The embarrassingly parallel nature of deep learning training tasks makes CPU-GPU co-
processors the primary commodity hardware for them. The computing and memory …

Evaluating hardware for deep learning is challenging. The models can take days or more to
run, the datasets are generally larger than what fits into memory, and the models are …

Training deep learning models is a repetitive and resource-intensive process. Data
scientists often train several models before landing on set of parameters (eg, hyper …

GPUs are becoming a scarce resource in high demand, as many teams build and train
increasingly advanced artificial intelligence workloads. As GPUs become more performant …

Abstract Graphics Processing Units (GPUs) are crucial for Deep Neural Network (DNN)
operations, yet they often suffer from under-utilization due to overallocation or workloads …

This thesis examines the impact of noise and deviation size on the performance of deep
neural networks (DNNs) in classifying deviations in photomask images. The study used a …

El objetivo de este trabajo es el estudio de métodos de rellenado de mapas de anisotropías
en temperatura del Fondo Cósmico de Microondas (FCM) basados en técnicas de …