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Description
As digital images are transmitted over the network or stored on a disk, image processing is done as part of the standard for efficient storage and bandwidth. This causes some amount of distortion or artifacts in the image which demands the need for quality assessment. Subjective image quality assessment is expensive, time consuming and influenced by the subject's perception. Hence, there is a need for developing mathematical models that are capable of predicting the quality evaluation. With the advent of the information era and an exponential growth in image/video generation and consumption, the requirement for automated quality assessment has become mandatory to assess the degradation. The last few decades have seen research on automated image quality assessment (IQA) algorithms gaining prominence. However, the focus has been on achieving better predication accuracy, and not on improving computational performance. As a result, existing serial implementations require a lot of time in processing a single frame. In the last 5 years, research on general-purpose graphic processing unit (GPGPU) based image quality assessment (IQA) algorithm implementation has shown promising results for single images. Still, the implementations are not efficient enough for deployment in real world applications, especially for live videos at high resolution. Hence, in this thesis, it is proposed that microarchitecture-conscious coding on a graphics processing unit (GPU) combined with detailed understanding of the image quality assessment (IQA) algorithm can result in non-trivial speedups without compromising quality prediction accuracy. This document focusses on the microarchitectural analysis of the most apparent distortion (MAD) algorithm. The results are analyzed in-depth and one of the major bottlenecks is identified. With the knowledge of underlying microarchitecture, the implementation is restructured thereby resolving the bottleneck and improving the performance.
ContributorsKannan, Vignesh (Author) / Sohoni, Sohum (Thesis advisor) / Ren, Fengbo (Committee member) / Sayeed, Mohamed (Committee member) / Arizona State University (Publisher)
Created2016
Description
Many real-world engineering problems require simulations to evaluate the design objectives and constraints. Often, due to the complexity of the system model, simulations can be prohibitive in terms of computation time. One approach to overcome this issue is to construct a surrogate model, which approximates the original model. The focus of this work is on the data-driven surrogate models, in which empirical approximations of the output are performed given the input parameters. Recently neural networks (NN) have re-emerged as a popular method for constructing data-driven surrogate models. Although, NNs have achieved excellent accuracy and are widely used, they pose their own challenges. This work addresses two common challenges, the need for: (1) hardware acceleration and (2) uncertainty quantification (UQ) in the presence of input variability. The high demand in the inference phase of deep NNs in cloud servers/edge devices calls for the design of low power custom hardware accelerators. The first part of this work describes the design of an energy-efficient long short-term memory (LSTM) accelerator. The overarching goal is to aggressively reduce the power consumption and area of the LSTM components using approximate computing, and then use architectural level techniques to boost the performance. The proposed design is synthesized and placed and routed as an application-specific integrated circuit (ASIC). The results demonstrate that this accelerator is 1.2X and 3.6X more energy-efficient and area-efficient than the baseline LSTM. In the second part of this work, a robust framework is developed based on an alternate data-driven surrogate model referred to as polynomial chaos expansion (PCE) for addressing UQ. In contrast to many existing approaches, no assumptions are made on the elements of the function space and UQ is a function of the expansion coefficients. Moreover, the sensitivity of the output with respect to any subset of the input variables can be computed analytically by post-processing the PCE coefficients. This provides a systematic and incremental method to pruning or changing the order of the model. This framework is evaluated on several real-world applications from different domains and is extended for classification tasks as well.
ContributorsAzari, Elham (Author) / Vrudhula, Sarma (Thesis advisor) / Fainekos, Georgios (Committee member) / Ren, Fengbo (Committee member) / Yang, Yezhou (Committee member) / Arizona State University (Publisher)
Created2021