Computer aided synthesis and optimisation of electronic logic circuits

In this thesis, a variety of algorithms for synthesis and optimisation of combinational and sequential logic circuits are developed. These algorithms could be part of new commercial EGAD package for future VLSI digital designs. The results show that considerable saving in components can be achieved resulting in simpler designs that are smaller, cheaper, consume less power and easier to test. The purpose of generating different sets of coefficients related to Reed Muller (RM) is that they contain different number of terms; therefore the minimum one can be selected to design the circuits with reduced gate count. To widen the search space and achieve better synthesis tools, representations of Mixed Polarity Reed Muller (MPRM), Mixed Polarity Dual Reed Muller (MPDRM), and Pseduo Kronecker Reed Muller (PKRO RM) expansions are investigated. Efficient and fast combinatorial techniques and algorithms are developed for the following: â Bidirectional conversion between MPRM/ MPDRM form and Fixed Polarity Reed Muller forms (FPRM)/Fixed Polarity Dual Reed Muller forms (FPDRM) form respectively. The main advantages for these techniques are their simplicity and suitability for single and multi output Boolean functions. â Computing the coefficients of any polarity related to PKRO_RM class starting from FPRM coefficients or Canonical Sum of Products (CSOP). â Computing the coefficients of any polarity related to MPRM/or MPDRM directly from standard form of CSOP/Canonical Product of sums (CPOS) Boolean functions, respectively. The proposed algorithms are efficient in terms of CPU time and can be used for large functions. For optimisation of combinational circuits, new techniques and algorithms based on algebraic techniques are developed which can be used to generate reduced RM expressions to design circuits in RM/DRM domain starting from FPRM/FPDRM, respectively. The outcome for these techniques is expansion in Reed Muller domain with minimal terms. The search space is 3`" Exclusive OR Sum of Product (ESOP)/or Exclusive NOR Product of Sums (ENPOS) expansions. Genetic Algorithms (GAs) are also developed to optimise combinational circuits to find optimal MPRM/MPDRM among 3° different polarities without the need to do exhaustive search. These algorithms are developed for completely and incompletely specified Boolean functions. The experimental results show that GA can find optimum solutions in a short time compared with long time required running exhaustive search in all the benchmarks tested. Multi Objective Genetic Algorithm (MOGA) is developed and implemented to determine the optimal state assignment which results in less area and power dissipation for completely and incompletely specified sequential circuits. The goal is to find the best assignments which reduce the component count and switching activity simultaneously. The experimental results show that saving in components and switching activity are achieved in most of the benchmarks tested compared with recently published research. All algorithms are implemented in C++.EThOS - Electronic Theses Online ServiceGBUnited Kingdo

Computer aided synthesis and optimisation of electronic logic circuits

In this thesis, a variety of algorithms for synthesis and optimisation of combinational and sequential logic circuits are developed. These algorithms could be part of new commercial EGAD package for future VLSI digital designs. The results show that considerable saving in components can be achieved resulting in simpler designs that are smaller, cheaper, consume less power and easier to test.The purpose of generating different sets of coefficients related to Reed Muller (RM) is that they contain different number of terms; therefore the minimum one can be selected to design the circuits with reduced gate count. To widen the search space and achieve better synthesis tools, representations of Mixed Polarity Reed Muller (MPRM), Mixed Polarity Dual Reed Muller (MPDRM), and Pseduo Kronecker Reed Muller (PKRO RM) expansions are investigated. Efficient and fast combinatorial techniques and algorithms are developed for the following:- Bidirectional conversion between MPRM/ MPDRM form and Fixed Polarity Reed Muller forms (FPRM)/Fixed Polarity Dual Reed Muller forms (FPDRM) form respectively. The main advantages for these techniques are their simplicity and suitability for single and multi output Boolean functions.- Computing the coefficients of any polarity related to PKRO_RM class starting from FPRM coefficients or Canonical Sum of Products (CSOP).- Computing the coefficients of any polarity related to MPRM/or MPDRM directly from standard form of CSOP/Canonical Product of sums (CPOS) Boolean functions, respectively. The proposed algorithms are efficient in terms of CPU time and can be used for large functions.For optimisation of combinational circuits, new techniques and algorithms based on algebraic techniques are developed which can be used to generate reduced RM expressions to design circuits in RM/DRM domain starting from FPRM/FPDRM, respectively. The outcome for these techniques is expansion in Reed Muller domain with minimal terms. The search space is 3`" Exclusive OR Sum of Product (ESOP)/or Exclusive NOR Product of Sums (ENPOS) expansions.Genetic Algorithms (GAs) are also developed to optimise combinational circuits to find optimal MPRM/MPDRM among 3° different polarities without the need to do exhaustive search. These algorithms are developed for completely and incompletely specified Boolean functions. The experimental results show that GA can find optimum solutions in a short time compared with long time required running exhaustive search in all the benchmarks tested.Multi Objective Genetic Algorithm (MOGA) is developed and implemented to determine the optimal state assignment which results in less area and power dissipation for completely and incompletely specified sequential circuits. The goal is to find the best assignments which reduce the component count and switching activity simultaneously. The experimental results show that saving in components and switchingactivity are achieved in most of the benchmarks tested compared with recentlypublished research. All algorithms are implemented in C++

Evolutionary Computation

This book presents several recent advances on Evolutionary Computation, specially evolution-based optimization methods and hybrid algorithms for several applications, from optimization and learning to pattern recognition and bioinformatics. This book also presents new algorithms based on several analogies and metafores, where one of them is based on philosophy, specifically on the philosophy of praxis and dialectics. In this book it is also presented interesting applications on bioinformatics, specially the use of particle swarms to discover gene expression patterns in DNA microarrays. Therefore, this book features representative work on the field of evolutionary computation and applied sciences. The intended audience is graduate, undergraduate, researchers, and anyone who wishes to become familiar with the latest research work on this field

Advances in Evolutionary Algorithms

With the recent trends towards massive data sets and significant computational power, combined with evolutionary algorithmic advances evolutionary computation is becoming much more relevant to practice. Aim of the book is to present recent improvements, innovative ideas and concepts in a part of a huge EA field

Evolutionary algorithms for scheduling operations

While business process automation is proliferating through industries and processes, operations such as job and crew scheduling are still performed manually in the majority of workplaces. The linear programming techniques are not capable of automated production of a job or crew schedule within a reasonable computation time due to the massive sizes of real-life scheduling problems. For this reason, AI solutions are becoming increasingly popular, specifically Evolutionary Algorithms (EAs). However, there are three key limitations of previous studies researching application of EAs for the solution of the scheduling problems. First of all, there is no justification for the selection of a particular genetic operator and conclusion about their effectiveness. Secondly, the practical efficiency of such algorithms is unknown due to the lack of comparison with manually produced schedules. Finally, the implications of real-life implementation of the algorithm are rarely considered. This research aims at addressing all three limitations. Collaborations with DBSchenker,the rail freight carrier, and Garnett-Dickinson, the printing company,have been established. Multi-disciplinary research methods including document analysis, focus group evaluations, and interviews with managers from different levels have been carried out. A standard EA has been enhanced with developed within research intelligent operators to efficiently solve the problems. Assessment of the developed algorithm in the context of real life crew scheduling problem showed that the automated schedule outperformed the manual one by 3.7% in terms of its operating efficiency. In addition, the automatically produced schedule required less staff to complete all the jobs and might provide an additional revenue opportunity of £500 000. The research has also revealed a positive attitude expressed by the operational and IT managers towards the developed system. Investment analysis demonstrated a 41% return rate on investment in the automated scheduling system, while the strategic analysis suggests that this system can enable attainment of strategic priorities. The end users of the system, on the other hand, expressed some degree of scepticism and would prefer manual methods

An algorithmic approach to system architecting using shape grammar-cellular automata

Thesis (Ph. D.)--Massachusetts Institute of Technology, Engineering Systems Division, 2008.Includes bibliographical references (p. 404-417).This thesis expands upon the understanding of the fundamentals of system architecting in order to more effectively apply this process to engineering systems. The universal concern about the system architecting process is that the needs and wants of the stakeholders are not being fully satisfied, primarily because too few design alternatives are created and ambiguity exists in the information required. At the same time, it is noted that nature offers a superb example of system architecting and therefore should be considered as a guide for the engineering of systems. Key features of nature's architecting processes include self-generation, diversity, emergence, least action (balance of kinetic and potential energy), system-of-systems organization, and selection for stability. Currently, no human-friendly method appears to exist that addresses the problems in the field of system architecture while at the same time emulating nature's processes. By adapting nature's self-generative approach, a systematic means is offered to more rigorously conduct system architecting and better satisfy stakeholders. After reviewing generative design methods, an algorithmic methodology is developed to generate a space of architectural solutions satisfying a given specification, local constraints, and physical laws. This approach combines a visually oriented human design interface (shape grammar) that provides an intuitive design language with a machine (cellular automata) to execute the system architecture's production set (algorithm). The manual output of the flexible shape grammar, the set of design rules, is transcribed into cellular automata neighborhoods as a sequenced production set that may include other simple programs (such as combinatoric instructions).(cont.) The resulting catalog of system architectures can be unmanageably large, so selection criteria (e.g., stability, matching interfaces, least action) are defined by the architect to narrow the solution space for stakeholder review. The shape grammar-cellular automata algorithmic approach was demonstrated across several domains of study. This methodology improves on the design's clarification and the number of design alternatives produced, which should result in greater stakeholder satisfaction. Of additional significance, this approach has shown value both in the study of the system architecting process, leading to the proposal of normative principles for system architecture, and in the modeling of systems for better understanding.by Thomas H. Speller, Jr.Ph.D