Direct Digital Frequency Synthesizer Architecture for Wireless Communication in 90 NM CMOS Technology

Software radio is one promising field that can meet the demands for low cost, low power, and high speed electronic devices for wireless communication. At the heart of software radio is a programmable oscillator called a Direct Digital Synthesizer (DDS). DDS has the capabilities of rapid frequency hopping by digital software control while operating at very high frequencies and having sub-hertz resolution. Nevertheless, the digital-to-analog converter (DAC) and the read-only-memory (ROM) look-up table, building blocks of the DDS, prevent the DDS to be used in wireless communication because they introduce errors and noises to the DDS and their performances deteriorate at high speed. The DAC and ROM are replaced in this thesis by analog active filters that convert the square wave output of the phase accumulator directly into a sine wave. The proposed architecture operates with a reference clock of 9.09 GHz and can be fully-integrated in 90 nm CMOS technology

Approximate computing: An integrated cross-layer framework

A new design approach, called approximate computing (AxC), leverages the flexibility provided by intrinsic application resilience to realize hardware or software implementations that are more efficient in energy or performance. Approximate computing techniques forsake exact (numerical or Boolean) equivalence in the execution of some of the application’s computations, while ensuring that the output quality is acceptable. While early efforts in approximate computing have demonstrated great potential, they consist of ad hoc techniques applied to a very narrow set of applications, leaving in question the applicability of approximate computing in a broader context. The primary objective of this thesis is to develop an integrated cross-layer approach to approximate computing, and to thereby establish its applicability to a broader range of applications. The proposed framework comprises of three key components: (i) At the circuit level, systematic approaches to design approximate circuits, or circuits that realize a slightly modified function with improved efficiency, (ii) At the architecture level, utilize approximate circuits to build programmable approximate processors, and (iii) At the software level, methods to apply approximate computing to machine learning classifiers, which represent an important class of applications that are being utilized across the computing spectrum. Towards this end, the thesis extends the state-of-the-art in approximate computing in the following important directions. Synthesis of Approximate Circuits: First, the thesis proposes a rigorous framework for the automatic synthesis of approximate circuits , which are the hardware building blocks of approximate computing platforms. Designing approximate circuits involves making judicious changes to the function implemented by the circuit such that its hardware complexity is lowered without violating the specified quality constraint. Inspired by classical approaches to Boolean optimization in logic synthesis, the thesis proposes two synthesis tools called SALSA and SASIMI that are general, i.e., applicable to any given circuit and quality specification. The framework is further extended to automatically design quality configurable circuits , which are approximate circuits with the capability to reconfigure their quality at runtime. Over a wide range of arithmetic circuits, complex modules and complete datapaths, the circuits synthesized using the proposed framework demonstrate significant benefits in area and energy. Programmable AxC Processors: Next, the thesis extends approximate computing to the realm of programmable processors by introducing the concept of quality programmable processors (QPPs). A key principle of QPPs is that the notion of quality is explicitly codified in their HW/SW interface i.e., the instruction set. Instructions in the ISA are extended with quality fields, enabling software to specify the accuracy level that must be met during their execution. The micro-architecture is designed with hardware mechanisms to understand these quality specifications and translate them into energy savings. As a first embodiment of QPPs, the thesis presents a quality programmable 1D/2D vector processor QP-Vec, which contains a 3-tiered hierarchy of processing elements. Based on an implementation of QP-Vec with 289 processing elements, energy benefits up to 2.5X are demonstrated across a wide range of applications. Software and Algorithms for AxC: Finally, the thesis addresses the problem of applying approximate computing to an important class of applications viz. machine learning classifiers such as deep learning networks. To this end, the thesis proposes two approaches—AxNN and scalable effort classifiers. Both approaches leverage domain- specific insights to transform a given application to an energy-efficient approximate version that meets a specified application output quality. In the context of deep learning networks, AxNN adapts backpropagation to identify neurons that contribute less significantly to the network’s accuracy, approximating these neurons (e.g., by using lower precision), and incrementally re-training the network to mitigate the impact of approximations on output quality. On the other hand, scalable effort classifiers leverage the heterogeneity in the inherent classification difficulty of inputs to dynamically modulate the effort expended by machine learning classifiers. This is achieved by building a chain of classifiers of progressively growing complexity (and accuracy) such that the number of stages used for classification scale with input difficulty. Scalable effort classifiers yield substantial energy benefits as a majority of the inputs require very low effort in real-world datasets. In summary, the concepts and techniques presented in this thesis broaden the applicability of approximate computing, thus taking a significant step towards bringing approximate computing to the mainstream. (Abstract shortened by ProQuest.

Digit-slicing architectures for real-time digital filters

One of the many important algorithmic techniques in digital signal processing is real-time digital filtering. Modular sliced structures for digital filters have been proposed before, but the nature of implementation has been mainly constrained to non-recursive second order digital filters with positive values of coefficients. The aim of this research project is to extend this modular digit slicing concept to more practical higher order digital filters which are recursive and are of many forms (direct, nondirect, canonic, non-canonic). [Continues.

Synthesis methods for linear-phase FIR filters with a piecewise-polynomial impulse response

his thesis concentrates on synthesis methods for linear-phase finite-impulse response filters with a piecewise-polynomial impulse response. One of the objectives has been to find integer-valued coefficients to efficiently implement filters of the piecewise-polynomial impulse response approach introduced by Saram¨aki and Mitra. In this method, the impulse response is divided into blocks of equal length and each block is created by a polynomial of a given degree. The arithmetic complexity of these filters depends on the polynomial degree and the number of blocks. By using integer-valued coefficients it is possible to make the implementation of the subfilters, which generates the polynomials, multiplication-free. The main focus has been on finding computationally-efficient synthesis methods by using a piecewise-polynomial and a piecewise-polynomial-sinusoidal impulse responses to make it possible to implement high-speed, low-power, highly integrated digital signal processing systems. The earlier method by Chu and Burrus has been studied. The overall impulse response of the approach proposed in this thesis consists of the sum of several polynomial-form responses. The arithmetic complexity depends on the polynomial degree and the number of polynomial-form responses. The piecewise-polynomial-sinusoidal approach is a modification of the piecewise-polynomial approach. The subresponses are multiplied by a sinusoidal function and an arbitrary number of separate center coefficients is added. Thereby, the arithmetic complexity depends also on the number of complex multipliers and separately generated center coefficients. The filters proposed in this thesis are optimized by using linear programming methods

New strategies for low noise, agile PLL frequency synthesis

Phase-Locked Loop based frequency synthesis is an essential technique employed in wireless communication systems for local oscillator generation. The ultimate goal in any design of frequency synthesisers is to generate precise and stable output frequencies with fast switching and minimal spurious and phase noise. The conflict between high resolution and fast switching leads to two separate integer synthesisers to satisfy critical system requirements. This thesis concerns a new sigma-delta fractional-N synthesiser design which is able to be directly modulated at high data rates while simultaneously achieving good noise performance. Measured results from a prototype indicate that fast switching, low noise and spurious free spectra are achieved for most covered frequencies. The phase noise of the unmodulated synthesiser was measured −113 dBc/Hz at 100 kHz offset from the carrier. The intermodulation effect in synthesisers is capable of producing a family of spurious components of identical form to fractional spurs caused in quantisation process. This effect directly introduces high spurs on some channels of the synthesiser output. Numerical and analytic results describing this effect are presented and amplitude and distribution of the resulting fractional spurs are predicted and validated against simulated and measured results. Finally an experimental arrangement, based on a phase compensation technique, is presented demonstrating significant suppression of intermodulation-borne spurs. A new technique, pre-distortion noise shaping, is proposed to dramatically reduce the impact of fractional spurs in fractional-N synthesisers. The key innovation is the introduction in the bitstream generation process of carefully-chosen set of components at identical offset frequencies and amplitudes and in anti-phase with the principal fractional spurs. These signals are used to modify the Σ-Δ noise shaping, so that fractional spurs are effectively cancelled. This approach can be highly effective in improving spectral purity and reduction of spurious components caused by the Σ-Δ modulator, quantisation noise, intermodulation effects and any other circuit factors. The spur cancellation is achieved in the digital part of the synthesiser without introducing additional circuitry. This technique has been convincingly demonstrated by simulated and experimental results

A custom computing framework for orientation and photogrammetry

Thesis (Ph.D.)--Massachusetts Institute of Technology, Dept. of Electrical Engineering and Computer Science, 2000.Includes bibliographical references (p. 211-223).This electronic version was submitted by the student author. The certified thesis is available in the Institute Archives and Special Collections.There is great demand today for real-time computer vision systems, with applications including image enhancement, target detection and surveillance, autonomous navigation, and scene reconstruction. These operations generally require extensive computing power; when multiple conventional processors and custom gate arrays are inappropriate, due to either excessive cost or risk, a class of devices known as Field-Programmable Gate Arrays (FPGAs) can be employed. FPGAs per the flexibility of a programmable solution and nearly the performance of a custom gate array. When implementing a custom algorithm in an FPGA, one must be more efficient than with a gate array technology. By tailoring the algorithms, architectures, and precisions, the gate count of an algorithm may be sufficiently reduced to t into an FPGA. The challenge is to perform this customization of the algorithm, while still maintaining the required performance. The techniques required to perform algorithmic optimization for FPGAs are scattered across many fields; what is currently lacking is a framework for utilizing all these well known and developing techniques. The purpose of this thesis is to develop this framework for orientation and photogrammetry systems.by Paul D. Fiore.Ph.D