Sökning: "Computer architecture"
Visar resultat 1 - 5 av 663 avhandlingar innehållade orden Computer architecture.
1. Early Stage Architectural Design Practice Perspectives on Life Cycle Building Performance Assessment
Sammanfattning : Architectural practitioners can avoid negative social and environmental impacts of new construction by making decisions supported by impact quantification during design processes. However, most software tools developed for such quantification see little use in practice, especially in early design stages when decisions have the greatest influence. LÄS MER
2. Advancing Automation in Digital Forensic Investigations
Sammanfattning : Digital Forensics is used to aid traditional preventive security mechanisms when they fail to curtail sophisticated and stealthy cybercrime events. The Digital Forensic Investigation process is largely manual in nature, or at best quasi-automated, requiring a highly skilled labour force and involving a sizeable time investment. LÄS MER
3. Rethinking Speculative Execution from a Security Perspective
Sammanfattning : Speculative out-of-order execution is one of the fundamental building blocks of modern, high-performance processors. To maximize the utilization of the system's resources, hardware and software security checks in the speculative domain can be temporarily ignored, without affecting the correctness of the application, as long as no architectural changes are made before transitioning to the non-speculative domain. LÄS MER
4. QoS Driven Coordinated Management of Resources to Save Energy in Multi-Core Systems
Sammanfattning : Reducing the energy consumption of computing systems is a necessary endeavor. However, saving energy should not come at the expense of degrading user experience. To this end, in this thesis, we assume that applications running on multi-core processors are associated with a quality-of-service (QoS) target in terms of performance constraints. 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