This thesis covers a wide area of the North Pacific Ocean, latitudinally from 10° to 55°N and longitudinally from Japan to the U.S. coast. With two longitudinal cruises as the main data sources and selected literature data sets as supplementary data source, the distribution of physical and chemical properties in...
Nanoscale Instrumented Indentation Testing (IIT) is a material characterization technique that is used to determine mechanical properties. The size effects present at this scale make it difficult to expand findings to a bulk scale. Modeling can be used to bridge this gap and better understand nanoscale IIT and the size...
This thesis presents a novel design for a rheometer style viscometer by implementing high temperature and inert atmospheric requirements on the device. The goals of this device are to measure the viscosity of high temperature molten salts, which are corrosive and extremely hydroscopic. It also aims to be able to...
Compactness in deep learning can be critical to a model’s viability in low-resource applications, and a common approach to extreme model compression is quantization. We consider Iterative Product Quantization (iPQ) with Quant-Noise [Fan et al., 2020] to be state-of-the-art in this area, but this quantization framework suffers from preventable inference...
Various natural language processing (NLP) tasks necessitate deep models that are fast, efficient, and small based on their ultimate application at the edge or elsewhere. While significant investigation has furthered the efficiency and reduced the size of these models, reducing their downstream latency without significant trade-offs remains a difficult task....
Machine learning applied to computer architecture has rapidly transitioned from a theoretical novelty to being a driving force behind design, control, and simulation in practically all components. These machine-learning-based methodologies are further notable for their scalability to increasingly complex design challenges, which has allowed these methodologies to surpass the prior...
Simultaneous speech-to-text translation remains a difficult yet important problem for modern machine learning models whereby a text translation is generated concurrently with receiving partial speech inputs. One state-of-the-art simultaneous speech-to-text model is the augmented memory transformer whose encoder breaks a speech input into fixed-size overlapping segments composed of left, right,...
As the number of nodes in high-performance computing (HPC) systems continues to grow, it becomes increasingly important to design scalable interconnection network topologies. Prior work has shown promise in adding random shortcuts on top of an existing topology to reduce average hop count and network diameter, but has been limited...
