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Hardware For Quantized Mixed-Precision Deep Neural Networks, Andres Rios Aug 2021

Hardware For Quantized Mixed-Precision Deep Neural Networks, Andres Rios

Open Access Theses & Dissertations

Recently, there has been a push to perform deep learning (DL) computations on the edge rather than the cloud due to latency, network connectivity, energy consumption, and privacy issues. However, state-of-the-art deep neural networks (DNNs) require vast amounts of computational power, data, and energyâ??resources that are limited on edge devices. This limitation has brought the need to design domain-specific architectures (DSAs) that implement DL-specific hardware optimizations. Traditionally DNNs have run on 32-bit floating-point numbers; however, a body of research has shown that DNNs are surprisingly robust and do not require all 32 bits. Instead, using quantization, networks can run on …


Application Of Machine Learning In Flood Depth Prediction, Armando Esquivel May 2021

Application Of Machine Learning In Flood Depth Prediction, Armando Esquivel

Open Access Theses & Dissertations

Machine learning technologies have helped provide answers for problems with a high degree of complexity. Machine learning has been utilized by various disciplines within the Civil Engineering profession and has proven to be efficient in solving complex problems. Although machine learning is being used in the Civil Engineering profession, a formal framework on developing and integrating machine learning has not been developed for flood depth prediction. The proposed word uses machine learning to predict the depth of flood at Houston, TX, due to a 100-year 24-hour storm. The proposed work can be used to collect, store and analyze data to …


On Using Demographic Data With Deprivation Index For Predicting Chronic Diseases, Olugbenga Temitope Iyiola May 2021

On Using Demographic Data With Deprivation Index For Predicting Chronic Diseases, Olugbenga Temitope Iyiola

Open Access Theses & Dissertations

Researchers have worked on modeling and predicting the likelihood of developingchronic diseases, such as diabetes and high blood pressure, using medical data (e.g., heart-rate, blood sugar). However, many of these diseases demonstrate strong links with demographics and socio-economic status (e.g., race, gender, income). It is also less time-consuming to retrieve demographic and socio-economic data, some of which are publicly available through US Census Bureau, than to carry out medical tests. Hence, demographic data can give a quicker estimate of the susceptibility of a person to a chronic disease.

In this work, we study the effect of using medical vs. demographics …