124 research outputs found

    Some aspects of an evolvable hardware approach for multiple-valued combinational circuit design

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    In this paper a gate-level evolvable hardware technique for designing multiple-valued (MV) combinational circuits is proposed for the first time. In comparison with the decomposition techniques used for synthesis of combinational circuits previously employed, this new approach is easily adapted for the different types of MV gates associated with operations corresponding to different algebra types and can include other more complex logical expressions (e.g. singlecontrol MV multiplexer called T-gate). The technique is based on evolving the functionality and connectivity of a rectangular array of logic cells. The experimental results show how the success of genetic algorithm depends on the number of columns, the number of rows in circuit structure and levels-back parameter (the number of columns to the left of current cell to which cell input may be connected). We show that the choice of the set of MV gates used radically affects the chances of successful evolution (in terms of number of 100% functional solutions found)

    An extrinsic function-level evolvable hardware approach

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    The function level evolvable hardware approach to synthesize the combinational multiple-valued and binary logic functions is proposed in first time. The new representation of logic gate in extrinsic EHW allows us to describe behaviour of any multi-input multi-output logic function. The circuit is represented in the form of connections and functionalities of a rectangular array of building blocks. Each building block can implement primitive logic function or any multi-input multi-output logic function defined in advance. The method has been tested on evolving logic circuits using half adder, full adder and multiplier. The effectiveness of this approach is investigated for multiple-valued and binary arithmetical functions. For these functions either method appears to be much more efficient than similar approach with two-input one-output cell representation

    Evolving more efficient digital circuits by allowing circuit layout evolution and multi-objective fitness

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    We use evolutionary search to design combinational logic circuits. The technique is based on evolving the functionality and connectivity of a rectangular array of logic cells whose dimension is defined by the circuit layout. The main idea of this approach is to improve quality of the circuits evolved by the GA by reducing the number of active gates used. We accomplish this by combining two ideas: 1) using multi-objective fitness function; 2) evolving circuit layout. It will be shown that using these two approaches allows us to increase the quality of evolved circuits. The circuits are evolved in two phases. Initially the genome fitness in given by the percentage of output bits that are correct. Once 100% functional circuits have been evolved, the number of gates actually used in the circuit is taken into account in the fitness function. This allows us to evolve circuits with 100% functionality and minimise the number of active gates in circuit structure. The population is initialised with heterogeneous circuit layouts and the circuit layout is allowed to vary during the evolutionary process. Evolving the circuit layout together with the function is one of the distinctive features of proposed approach. The experimental results show that allowing the circuit layout to be flexible is useful when we want to evolve circuits with the smallest number of gates used. We find that it is better to use a fixed circuit layout when the objective is to achieve the highest number of 100% functional circuits. The two-fitness strategy is most effective when we allow a large number of generations

    The genetic algorithm as a discovery engine: Strange circuits and new principles

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    This paper examines the idea of a genetic or evolutionary algorithm being an inspirational or discovery engine. This is illustrated in the particular context of designing electronic circuits. We argue that by connecting pieces of logic together and testing them to see if they carry out the desired function it may be possible to discover new principles of design, and new algebraic techniques. This is illustrated in the design of binary circuits, particularly arithmetic functions, where we demonstrate that by evolving a hierarchical series of examples, it becomes possible to re-discover the well known ripple-carry principle for building adder circuits of any size. We also examine the much harder case of multiplication. We show also that extending the work into the field of multiple-valued logic, the genetic algorithm is able to produce fully working circuits that lie outside conventional algebra. In addition we look at the issue of principle extraction from evolved data

    Evolutionary algorithms for synthesis and optimisation of sequential logic circuits

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    Considerable progress has been made recently 1n the understanding of combinational logic optimization. Consequently a large number of university and industrial Electric Computing Aided Design (ECAD) programs are now available for optimal logic synthesis of combinational circuits. The progress with sequential logic synthesis and optimization, on the other hand, is considerably less mature. In recent years, evolutionary algorithms have been found to be remarkably effective way of using computers for solving difficult problems. This thesis is, in large part, a concentrated effort to apply this philosophy to the synthesis and optimization of sequential circuits. A state assignment based on the use of a Genetic Algorithm (GA) for the optimal synthesis of sequential circuits is presented. The state assignment determines the structure of the sequential circuit realizing the state machine and therefore its area and performances. The synthesis based on the GA approach produced designs with the smallest area to date. Test results on standard fmite state machine (FS:M) benchmarks show that the GA could generate state assignments, which required on average 15.44% fewer gates and 13.47% fewer literals compared with alternative techniques. Hardware evolution is performed through a succeSSlOn of changes/reconfigurations of elementary components, inter-connectivity and selection of the fittest configurations until the target functionality is reached. The thesis presents new approaches, which combine both genetic algorithm for state assignment and extrinsic Evolvable Hardware (EHW) to design sequential logic circuits. The implemented evolutionary algorithms are able to design logic circuits with size and complexity, which have not been demonstrated in published work. There are still plenty of opportunities to develop this new line of research for the synthesis, optimization and test of novel digital, analogue and mixed circuits. This should lead to a new generation of Electronic Design Automation tools.EThOS - Electronic Theses Online ServiceGBUnited Kingdo
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