Sökning: "Computer art"
Visar resultat 1 - 5 av 484 avhandlingar innehållade orden Computer art.
1. Explainable and Resource-Efficient Stream Processing Through Provenance and Scheduling
Sammanfattning : In our era of big data, information is captured at unprecedented volumes and velocities, with technologies such as Cyber-Physical Systems making quick decisions based on the processing of streaming, unbounded datasets. In such scenarios, it can be beneficial to process the data in an online manner, using the stream processing paradigm implemented by Stream Processing Engines (SPEs). LÄS MER
2. High Performance Hybrid Memory Systems with 3D-stacked DRAM
Sammanfattning : The bandwidth of traditional DRAM is pin limited and so does not scale well with the increasing demand of data intensive workloads limiting performance. 3D-stacked DRAM can alleviate this problem providing substantially higher bandwidth to a processor chip. LÄS MER
3. Clustering in the Big Data Era: methods for efficient approximation, distribution, and parallelization
Sammanfattning : Data clustering is an unsupervised machine learning task whose objective is to group together similar items. As a versatile data mining tool, data clustering has numerous applications, such as object detection and localization using data from 3D laser-based sensors, finding popular routes using geolocation data, and finding similar patterns of electricity consumption using smart meters. LÄS MER
4. Functional EDSLs for Web Applications
Sammanfattning : This thesis aims to make the development of complex web applications easier, faster and safer through the application of strongly typed functional programming techniques. Traditional web applications are commonly written in the de facto standard language of the web, JavaScript, which, being untyped, provides no guarantees regarding the data processed by programs, increasing the burden of testing and defensive programming. LÄS MER
5. Multi-LSTM Acceleration and CNN Fault Tolerance
Sammanfattning : This thesis addresses the following two problems related to the field of Machine Learning: the acceleration of multiple Long Short Term Memory (LSTM) models on FPGAs and the fault tolerance of compressed Convolutional Neural Networks (CNN). LSTMs represent an effective solution to capture long-term dependencies in sequential data, like sentences in Natural Language Processing applications, video frames in Scene Labeling tasks or temporal series in Time Series Forecasting. LÄS MER
