The Frequency Disturbance Recorder (FDR) is a specialized data acquisition device designed to monitor fluctuations in the overall power system. The device is designed such that it can be attached by way of a standard wall power outlet to the power system. These devices then transmit their calculated frequency data through the public internet to a centralized data management and storage server. By distributing a number of these identical systems throughout the three major North American power systems, Virginia Tech has created a Frequency Monitoring Network (FNET). The FNET is composed of these distributed FDRs as well as an Information Management Server (IMS). Since frequency information can be used in many areas of power system analysis, operation and control, there are a great number of end uses for the information provided by the FNET system. The data provides researchers and other users with the information to make frequency analyses and comparisons for the overall power system. Prior to the end of 2004, the FNET system was made a reality, and a number of FDRs were placed strategically throughout the United States. The purpose of this thesis is to present the elements of a new generation of FDR hardware design. These elements will enable the design to be more flexible and to lower reliance on some vendor specific components. Additionally, these enhancements will offload most of the computational processing required of the system to a commodity PC rather than an embedded system solution that is costly in both development time and financial cost. These goals will be accomplished by using a Field Programmable Gate Array (FPGA), a commodity off-the-shelf personal computer, and a new overall system design. / Master of Science

Security of cryptographic algorithms relies on the computational difficulty of deriving the secret key using public information. Cryptanalysis, including logical and implementation attacks, plays an important role in allowing the security community to estimate their cost, based on the computational resources of an attacker. Practical implementations of cryptanalytic systems require complex designs that integrate multiple functional components with many parameters. In this thesis, methodologies are proposed to improve the design process of cryptanalytic systems and reduce the cost of design space exploration required for optimization. First, Bluespec, a rule-based HDL, is used to increase the abstraction level of hardware design and support efficient design space exploration. Bluespec is applied to implement a hardware-accelerated logical attack on ECC with optimized modular arithmetic components. The language features of Bluespec support exploration and this is demonstrated by applying Bluespec to investigate the speed area tradeoff resulting from various design parameters and demonstrating performance that is competitive with prior work. This work also proposes a testing environment for use in verifying the implementation attack resistance of secure systems. A modular design approach is used to provide separation between the device being tested and the test script, as well as portability, and openness. This yields an open-source solution that supports implementation attack testing independent of the system platform, implementation details, and type of attack under evaluation. The suitability of the proposed test environment for implementation attack vulnerability analysis is demonstrated by applying the environment to perform an implementation attack on AES. The design of complex cryptanalytic hardware can greatly benefit from better design methodologies and the results presented in this thesis advocate the importance of this aspect. / Master of Science

With the increasing use of machine learning by devices at the network's edge, a trend of moving computation from data centers to these devices is emerging. This shift imposes strict energy requirements on the algorithms used and the hardware on which they are implemented. Neuromorphic spiking neural networks (SNNs) and heterogeneous sytems on a chip (SoCs) are showing great potential for energy-efficient computing on the edge. This thesis describes the development of a high-level interface for accelerating SNNs on an FPGA–CPU SoC. The system is based on an existing open-source, low-level implementation, adapting it for a research-focused Python front-end. The developed interface provides a productive environment for exploring and evaluating SNN algorithms and topologies through compatibility with industry-standard tools for numerical computing, data analysis, and visualization, while still taking full advantage of FPGA-based hardware acceleration. The system is evaluated and showcased by analyzing the training of a small network to solve the XOR problem. As the project matures, future development could enable integration with commonly used machine learning libraries, further increasing it's potential.

Active feedback control in magnetic confinement fusion devices is desirable to mitigate plasma instabilities and enable robust operation. Among various diagnostics, optical high-speed cameras provide a powerful, non-invasive diagnostic and can be suitable for these applications. This thesis reports the first application of high-speed imaging videography and deep learning as real-time diagnostics of rotating MHD modes in a tokamak device. The developed system uses a convolutional neural network (CNN) to predict the amplitudes of the ?=1 sine and cosine mode components using solely optical measurements acquired from one or more cameras. Using the newly assembled high-speed camera diagnostics on the High Beta Tokamak – Extended Pulse (HBT-EP) device, an experimental dataset consisting of camera frame images and magnetic-based mode measurements was assembled and used to develop the mode-tracking CNN model. The optimized models outperformed other tested conventional algorithms given identical image inputs. A prototype controller based on a field-programmable gate array (FPGA) hardware was developed to perform real-time mode tracking using the high-speed camera diagnostic with the mode-tracking CNN model. In this system, a trained model was directly implemented in the firmware of an FPGA device onboard the frame grabber hardware of the camera’s data readout system. Adjusting the model size and its implementation-related parameters allowed achieving an optimal trade-off between a model’s prediction accuracy, its FPGA resource utilization and inference speed. Through fine-tuning these parameters, the final implementation satisfied all of the design constraints, achieving a total trigger-to-output latency of 17.6 ?s and a throughput of up to 120 kfps. These results are on-par with the existing GPU-based control system using magnetic sensor diagnostic, indicating that the camera-based controller will be capable to perform active feedback control of MHD modes on HBT-EP.

Quand on veut évaluer ou manipuler une fonction mathématique f, il est fréquent de la remplacer par une approximation polynomiale p. On le fait, par exemple, pour implanter des fonctions élémentaires en machine, pour la quadrature ou la résolution d'équations différentielles ordinaires (ODE). De nombreuses méthodes numériques existent pour l'ensemble de ces questions et nous nous proposons de les aborder dans le cadre du calcul rigoureux, au sein duquel on exige des garanties sur la précision des résultats, tant pour l'erreur de méthode que l'erreur d'arrondi.Une approximation polynomiale rigoureuse (RPA) pour une fonction f définie sur un intervalle [a,b], est un couple (P, Delta) formé par un polynôme P et un intervalle Delta, tel que f(x)-P(x) appartienne à Delta pour tout x dans [a,b].Dans ce travail, nous analysons et introduisons plusieurs procédés de calcul de RPAs dans le cas de fonctions univariées. Nous analysons et raffinons une approche existante à base de développements de Taylor.Puis nous les remplaçons par des approximants plus fins, tels que les polynômes minimax, les séries tronquées de Chebyshev ou les interpolants de Chebyshev.Nous présentons aussi plusieurs applications: une relative à l'implantation de fonctions standard dans une bibliothèque mathématique (libm), une portant sur le calcul de développements tronqués en séries de Chebyshev de solutions d'ODE linéaires à coefficients polynômiaux et, enfin, un processus automatique d'évaluation de fonction à précision garantie sur une puce reconfigurable. / For purposes of evaluation and manipulation, mathematical functions f are commonly replaced by approximation polynomials p. Examples include floating-point implementations of elementary functions, integration, ordinary differential equations (ODE) solving. For that, a wide range of numerical methods exists. We consider the application of such methods in the context of rigorous computing, where we need guarantees on the accuracy of the result, with respect to both the truncation and rounding errors.A rigorous polynomial approximation (RPA) for a function f defined over an interval [a,b] is a couple (P, Delta) where P is a polynomial and Delta is an interval such that f(x)-P(x) belongs to Delta, for all x in [a,b]. In this work we analyse and bring forth several ways of obtaining RPAs for univariate functions. Firstly, we analyse and refine an existing approach based on Taylor expansions. Secondly, we replace them with better approximations such as minimax approximations, Chebyshev truncated series or interpolation polynomials.Several applications are presented: one from standard functions implementation in mathematical libraries (libm), another regarding the computation of Chebyshev series expansions solutions of linear ODEs with polynomial coefficients, and finally an automatic process for function evaluation with guaranteed accuracy in reconfigurable hardware.

Network-on-chip (NoC) based multi-processor systems-on-chip (MPSoCs) are promising candidates for future multi-processor embedded platforms, which are expected to be composed of hundreds of heterogeneous processing elements (PEs) to potentially provide high performances. However, together with the performances, the systems complexity will increase, and new high level design techniques will be needed to efficiently model, simulate, debug and synthesize them. System-level design (SLD) is considered to be the next frontier in electronic design automation (EDA). It enables the description of embedded systems in terms of abstract functions and interconnected blocks. A promising complementary approach to SLD is the use of models of computation (MoCs) to formally describe the execution semantics of functions and blocks through a set of rules. However, also when this formalization is used, there is no clear way to synthesize system-level models into software (SW) and hardware (HW) towards a NoC-based MPSoC implementation, i.e., there is a lack of system design automation (SDA) techniques to rapidly synthesize and prototype system-level models onto heterogeneous NoC-based MPSoCs. In addition, many of the proposed solutions require large overhead in terms of SW components and memory requirements, resulting in complex and customized multi-processor platforms. In order to tackle the problem, a novel model-based SDA flow has been developed as part of the thesis. It starts from a system-level specification, where functions execute according to the synchronous MoC, and then it can rapidly prototype the system onto an FPGA configured as an heterogeneous NoC-based MPSoC. In the first part of the thesis the HeartBeat model is proposed as a model-based technique which fills the abstraction gap between the abstract system-level representation and its implementation on the multiprocessor prototype. Then details are provided to describe how this technique is automated to rapidly prototype the modeled system on a flexible platform, permitting to adjust the system specification until the designer is satisfied with the results. Finally, the proposed SDA technique is improved defining a methodology to automatically explore possible design alternatives for the modeled system to be implemented on a heterogeneous NoC-based MPSoC. The goal of the exploration is to find an implementation satisfying the designer's requirements, which can be integrated in the proposed SDA flow. Through the proposed SDA flow, the designer is relieved from implementation details and the design time of systems targeting heterogeneous NoC-based MPSoCs on FPGA is significantly reduced. In addition, it reduces possible design errors proposing a completely automated technique for fast prototyping. Compared to other SDA flows, the proposed technique targets a bare-metal solution, avoiding the use of an operating system (OS). This reduces the memory requirements on the FPGA platform comparing to related work targeting MPSoC on FPGA. At the same time, the performance (throughput) of the modeled applications can be increased when the number of processors of the target platform is increased. This is shown through a wide set of case studies implemented on FPGA. / <p>QC 20140609</p>

Thesis (D. Tech. (Engineering: Electrical)) -- Central University of technology, Free State, 2012 / This thesis describes a method for the automatic generation of appli- cation speci_c processors. The thesis was organized into three sepa- rate but interrelated studies, which together provide: a justi_cation for the method used, a theory that supports the method, and a soft- ware application that realizes the method. The _rst study looked at how modern day microprocessors utilize their hardware resources and it proposed a metric, called core density, for measuring the utilization rate. The core density is a function of the microprocessor's instruction set and the application scheduled to run on that microprocessor. This study concluded that modern day microprocessors use their resources very ine_ciently and proposed the use of subset processors to exe- cute the same applications more e_ciently. The second study sought to provide a theoretical framework for the use of subset processors by developing a generic formal model of computer architecture. To demonstrate the model's versatility, it was used to describe a number of computer architecture components and entire computing systems. The third study describes the development of a set of software tools that enable the automatic generation of application speci_c proces- sors. The FiT toolkit automatically generates a unique Hardware Description Language (HDL) description of a processor based on an application binary _le and a parameterizable template of a generic mi- croprocessor. Area-optimized and performance-optimized custom soft processors were generated using the FiT toolkit and the utilization of the hardware resources by the custom soft processors was character- ized. The FiT toolkit was combined with an ANSI C compiler and a third-party tool for programming _eld-programmable gate arrays (FPGAs) to create an unconstrained C-to-silicon compiler.

Floating-gate transistors similar to those used in FLASH and EEPROM can be used to build reconfigurable analog arrays. The charge on the floating gate can be modified to pass or block a signal in a cross-bar switch matrix, or it can be finely tuned to eliminate a threshold difference across a chip or set a bias. By using such a compact and versatile reconfigurable analog memory element, the number of analog circuit components included on an integrated circuit that is field-programmable is significantly higher. As a result, large-scale FPAAs can be built with the same impact on analog design that FPGAs have had on digital design. In my research, I investigate the areas floating-gate transistors can be used to impact FPAA design and implementation. An FPAA can be broken up into two basic components, elements of connection and elements of computation. With respect to connection, I show that a floating-gate switch can be used in a cross-bar matrix in place of a transmission gate resulting in less parasitic capacitance and a more linear resistance for the same size transistor. I illuminate the programming issues relating to injecting a floating-gate for use as a switch, including the drain selection circuitry and rogue injection due to gate induced drain leakage. With respect to computation, I explain how a Multiple-Input Translinear Element, or MITE, can be augmented to fit in an FPAA framework. I also discuss two different MITE implementations compatible with CMOS technology, a subthreshold MOS design and a BJT MITE that uses a lateral BJT. Beyond FPAA components, I present two alternative FPAA systems. The first is a general purpose reconfigurable analog system that uses standard analog design components that have been augmented with floating-gates. The second FPAA is built upon MITE circuits, and is focused on supporting direct system synthesis. I conclude with a discussion of a future large-scale MITE FPAA.

A voltage source inverter is commonly used to supply a variable frequency variable voltage to a three phase induction motor in a variable speed application. A suitable pulse width modulation (PWM) technique is employed to obtain the required output voltage in the line side of the inverter. Real-time methods for PWM generation can be broadly classified into triangle comparison based PWM (TCPWM) and space vector based PWM (SVPWM). In TCPWM methods such as sine-triangle PWM, three phase reference modulating signals are compared against a common triangular carrier to generate the PWM signals for the three phases. In SVPWM methods, a revolving reference voltage vector is provided as voltage reference instead of three phase modulating waves. The magnitude and frequency of the fundamental component in the line side are controlled by the magnitude and frequency, respectively, of the reference vector. The fundamental line side voltage is proportional to the reference magnitude during linear modulation. With sine-triangle PWM, the highest possible peak phase fundamental voltage is 0.5Vdc, where Vdc is the DC bus voltage, in the linear modulation zone. With techniques such as third harmonic injection PWM and space vector based PWM, the peak phase fundamental voltage can be as high as (formula) (i.e., 0:577Vdc)during linear modulation. To increase the line side voltage further, the operation of the VSI must be extended into the overmodulation region. The overmodulation region extends upto the six-step mode, which gives the highest possible ac voltage for a given (formula). In TCPWM based methods, increasing the reference magnitude beyond a certain level leads to pulse dropping, and gradually leads to six-step operation. However, in SVPWM methods, an overmodulation algorithm is required for controlling the line-side voltage during overmodulation and to achieve a smooth transition from PWM to six-step mode. Numerous overmodulation algorithms have been proposed in the literature for space vector modulated inverter. A well known algorithm among these divides the overmodulation zone into two zones, namely zone-I and zone-II. This is termed as the 'existing overmodulation algorithm' here. This algorithm is modified in the present work to reduce computational burden without much increase in the line current distortion. During overmodulation, the fundamental line side voltage and the reference magnitude are not proportional, which is undesirable from the control point of view. The present work ensures a linear relationship between the two. Apart from the fundamental component, the inverter output voltage mainly consists of harmonic components at high frequencies (around switching frequency and the integral multiples) during linear modulation. However, during overmodulation, low order harmonic components such as 5th, 7th, 11th, 13th etc., are also present in the output voltage. These low order harmonic voltages lead to low order harmonic currents in the motor. The sum of the lower order harmonic currents is termed as 'lower order current ripple'. The present thesis proposes a method for estimation of lower order current ripple in real-time. In closed loop current control, the motor current is fed back to the current controller. During overmodulation, the motor current contains low order harmonics, which appear in the current error fed to the controller. These harmonic currents are amplified by the current error amplifier deteriorating the performance of the drive. It is possible to filter the lower order harmonic currents before being fed back. However, filtering introduces delay in the current loop, and reduces the bandwidth even during linear modulation. In the present work, the estimated lower order current ripple is subtracted from the measured current before the latter is fed back to the controller. The estimation of lower order current ripple and the proposed current control are verified through simulation using MATLAB/SIMULINK and also experimentally on a laboratory prototype. The experimental setup comprises of a field programmable gate arrays (FPGA) based digital controller, an IGBT based inverter and a four-pole squirrel cage induction motor. (Pl refer the original document for formula)

The realisation of signal processing algorithms in fixed-point offers substantial performance advantages over floating-point realisations. However, it is widely acknowledged that the task of realising algorithms in fixed-point is a challenging one with limited tool support. This thesis examines various aspects related to the translation of algorithms, given in infinite precision or floating-point, into fixed-point. In particular, this thesis reports on the implementation of a given algorithm, an EDFA (Erbium-Doped Fibre Amplifier) control algorithm, on a FPGA (Field Programmable Gate Array) using fixed-point arithmetic. An analytical approach is proposed that allows the automated realisation of algorithms in fixedpoint. The technique provides fixed-point parameters for a given floating-point model that satisfies a precision constraint imposed on the primary output of the algorithm to be realised. The development of a simulation framework based on this analysis allows fixed-point designs to be generated in a shorter time frame. Albeit being limited to digital algorithms that can be represented as a data flow graph (DFG), the approach developed in the thesis allows for a speed up in the design and development cycle, reduces the possibility of error and eases the overall effort involved in the process. It is shown in this thesis that a fixed-point realisation of an EDFA control algorithm using this technique produces results that satisfy the given constraints.