Sökning: "Data flow architectures"

Visar resultat 1 - 5 av 23 uppsatser innehållade orden Data flow architectures.

1. 1. Exploring Normalizing Flow Modifications for Improved Model Expressivity

   Master-uppsats, KTH/Skolan för elektroteknik och datavetenskap (EECS)

   Författare :Marcel Juschak; [2023]
   Nyckelord :Normalizing Flows; Motion Synthesis; Invertible Neural Networks; Glow; MoGlow; Maximum Likelihood Estimation; Generative models; normaliserande flöden; rörelsesyntes; inverterbara neurala nätverk; Glow; MoGlow; maximum likelihood-skattning generativa modeller;

   Sammanfattning : Normalizing flows represent a class of generative models that exhibit a number of attractive properties, but do not always achieve state-of-the-art performance when it comes to perceived naturalness of generated samples. To improve the quality of generated samples, this thesis examines methods to enhance the expressivity of discrete-time normalizing flow models and thus their ability to capture different aspects of the data. LÄS MER

3. 2. Low-power Acceleration of Convolutional Neural Networks using Near Memory Computing on a RISC-V SoC

   Master-uppsats, Lunds universitet/Institutionen för elektro- och informationsteknik

   Författare :Kristoffer Westring; Linus Svensson; [2023]
   Nyckelord :FPGA; ASIC; Near Memory Computing; RISC-V; Convolutional Neural Network; Technology and Engineering;

   Sammanfattning : The recent peak in interest for artificial intelligence, partly fueled by language models such as ChatGPT, is pushing the demand for machine learning and data processing in everyday applications, such as self-driving cars, where low latency is crucial and typically achieved through edge computing. The vast amount of data processing required intensifies the existing performance bottleneck of the data movement. LÄS MER

4. 3. Prediction and Analysis of 5G beyond Radio Access Network

   Master-uppsats, Uppsala universitet/Institutionen för informationsteknologi

   Författare :Gaurav Singh; Shreyansh Singh; [2023]
   Nyckelord :- LSTM; RNN; AE-LSTM; Deep Learning; Machine Learning; network traffic flow; forecasting; quality-of-service.;

   Sammanfattning : Network traffic forecasting estimates future network traffic based on historical traffic observations. It has a wide range of applications, and substantial attention has been dedicated to this research area. LÄS MER

5. 4. Precipitation Nowcasting using Deep Neural Networks

   Master-uppsats, KTH/Fysik

   Författare :Valter Fallenius; [2022]
   Nyckelord :Deep Neura Networks; Artificial Intelligence; Meteorology; Precipitationa Nowcasting; Djupa neurala nätverk; artificiell intelligens; nederbördsprognoser; meteorologi;

   Sammanfattning : Deep neural networks (DNNs) based on satellite and radar data have shown promising results for precipitation nowcasting, beating physical models and optical flow for time horizons up to 8 hours. “MetNet”, developed by Google AI, is a 225 million parameter DNN combining three different types of architectures that was trained on satellite and radar data over the United States. LÄS MER

7. 5. Using GPU-aware message passing to accelerate high-fidelity fluid simulations

   Master-uppsats, KTH/Skolan för elektroteknik och datavetenskap (EECS)

   Författare :Jacob Wahlgren; [2022]
   Nyckelord :high-performance computing; computational fluid dynamics; spectral element method; graphical processing units; message passing interface; högprestandaberäkningar; beräkningsströmningsdynamik; spektralelementmetoden; grafikprocessorer; meddelandeförmedlingsgränssnitt;

   Sammanfattning : Motivated by the end of Moore’s law, graphics processing units (GPUs) are replacing general-purpose processors as the main source of computational power in emerging supercomputing architectures. A challenge in systems with GPU accelerators is the cost of transferring data between the host memory and the GPU device memory. LÄS MER