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...
Objective: To perform a systematic review and meta-analysis on the prevalence rate of mental health disorders including anxiety, depression, and insomnia during the COVID-19 pandemic in Eastern Europe in the general population, as well as within select sub-populations (i.e., students, general healthcare workers, and frontline workers).
Data sources: Articles in PubMed,...
Data centers have seen a rapid growth in recent years as the technology industry is relying more on cloud services. In effect, data centers are paying millions of US $ in energy costs. Several strategies have been proposed to lower the total energy cost at data centers- task migration, server...
The Machine Learning (ML) algorithms are increasingly explored in varies of fields including designing and optimizing computer systems. Recent research, such as optimizing memory/cache prefetching by ML training or predicting traffic pattern in throughput processors, also exhibits a promising future of introducing ML into computer system design and optimization. Throughput...
Idaho National Laboratories recently conducted micromechanical testing of nano-recrystallized UO₂ as a part of an ongoing investigation of High Burnup Structure (HBS). The goals of the test were to determine the fracture stress, and the elastic modulus of subdivided grains within UO₂ using ion irradiation rather than neutron irradiation to...
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....
The machine learning and deep learning models have been very lightly explored in analyzing the behavior of On-Chip network traffic. These models have proven their potential in pattern recognition, classification etc... In this paper we analyze the spatial pattern that each workload exhibits in its life cycle during execution. We...
Routerless networks on chip (NoCs) were recently introduced as an alternative to traditional mesh-based scalable networks, promising improved performance and scalability with a large decrease in power and area usage by the elimination of routing on in-flight packets. Without mitigation, most network designs, including routerless, are susceptible to loss of...
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...