fault testing represents an important issue. In the literature, several techniques have been proposed to reduce peak power consumption during at-speed LOC or LOS delay testing. On the other side, limiting too much the power consumption during test may reduce the defect coverage. Hence, techniques for identifying upper and lower functional power limits are crucial for delay fault testing. Yet, the task of computing the maximum functional peak power achievable by CPU cores is challenging, since the functional patterns with maximum peak power depend on specific instruction execution order and operands. In this paper, we present a methodology combining neural networks and evolutionary computing for quickly estimating peak power consumption. The method is used within an algorithm for automatic functional program generation used to identify test programs with maximal functional peak power consumption, which are suitable for defining peak power limits under test. The proposed approach was applied on the Intel 8051 CPU core synthesized with a 65 nm industrial technology reducing significant time with respect to old methods.

Matteo Sonza Reorda

Papers

Fault-Tolerance Techniques for Soft-Core Processors Using the Trace Interface

Automatic test generation for verifying microprocessors

An Effective Method to Identify Microarchitectural Vulnerabilities in GPUs

Fast Power Evaluation for Effective Generation of Test Programs Maximizing Peak Power Consumption

A P1500 compliant architecture for BIST-based Diagnosis of embedded RAMs