Error Mitigation Using Approximate Logic Circuits: A Comparison of Probabilistic and Evolutionary Approaches

Technology scaling poses an increasing challenge to the reliability of digital circuits. Hardware redundancy solutions, such as triple modular redundancy (TMR), produce very high area overhead, so partial redundancy is often used to reduce the overheads. Approximate logic circuits provide a general framework for optimized mitigation of errors arising from a broad class of failure mechanisms, including transient, intermittent, and permanent failures. However, generating an optimal redundant logic circuit that is able to mask the faults with the highest probability while minimizing the area overheads is a challenging problem. In this study, we propose and compare two new approaches to generate approximate logic circuits to be used in a TMR schema. The probabilistic approach approximates a circuit in a greedy manner based on a probabilistic estimation of the error. The evolutionary approach can provide radically different solutions that are hard to reach by other methods. By combining these two approaches, the solution space can be explored in depth. Experimental results demonstrate that the evolutionary approach can produce better solutions, but the probabilistic approach is close. On the other hand, these approaches provide much better scalability than other existing partial redundancy techniques.This work was supported by the Ministry of Economy and Competitiveness of Spain under project ESP2015-68245-C4-1-P, and by the Czech science foundation project GA16-17538S and the Ministry of Education, Youth and Sports of the Czech Republic from the National Programme of Sustainability (NPU II); project IT4Innovations excellence in science - LQ1602

An investigation of defect detection using random defect excitation and deterministic defect observation in complex integrated logic circuits

Due to the character of the original source materials and the nature of batch digitization, quality control issues may be present in this document. Please report any quality issues you encounter to [email protected], referencing the URI of the item.Includes bibliographical references: leaves 22-23.aWhenever integrated circuits are manufactured, a certain percentage of those circuits will be defective. Defective circuits present problems for both the manufacturers who wish to maintain a good reputation with their customers and the consumers who depend upon the correct operation of the products they buy. Thus, testing must be done to detect which parts are defective so that they are not sold to unwitting consumers. Most current testing methods involve generating test patterns that will detect single stuck-at faults. Unfortunately, however, the single stuck-at fault model cannot adequately describe all of the potential defects that may occur. The requirements for exciting a fault vary depending upon the specific model (stuck-at, bridge, etc.) being used, but the observation of the fault always requires that the erroneous logic value be propagated to a primary output. The proposed new method of automatic test pattern generation involves deterministically observing all of the sites in the circuit as many times as possible while randomly exciting the defects which may occur. This research demonstrates the importance of site observation on the detection of defects and shows some of the inefficiencies and shortcomings of the current stuck-at fault ATPG

Fault Diagnosis Of Sensor And Actuator Faults In Multi-Zone Hvac Systems

Globally, the buildings sector accounts for 30% of the energy consumption and more than 55% of the electricity demand. Specifically, the Heating, Ventilation, and Air Conditioning (HVAC) system is the most extensively operated component and it is responsible alone for 40% of the final building energy usage. HVAC systems are used to provide healthy and comfortable indoor conditions, and their main objective is to maintain the thermal comfort of occupants with minimum energy usage. HVAC systems include a considerable number of sensors, controlled actuators, and other components. They are at risk of malfunctioning or failure resulting in reduced efficiency, potential interference with the execution of supervision schemes, and equipment deterioration. Hence, Fault Diagnosis (FD) of HVAC systems is essential to improve their reliability, efficiency, and performance, and to provide preventive maintenance. In this thesis work, two neural network-based methods are proposed for sensor and actuator faults in a 3-zone HVAC system. For sensor faults, an online semi-supervised sensor data validation and fault diagnosis method using an Auto-Associative Neural Network (AANN) is developed. The method is based on the implementation of Nonlinear Principal Component Analysis (NPCA) using a Back-Propagation Neural Network (BPNN) and it demonstrates notable capability in sensor fault and inaccuracy correction, measurement noise reduction, missing sensor data replacement, and in both single and multiple sensor faults diagnosis. In addition, a novel on-line supervised multi-model approach for actuator fault diagnosis using Convolutional Neural Networks (CNNs) is developed for single actuator faults. It is based a data transformation in which the 1-dimensional data are configured into a 2-dimensional representation without the use of advanced signal processing techniques. The CNN-based actuator fault diagnosis approach demonstrates improved performance capability compared with the commonly used Machine Learning-based algorithms (i.e., Support Vector Machine and standard Neural Networks). The presented schemes are compared with other commonly used HVAC fault diagnosis methods for benchmarking and they are proven to be superior, effective, accurate, and reliable. The proposed approaches can be applied to large-scale buildings with additional zones

BDD and DNF based algorithms for constructing all testability functions of combinational circuit

Constructing testability functions of a combinational circuit line, such as: the controllability, observability and stuck-at fault detection functions, as well as the complement of the observability function is considered. Methods and algorithms for constructing testability functions based on Binary Decision Diagram (BDD) and Disjunctive Normal Form (DNF), as well as methods for constructing Conjunctive Normal Form (CNF) and obtaining testability functions using a SAT solver are proposed. Methods and algorithms for constructing testability functions for all and a subset of lines of a circuit are also proposed. Proposed methods and algorithms make it possible to significantly reduce the computational costs for constructing testability functions of a combinational circuit

Computational intelligence techniques for HVAC systems: a review

Buildings are responsible for 40% of global energy use and contribute towards 30% of the total CO2 emissions. The drive to reduce energy use and associated greenhouse gas emissions from buildings has acted as a catalyst in the development of advanced computational methods for energy efficient design, management and control of buildings and systems. Heating, ventilation and air conditioning (HVAC) systems are the major source of energy consumption in buildings and an ideal candidate for substantial reductions in energy demand. Significant advances have been made in the past decades on the application of computational intelligence (CI) techniques for HVAC design, control, management, optimization, and fault detection and diagnosis. This article presents a comprehensive and critical review on the theory and applications of CI techniques for prediction, optimization, control and diagnosis of HVAC systems.The analysis of trends reveals the minimization of energy consumption was the key optimization objective in the reviewed research, closely followed by the optimization of thermal comfort, indoor air quality and occupant preferences. Hardcoded Matlab program was the most widely used simulation tool, followed by TRNSYS, EnergyPlus, DOE–2, HVACSim+ and ESP–r. Metaheuristic algorithms were the preferred CI method for solving HVAC related problems and in particular genetic algorithms were applied in most of the studies. Despite the low number of studies focussing on MAS, as compared to the other CI techniques, interest in the technique is increasing due to their ability of dividing and conquering an HVAC optimization problem with enhanced overall performance. The paper also identifies prospective future advancements and research directions

Detection of hard-to-detect stuck-at faults and generation of their tests based on testability functions

An efficient method is proposed for detecting hard- to-detect stuck-at faults of combinational circuits and generating all tests or one test vector for them. The method is based on the previously proposed efficient methods of constructing the ODNF and ROBDD representations of the observability and stuck-at fault detection Boolean functions, corresponding to a line of the combinational circuit

Experimental analysis of computer system dependability

This paper reviews an area which has evolved over the past 15 years: experimental analysis of computer system dependability. Methodologies and advances are discussed for three basic approaches used in the area: simulated fault injection, physical fault injection, and measurement-based analysis. The three approaches are suited, respectively, to dependability evaluation in the three phases of a system's life: design phase, prototype phase, and operational phase. Before the discussion of these phases, several statistical techniques used in the area are introduced. For each phase, a classification of research methods or study topics is outlined, followed by discussion of these methods or topics as well as representative studies. The statistical techniques introduced include the estimation of parameters and confidence intervals, probability distribution characterization, and several multivariate analysis methods. Importance sampling, a statistical technique used to accelerate Monte Carlo simulation, is also introduced. The discussion of simulated fault injection covers electrical-level, logic-level, and function-level fault injection methods as well as representative simulation environments such as FOCUS and DEPEND. The discussion of physical fault injection covers hardware, software, and radiation fault injection methods as well as several software and hybrid tools including FIAT, FERARI, HYBRID, and FINE. The discussion of measurement-based analysis covers measurement and data processing techniques, basic error characterization, dependency analysis, Markov reward modeling, software-dependability, and fault diagnosis. The discussion involves several important issues studies in the area, including fault models, fast simulation techniques, workload/failure dependency, correlated failures, and software fault tolerance

Efficient Path Delay Test Generation with Boolean Satisfiability

This dissertation focuses on improving the accuracy and efficiency of path delay test generation using a Boolean satisfiability (SAT) solver. As part of this research, one of the most commonly used SAT solvers, MiniSat, was integrated into the path delay test generator CodGen. A mixed structural-functional approach was implemented in CodGen where longest paths were detected using the K Longest Path Per Gate (KLPG) algorithm and path justification and dynamic compaction were handled with the SAT solver. Advanced techniques were implemented in CodGen to further speed up the performance of SAT based path delay test generation using the knowledge of the circuit structure. SAT solvers are inherently circuit structure unaware, and significant speedup can be availed if structure information of the circuit is provided to the SAT solver. The advanced techniques explored include: Dynamic SAT Solving (DSS), Circuit Observability Don’t Care (Cir-ODC), SAT based static learning, dynamic learnt clause management and Approximate Observability Don’t Care (ACODC). Both ISCAS 89 and ITC 99 benchmarks as well as industrial circuits were used to demonstrate that the performance of CodGen was significantly improved with MiniSat and the use of circuit structure