An Experimental Study Of Combinational Logic Circuit Minimization Using Firefly Algorithm

Combinatorial logic circuit minimization is usually done using Karnaugh’s Map or Boolean equation. This paper presents an application of Firefly Algorithm to design combinational logic circuit in which the objective function is to minimize the total number of gates used. Then, the algorithm is benchmarked with other literatures. Result indicates that it able to find optimal solution but further analysis is required for a more complex combinatorial logic circuit minimization

Gene expression programming for logic circuit design

Finding an optimal solution for the logic circuit design problem is challenging and time-consuming especially for complex logic circuits. As the number of logic gates increases the task of designing optimal logic circuits extends beyond human capability. A number of evolutionary algorithms have been invented to tackle a range of optimisation problems, including logic circuit design. This dissertation explores two of these evolutionary algorithms i.e. Gene Expression Programming (GEP) and Multi Expression Programming (MEP) with the aim of integrating their strengths into a new Genetic Programming (GP) algorithm. GEP was invented by Candida Ferreira in 1999 and published in 2001 [8]. The GEP algorithm inherits the advantages of the Genetic Algorithm (GA) and GP, and it uses a simple encoding method to solve complex problems [6, 32]. While GEP emerged as powerful due to its simplicity in implementation and exibility in genetic operations, it is not without weaknesses. Some of these inherent weaknesses are discussed in [1, 6, 21]. Like GEP, MEP is a GP-variant that uses linear chromosomes of xed length [23]. A unique feature of MEP is its ability to store multiple solutions of a problem in a single chromosome. MEP also has an ability to implement code-reuse which is achieved through its representation which allow multiple references to a single sub-structure. This dissertation proposes a new GP algorithm, Improved Gene Expression Programming (IGEP) which im- proves the performance of the traditional GEP by combining the code-reuse capability and simplicity of gene encoding method from MEP and GEP, respectively. The results obtained using the IGEP and the traditional GEP show that the two algorithms are comparable in terms of the success rate when applied on simple problems such as basic logic functions. However, for complex problems such as one-bit Full Adder (FA) and AND-OR Arithmetic Logic Unit (ALU) the IGEP performs better than the traditional GEP due to the code-reuse in IGEPMathematical SciencesM. Sc. (Applied Mathematics

On two-way communication in cellular automata with a fixed number of cells

The effect of adding two-way communication to k cells one-way cellular automata (kC-OCAs) on their size of description is studied. kC-OCAs are a parallel model for the regular languages that consists of an array of k identical deterministic finite automata (DFAs), called cells, operating in parallel. Each cell gets information from its right neighbor only. In this paper, two models with different amounts of two-way communication are investigated. Both models always achieve quadratic savings when compared to DFAs. When compared to a one-way cellular model, the result is that minimum two-way communication can achieve at most quadratic savings whereas maximum two-way communication may provide savings bounded by a polynomial of degree k

Transient error mitigation by means of approximate logic circuits

Mención Internacional en el título de doctorThe technological advances in the manufacturing of electronic circuits have allowed to greatly improve their performance, but they have also increased the sensitivity of electronic devices to radiation-induced errors. Among them, the most common effects are the SEEs, i.e., electrical perturbations provoked by the strike of high-energy particles, which may modify the internal state of a memory element (SEU) or generate erroneous transient pulses (SET), among other effects. These events pose a threat for the reliability of electronic circuits, and therefore fault-tolerance techniques must be applied to deal with them. The most common fault-tolerance techniques are based in full replication (DWC or TMR). These techniques are able to cover a wide range of failure mechanisms present in electronic circuits. However, they suffer from high overheads in terms of area and power consumption. For this reason, lighter alternatives are often sought at the expense of slightly reducing reliability for the least critical circuit sections. In this context a new paradigm of electronic design is emerging, known as approximate computing, which is based on improving the circuit performance in change of slight modifications of the intended functionality. This is an interesting approach for the design of lightweight fault-tolerant solutions, which has not been yet studied in depth. The main goal of this thesis consists in developing new lightweight fault-tolerant techniques with partial replication, by means of approximate logic circuits. These circuits can be designed with great flexibility. This way, the level of protection as well as the overheads can be adjusted at will depending on the necessities of each application. However, finding optimal approximate circuits for a given application is still a challenge. In this thesis a method for approximate circuit generation is proposed, denoted as fault approximation, which consists in assigning constant logic values to specific circuit lines. On the other hand, several criteria are developed to generate the most suitable approximate circuits for each application, by using this fault approximation mechanism. These criteria are based on the idea of approximating the least testable sections of circuits, which allows reducing overheads while minimising the loss of reliability. Therefore, in this thesis the selection of approximations is linked to testability measures. The first criterion for fault selection developed in this thesis uses static testability measures. The approximations are generated from the results of a fault simulation of the target circuit, and from a user-specified testability threshold. The amount of approximated faults depends on the chosen threshold, which allows to generate approximate circuits with different performances. Although this approach was initially intended for combinational circuits, an extension to sequential circuits has been performed as well, by considering the flip-flops as both inputs and outputs of the combinational part of the circuit. The experimental results show that this technique achieves a wide scalability, and an acceptable trade-off between reliability versus overheads. In addition, its computational complexity is very low. However, the selection criterion based in static testability measures has some drawbacks. Adjusting the performance of the generated approximate circuits by means of the approximation threshold is not intuitive, and the static testability measures do not take into account the changes as long as faults are approximated. Therefore, an alternative criterion is proposed, which is based on dynamic testability measures. With this criterion, the testability of each fault is computed by means of an implication-based probability analysis. The probabilities are updated with each new approximated fault, in such a way that on each iteration the most beneficial approximation is chosen, that is, the fault with the lowest probability. In addition, the computed probabilities allow to estimate the level of protection against faults that the generated approximate circuits provide. Therefore, it is possible to generate circuits which stick to a target error rate. By modifying this target, circuits with different performances can be obtained. The experimental results show that this new approach is able to stick to the target error rate with reasonably good precision. In addition, the approximate circuits generated with this technique show better performance than with the approach based in static testability measures. In addition, the fault implications have been reused too in order to implement a new type of logic transformation, which consists in substituting functionally similar nodes. Once the fault selection criteria have been developed, they are applied to different scenarios. First, an extension of the proposed techniques to FPGAs is performed, taking into account the particularities of this kind of circuits. This approach has been validated by means of radiation experiments, which show that a partial replication with approximate circuits can be even more robust than a full replication approach, because a smaller area reduces the probability of SEE occurrence. Besides, the proposed techniques have been applied to a real application circuit as well, in particular to the microprocessor ARM Cortex M0. A set of software benchmarks is used to generate the required testability measures. Finally, a comparative study of the proposed approaches with approximate circuit generation by means of evolutive techniques have been performed. These approaches make use of a high computational capacity to generate multiple circuits by trial-and-error, thus reducing the possibility of falling into local minima. The experimental results demonstrate that the circuits generated with evolutive approaches are slightly better in performance than the circuits generated with the techniques here proposed, although with a much higher computational effort. In summary, several original fault mitigation techniques with approximate logic circuits are proposed. These approaches are demonstrated in various scenarios, showing that the scalability and adaptability to the requirements of each application are their main virtuesLos avances tecnológicos en la fabricación de circuitos electrónicos han permitido mejorar en gran medida sus prestaciones, pero también han incrementado la sensibilidad de los mismos a los errores provocados por la radiación. Entre ellos, los más comunes son los SEEs, perturbaciones eléctricas causadas por el impacto de partículas de alta energía, que entre otros efectos pueden modificar el estado de los elementos de memoria (SEU) o generar pulsos transitorios de valor erróneo (SET). Estos eventos suponen un riesgo para la fiabilidad de los circuitos electrónicos, por lo que deben ser tratados mediante técnicas de tolerancia a fallos. Las técnicas de tolerancia a fallos más comunes se basan en la replicación completa del circuito (DWC o TMR). Estas técnicas son capaces de cubrir una amplia variedad de modos de fallo presentes en los circuitos electrónicos. Sin embargo, presentan un elevado sobrecoste en área y consumo. Por ello, a menudo se buscan alternativas más ligeras, aunque no tan efectivas, basadas en una replicación parcial. En este contexto surge una nueva filosofía de diseño electrónico, conocida como computación aproximada, basada en mejorar las prestaciones de un diseño a cambio de ligeras modificaciones de la funcionalidad prevista. Es un enfoque atractivo y poco explorado para el diseño de soluciones ligeras de tolerancia a fallos. El objetivo de esta tesis consiste en desarrollar nuevas técnicas ligeras de tolerancia a fallos por replicación parcial, mediante el uso de circuitos lógicos aproximados. Estos circuitos se pueden diseñar con una gran flexibilidad. De este forma, tanto el nivel de protección como el sobrecoste se pueden regular libremente en función de los requisitos de cada aplicación. Sin embargo, encontrar los circuitos aproximados óptimos para cada aplicación es actualmente un reto. En la presente tesis se propone un método para generar circuitos aproximados, denominado aproximación de fallos, consistente en asignar constantes lógicas a ciertas líneas del circuito. Por otro lado, se desarrollan varios criterios de selección para, mediante este mecanismo, generar los circuitos aproximados más adecuados para cada aplicación. Estos criterios se basan en la idea de aproximar las secciones menos testables del circuito, lo que permite reducir los sobrecostes minimizando la perdida de fiabilidad. Por tanto, en esta tesis la selección de aproximaciones se realiza a partir de medidas de testabilidad. El primer criterio de selección de fallos desarrollado en la presente tesis hace uso de medidas de testabilidad estáticas. Las aproximaciones se generan a partir de los resultados de una simulación de fallos del circuito objetivo, y de un umbral de testabilidad especificado por el usuario. La cantidad de fallos aproximados depende del umbral escogido, lo que permite generar circuitos aproximados con diferentes prestaciones. Aunque inicialmente este método ha sido concebido para circuitos combinacionales, también se ha realizado una extensión a circuitos secuenciales, considerando los biestables como entradas y salidas de la parte combinacional del circuito. Los resultados experimentales demuestran que esta técnica consigue una buena escalabilidad, y unas prestaciones de coste frente a fiabilidad aceptables. Además, tiene un coste computacional muy bajo. Sin embargo, el criterio de selección basado en medidas estáticas presenta algunos inconvenientes. No resulta intuitivo ajustar las prestaciones de los circuitos aproximados a partir de un umbral de testabilidad, y las medidas estáticas no tienen en cuenta los cambios producidos a medida que se van aproximando fallos. Por ello, se propone un criterio alternativo de selección de fallos, basado en medidas de testabilidad dinámicas. Con este criterio, la testabilidad de cada fallo se calcula mediante un análisis de probabilidades basado en implicaciones. Las probabilidades se actualizan con cada nuevo fallo aproximado, de forma que en cada iteración se elige la aproximación más favorable, es decir, el fallo con menor probabilidad. Además, las probabilidades calculadas permiten estimar la protección frente a fallos que ofrecen los circuitos aproximados generados, por lo que es posible generar circuitos que se ajusten a una tasa de fallos objetivo. Modificando esta tasa se obtienen circuitos aproximados con diferentes prestaciones. Los resultados experimentales muestran que este método es capaz de ajustarse razonablemente bien a la tasa de fallos objetivo. Además, los circuitos generados con esta técnica muestran mejores prestaciones que con el método basado en medidas estáticas. También se han aprovechado las implicaciones de fallos para implementar un nuevo tipo de transformación lógica, consistente en sustituir nodos funcionalmente similares. Una vez desarrollados los criterios de selección de fallos, se aplican a distintos campos. En primer lugar, se hace una extensión de las técnicas propuestas para FPGAs, teniendo en cuenta las particularidades de este tipo de circuitos. Esta técnica se ha validado mediante experimentos de radiación, los cuales demuestran que una replicación parcial con circuitos aproximados puede ser incluso más robusta que una replicación completa, ya que un área más pequeña reduce la probabilidad de SEEs. Por otro lado, también se han aplicado las técnicas propuestas en esta tesis a un circuito de aplicación real, el microprocesador ARM Cortex M0, utilizando un conjunto de benchmarks software para generar las medidas de testabilidad necesarias. Por ´último, se realiza un estudio comparativo de las técnicas desarrolladas con la generación de circuitos aproximados mediante técnicas evolutivas. Estas técnicas hacen uso de una gran capacidad de cálculo para generar múltiples circuitos mediante ensayo y error, reduciendo la posibilidad de caer en algún mínimo local. Los resultados confirman que, en efecto, los circuitos generados mediante técnicas evolutivas son ligeramente mejores en prestaciones que con las técnicas aquí propuestas, pero con un coste computacional mucho mayor. En definitiva, se proponen varias técnicas originales de mitigación de fallos mediante circuitos aproximados. Se demuestra que estas técnicas tienen diversas aplicaciones, haciendo de la flexibilidad y adaptabilidad a los requisitos de cada aplicación sus principales virtudes.Programa Oficial de Doctorado en Ingeniería Eléctrica, Electrónica y AutomáticaPresidente: Raoul Velazco.- Secretario: Almudena Lindoso Muñoz.- Vocal: Jaume Segura Fuste

A Field Guide to Genetic Programming

xiv, 233 p. : il. ; 23 cm.Libro ElectrónicoA Field Guide to Genetic Programming (ISBN 978-1-4092-0073-4) is an introduction to genetic programming (GP). GP is a systematic, domain-independent method for getting computers to solve problems automatically starting from a high-level statement of what needs to be done. Using ideas from natural evolution, GP starts from an ooze of random computer programs, and progressively refines them through processes of mutation and sexual recombination, until solutions emerge. All this without the user having to know or specify the form or structure of solutions in advance. GP has generated a plethora of human-competitive results and applications, including novel scientific discoveries and patentable inventions. The authorsIntroduction -- Representation, initialisation and operators in Tree-based GP -- Getting ready to run genetic programming -- Example genetic programming run -- Alternative initialisations and operators in Tree-based GP -- Modular, grammatical and developmental Tree-based GP -- Linear and graph genetic programming -- Probalistic genetic programming -- Multi-objective genetic programming -- Fast and distributed genetic programming -- GP theory and its applications -- Applications -- Troubleshooting GP -- Conclusions.Contents xi 1 Introduction 1.1 Genetic Programming in a Nutshell 1.2 Getting Started 1.3 Prerequisites 1.4 Overview of this Field Guide I Basics 2 Representation, Initialisation and GP 2.1 Representation 2.2 Initialising the Population 2.3 Selection 2.4 Recombination and Mutation Operators in Tree-based 3 Getting Ready to Run Genetic Programming 19 3.1 Step 1: Terminal Set 19 3.2 Step 2: Function Set 20 3.2.1 Closure 21 3.2.2 Sufficiency 23 3.2.3 Evolving Structures other than Programs 23 3.3 Step 3: Fitness Function 24 3.4 Step 4: GP Parameters 26 3.5 Step 5: Termination and solution designation 27 4 Example Genetic Programming Run 4.1 Preparatory Steps 29 4.2 Step-by-Step Sample Run 31 4.2.1 Initialisation 31 4.2.2 Fitness Evaluation Selection, Crossover and Mutation Termination and Solution Designation Advanced Genetic Programming 5 Alternative Initialisations and Operators in 5.1 Constructing the Initial Population 5.1.1 Uniform Initialisation 5.1.2 Initialisation may Affect Bloat 5.1.3 Seeding 5.2 GP Mutation 5.2.1 Is Mutation Necessary? 5.2.2 Mutation Cookbook 5.3 GP Crossover 5.4 Other Techniques 32 5.5 Tree-based GP 39 6 Modular, Grammatical and Developmental Tree-based GP 47 6.1 Evolving Modular and Hierarchical Structures 47 6.1.1 Automatically Defined Functions 48 6.1.2 Program Architecture and Architecture-Altering 50 6.2 Constraining Structures 51 6.2.1 Enforcing Particular Structures 52 6.2.2 Strongly Typed GP 52 6.2.3 Grammar-based Constraints 53 6.2.4 Constraints and Bias 55 6.3 Developmental Genetic Programming 57 6.4 Strongly Typed Autoconstructive GP with PushGP 59 7 Linear and Graph Genetic Programming 61 7.1 Linear Genetic Programming 61 7.1.1 Motivations 61 7.1.2 Linear GP Representations 62 7.1.3 Linear GP Operators 64 7.2 Graph-Based Genetic Programming 65 7.2.1 Parallel Distributed GP (PDGP) 65 7.2.2 PADO 67 7.2.3 Cartesian GP 67 7.2.4 Evolving Parallel Programs using Indirect Encodings 68 8 Probabilistic Genetic Programming 8.1 Estimation of Distribution Algorithms 69 8.2 Pure EDA GP 71 8.3 Mixing Grammars and Probabilities 74 9 Multi-objective Genetic Programming 75 9.1 Combining Multiple Objectives into a Scalar Fitness Function 75 9.2 Keeping the Objectives Separate 76 9.2.1 Multi-objective Bloat and Complexity Control 77 9.2.2 Other Objectives 78 9.2.3 Non-Pareto Criteria 80 9.3 Multiple Objectives via Dynamic and Staged Fitness Functions 80 9.4 Multi-objective Optimisation via Operator Bias 81 10 Fast and Distributed Genetic Programming 83 10.1 Reducing Fitness Evaluations/Increasing their Effectiveness 83 10.2 Reducing Cost of Fitness with Caches 86 10.3 Parallel and Distributed GP are Not Equivalent 88 10.4 Running GP on Parallel Hardware 89 10.4.1 Master–slave GP 89 10.4.2 GP Running on GPUs 90 10.4.3 GP on FPGAs 92 10.4.4 Sub-machine-code GP 93 10.5 Geographically Distributed GP 93 11 GP Theory and its Applications 97 11.1 Mathematical Models 98 11.2 Search Spaces 99 11.3 Bloat 101 11.3.1 Bloat in Theory 101 11.3.2 Bloat Control in Practice 104 III Practical Genetic Programming 12 Applications 12.1 Where GP has Done Well 12.2 Curve Fitting, Data Modelling and Symbolic Regression 12.3 Human Competitive Results – the Humies 12.4 Image and Signal Processing 12.5 Financial Trading, Time Series, and Economic Modelling 12.6 Industrial Process Control 12.7 Medicine, Biology and Bioinformatics 12.8 GP to Create Searchers and Solvers – Hyper-heuristics xiii 12.9 Entertainment and Computer Games 127 12.10The Arts 127 12.11Compression 128 13 Troubleshooting GP 13.1 Is there a Bug in the Code? 13.2 Can you Trust your Results? 13.3 There are No Silver Bullets 13.4 Small Changes can have Big Effects 13.5 Big Changes can have No Effect 13.6 Study your Populations 13.7 Encourage Diversity 13.8 Embrace Approximation 13.9 Control Bloat 13.10 Checkpoint Results 13.11 Report Well 13.12 Convince your Customers 14 Conclusions Tricks of the Trade A Resources A.1 Key Books A.2 Key Journals A.3 Key International Meetings A.4 GP Implementations A.5 On-Line Resources 145 B TinyGP 151 B.1 Overview of TinyGP 151 B.2 Input Data Files for TinyGP 153 B.3 Source Code 154 B.4 Compiling and Running TinyGP 162 Bibliography 167 Inde

Design of Hardware with Quantifiable Security against Reverse Engineering

Semiconductors are a 412 billion dollar industry and integrated circuits take on important roles in human life, from everyday use in smart-devices to critical applications like healthcare and aviation. Saving today\u27s hardware systems from attackers can be a huge concern considering the budget spent on designing these chips and the sensitive information they may contain. In particular, after fabrication, the chip can be subject to a malicious reverse engineer that tries to invasively figure out the function of the chip or other sensitive data. Subsequent to an attack, a system can be subject to cloning, counterfeiting, or IP theft. This dissertation addresses some issues concerning the security of hardware systems in such scenarios. First, the issue of privacy risks from approximate computing is investigated in Chapter 2. Simulation experiments show that the erroneous outputs produced on each chip instance can reveal the identity of the chip that performed the computation, which jeopardizes user privacy. The next two chapters deal with camouflaging, which is a technique to prevent reverse engineering from extracting circuit information from the layout. Chapter 3 provides a design automation method to protect camouflaged circuits against an adversary with prior knowledge about the circuit\u27s viable functions. Chapter 4 provides a method to reverse engineer camouflaged circuits. The proposed reverse engineering formulation uses Boolean Satisfiability (SAT) solving in a way that incorporates laser fault injection and laser voltage probing capabilities to figure out the function of an aggressively camouflaged circuit with unknown gate functions and connections. Chapter 5 addresses the challenge of secure key storage in hardware by proposing a new key storage method that applies threshold-defined behavior of memory cells to store secret information in a way that achieves a high degree of protection against invasive reverse engineering. This approach requires foundry support to encode the secrets as threshold voltage offsets in transistors. In Chapter 6, a secret key storage approach is introduced that does not rely on a trusted foundry. This approach only relies on the foundry to fabricate the hardware infrastructure for key generation but not to encode the secret key. The key is programmed by the IP integrator or the user after fabrication via directed accelerated aging of transistors. Additionally, this chapter presents the design of a working hardware prototype on PCB that demonstrates this scheme. Finally, chapter 7 concludes the dissertation and summarizes possible future research

Evolving Graphs by Graph Programming

Graphs are a ubiquitous data structure in computer science and can be used to represent solutions to difficult problems in many distinct domains. This motivates the use of Evolutionary Algorithms to search over graphs and efficiently find approximate solutions. However, existing techniques often represent and manipulate graphs in an ad-hoc manner. In contrast, rule-based graph programming offers a formal mechanism for describing relations over graphs. This thesis proposes the use of rule-based graph programming for representing and implementing genetic operators over graphs. We present the Evolutionary Algorithm Evolving Graphs by Graph Programming and a number of its extensions which are capable of learning stateful and stateless digital circuits, symbolic expressions and Artificial Neural Networks. We demonstrate that rule-based graph programming may be used to implement new and effective constraint-respecting mutation operators and show that these operators may strictly generalise others found in the literature. Through our proposal of Semantic Neutral Drift, we accelerate the search process by building plateaus into the fitness landscape using domain knowledge of equivalence. We also present Horizontal Gene Transfer, a mechanism whereby graphs may be passively recombined without disrupting their fitness. Through rigorous evaluation and analysis of over 20,000 independent executions of Evolutionary Algorithms, we establish numerous benefits of our approach. We find that on many problems, Evolving Graphs by Graph Programming and its variants may significantly outperform other approaches from the literature. Additionally, our empirical results provide further evidence that neutral drift aids the efficiency of evolutionary search