At this time the synthesis of reversible circuits for quantum computing is an active area of research. In the most restrictive quantum computing models there are no ancilla lines and the quantum cost, or latency, of performing a reversible form of the AND gate, or Toffoli gate, increases exponentially with the number of input variables. In contrast, the quantum cost of performing any combination of reversible EXOR gates, or CNOT gates, on n input variables requires at most O(n2/log2n) gates. It was under these conditions that EXOR-AND-EXOR, or EPOE, synthesis was developed. In this work, the GF(2) logic theory used in EPOE is expanded and the concept of an EXOR-AND product transform is introduced. Because of the generality of this logic theory, it is adapted to EXOR-AND-OR, or SPOE, synthesis. Three heuristic spectral logic synthesis algorithms are introduced, implemented in a program called XAX, and compared with previous work in classical logic circuits of up to 26 inputs. Three linear reversible circuit methods are also introduced and compared with previous work in linear reversible logic circuits of up to 100 inputs.

Neural networks play an important role in the execution of goal-oriented paradigms. They offer flexibility, adaptability and versatility, so that a variety of approaches may be used to meet a specific goal, depending upon the circumstances and the requirements of the design specifications. Development of higher-order neural units with higher-order synaptic operations will open a new window for some complex problems such as control of aerospace vehicles, pattern recognition, and image processing. The neural models described in this thesis consider the behavior of a single neuron as the basic computing unit in neural information processing operations. Each computing unit in the network is based on the concept of an idealized neuron in the central nervous system (CNS). Most recent mathematical models and their architectures for neuro-control systems have generated many theoretical and industrial interests. Recent advances in static and dynamic neural networks have created a profound impact in the field of neuro-control. Neural networks consisting of several layers of neurons, with linear synaptic operation, have been extensively used in different applications such as pattern recognition, system identification and control of complex systems such as flexible structures, and intelligent robotic systems. The conventional linear neural models are highly simplified models of the biological neuron. Using this model, many neural morphologies, usually referred to as multilayer feedforward neural networks (MFNNs), have been reported in the literature. The performance of the neurons is greatly affected when a layer of neurons are implemented for system identification, pattern recognition and control problems. Through simulation studies of the XOR logic it was concluded that the neurons with linear synaptic operation are limited to only linearly separable forms of pattern distribution. However, they perform a variety of complex mathematical operations when they are implemented in the form of a network structure. These networks suffer from various limitations such as computational efficiency and learning capabilities and moreover, these models ignore many salient features of the biological neurons such as time delays, cross and self correlations, and feedback paths which are otherwise very important in the neural activity. In this thesis an effort is made to develop new mathematical models of neurons that belong to the class of higher-order neural units (HONUs) with higher-order synaptic operations such as quadratic and cubic synaptic operations. The advantage of using this type of neural unit is associated with performance of the neurons but the performance comes at the cost of exponential increase in parameters that hinders the speed of the training process. In this context, a novel method of representation of weight parameters without sacrificing the neural performance has been introduced. A generalised representation of the higher-order synaptic operation for these neural structures was proposed. It was shown that many existing neural structures can be derived from this generalized representation of the higher-order synaptic operation. In the late 1960s, McCulloch and Pitts modeled the stimulation-response of the primitive neuron using the threshold logic. Since then, it has become a practice to implement the logic circuits using neural structures. In this research, realization of the logic circuits such as OR, AND, and XOR were implemented using the proposed neural structures. These neural structures were also implemented as neuro-controllers for the control problems such as satellite attitude control and model reference adaptive control. A comparative study of the performance of these neural structures compared to that of the conventional linear controllers has been presented. The simulation results obtained in this research were applicable only for the simplified model presented in the simulation studies.

With the advancement of technology, submicron CMOSonly process is available now for Application Specific Integrated Circuits (ASICs). The high integration leads to the need for high pin counts. However voltage supply and ground bounce due to many output drivers switching at the same time is becoming a major problem. In this thesis, a CMOS offchip buffer design which generates ECL logic levels with lower ground bounce noise is described and demonstrated. The technique used in designing this buffer to reduce voltage noise differs from conventional design techniques. Traditionally there are two general methods to reduce ground bounce. One approach tries to reduce the instantaneous current change (di/dt) by increasing (prolonging) the rise and fall time of the signals. The other approach attempts to reduce the parasitic inductance attributed to packaging by using multiple supply pins. Our technique reduces the voltage noise by controlling the instantaneous current change through the reduction of current difference during switching time. Based on this approach, a novel circuit structure is designed. This circuit has a fully symmetrical configuration and is being selfbiased through negative feedback. A current injection technique is also used to increase the stability of the circuit. SPICE simulation of the proposed circuit is performed. Comparison and tradeoffs with other approaches are studied. / Graduation date: 1994

Neural networks play an important role in the execution of goal-oriented paradigms. They offer flexibility, adaptability and versatility, so that a variety of approaches may be used to meet a specific goal, depending upon the circumstances and the requirements of the design specifications. Development of higher-order neural units with higher-order synaptic operations will open a new window for some complex problems such as control of aerospace vehicles, pattern recognition, and image processing. The neural models described in this thesis consider the behavior of a single neuron as the basic computing unit in neural information processing operations. Each computing unit in the network is based on the concept of an idealized neuron in the central nervous system (CNS). Most recent mathematical models and their architectures for neuro-control systems have generated many theoretical and industrial interests. Recent advances in static and dynamic neural networks have created a profound impact in the field of neuro-control. Neural networks consisting of several layers of neurons, with linear synaptic operation, have been extensively used in different applications such as pattern recognition, system identification and control of complex systems such as flexible structures, and intelligent robotic systems. The conventional linear neural models are highly simplified models of the biological neuron. Using this model, many neural morphologies, usually referred to as multilayer feedforward neural networks (MFNNs), have been reported in the literature. The performance of the neurons is greatly affected when a layer of neurons are implemented for system identification, pattern recognition and control problems. Through simulation studies of the XOR logic it was concluded that the neurons with linear synaptic operation are limited to only linearly separable forms of pattern distribution. However, they perform a variety of complex mathematical operations when they are implemented in the form of a network structure. These networks suffer from various limitations such as computational efficiency and learning capabilities and moreover, these models ignore many salient features of the biological neurons such as time delays, cross and self correlations, and feedback paths which are otherwise very important in the neural activity. In this thesis an effort is made to develop new mathematical models of neurons that belong to the class of higher-order neural units (HONUs) with higher-order synaptic operations such as quadratic and cubic synaptic operations. The advantage of using this type of neural unit is associated with performance of the neurons but the performance comes at the cost of exponential increase in parameters that hinders the speed of the training process. In this context, a novel method of representation of weight parameters without sacrificing the neural performance has been introduced. A generalised representation of the higher-order synaptic operation for these neural structures was proposed. It was shown that many existing neural structures can be derived from this generalized representation of the higher-order synaptic operation. In the late 1960s, McCulloch and Pitts modeled the stimulation-response of the primitive neuron using the threshold logic. Since then, it has become a practice to implement the logic circuits using neural structures. In this research, realization of the logic circuits such as OR, AND, and XOR were implemented using the proposed neural structures. These neural structures were also implemented as neuro-controllers for the control problems such as satellite attitude control and model reference adaptive control. A comparative study of the performance of these neural structures compared to that of the conventional linear controllers has been presented. The simulation results obtained in this research were applicable only for the simplified model presented in the simulation studies.

No description available.

In recent years ternary reversible logic has caught the attention of researchers because of its enormous potential in different fields, in particular quantum computing. It is desirable that any future reversible technology should be fault tolerant and have low power consumption; hence developing testing techniques in this area is of great importance. In this work we propose a design for an online testable ternary reversible circuit. The proposed design can implement almost all of the ternary logic operations and is also capable of testing the reversible ternary network in real time (online). The error detection unit is also constructed in a reversible manner, which results in an overall circuit which meets the requirements of reversible computing. We have also proposed an upgrade of the initial design to make the design more optimized. Several ternary benchmark circuits have been implemented using the proposed approaches. The number of gates required to implement the benchmarks for each approach have also been compared. To our knowledge this is the first such circuit in ternary with integrated online testability feature. / xii, 92 leaves : ill. ; 29 cm

Parameter variations, noise susceptibility, and increasing energy dissipation of CMOS devices have been recognized as major challenges in circuit and micro-architecture design in the nanometer regime. Among these, parameter variations and noise susceptibility are increasingly causing CMOS devices to behave in an "unreliable" or "probabilistic" manner. To address these challenges, a shift in design paradigm, from current day deterministic designs to "statistical" or "probabilistic" designs is deemed inevitable. Motivated by these considerations, I introduce and define probabilistic Boolean logic, whose logical operators are by definition "correct" with a probability 1/2 <= p <= 1. While most of the laws of conventional Boolean logic can be naturally extended to be valid in the probabilistic case, there are a few significant departures. We also show that computations realized using implicitly probabilistic Boolean operators are more energy efficient than their counterparts which use explicit sources of randomness, in the context of probabilistic Boolean circuits as well as probabilistic models with state, Rabin automata. To demonstrate the utility of implicitly probabilistic elements, we study a family of probabilistic architectures: the probabilistic system-on-a-chip PSOC, based on CMOS devices rendered probabilistic due to noise, referred to as probabilistic CMOS or PCMOS devices. These architectures yield significant improvements, both in the energy consumed as well as in the performance in the context of probabilistic or randomized applications with broad utility. Finally, we extend the consideration of probability of correctness to arithmetic operations, through probabilistic arithmetic. We show that in the probabilistic context, substantial savings in energy over correct arithmetic operations may be achieved. This is the theoretical basis of the energy savings reported in the video decoding and radar processing applications that has been demonstrated in prior work.

Thesis (M.App.Sc.) - Carleton University, 2002. / Includes bibliographical references (p. 93-95). Also available in electronic format on the Internet.

Thesis (M. App. Sc.)--Carleton University, 2004. / Includes bibliographical references (p. 95-98). Also available in electronic format on the Internet.

Orientador: Alberto Martins Jorge / Dissertação (mestrado) - Universidade Estadual de Campinas, Faculdade de Engenharia Eletrica e de Computação / Made available in DSpace on 2018-08-09T08:44:01Z (GMT). No. of bitstreams: 1 MingotoJunior_CarlosRoberto_M.pdf: 657421 bytes, checksum: dc6ef4bc58fb70a90293781871a969c6 (MD5) Previous issue date: 2005 / Resumo: Os circuitos quaternários são uma alternativa para o processamento das informações, que, atualmente, acontece de forma binária. Ainda em fase de definições, a lógica multivalores mostra-se como um campo de pesquisas que pode auxiliar a busca pelo incremento de desempenho e redução de área de ocupação dos transistores de um circuito integrado. A lógica multi-valores utilizando-se de quatro dígitos na representação das informações é a lógica quaternária. Neste trabalho são propostos alguns blocos básicos de circuitos eletrônicos quaternários que, progressivamente, são aglutinados formando blocos mais complexos para finalmente construir-se um circuito meio-somador, um somador completo e um multiplicador quaternários. As montagens são feitas e testadas em simulador de circuitos eletrônicos e operam em modo corrente com transistores bipolares NPN e PNP / Abstract: The quaternary circuits are an alternative to data processing that, nowadays, occurs in a binary way. Still in a definition stage, the multiple-valued logic seems to be a research area to aid the increase of performance and reduction of area of the transistors inside an integrated circuit. The multiple-valued logic using four digits to represent the data is called quaternary logic. In this work are proposed some basic blocks of electronic quaternary circuit which are progressively joined to become more complex blocks and finally a half-adder, a full adder and a multiplier. The configurations are done and evaluated in a circuit simulator operating in a current-mode with bipolar NPN and PNP transistors / Mestrado / Eletrônica, Microeletrônica e Optoeletrônica / Mestre em Engenharia Elétrica