Sökning: "Data hårdvara"

Visar resultat 1 - 5 av 254 uppsatser innehållade orden Data hårdvara.

1. 1. Utilizing energy-saving techniques to reduce energy and memory consumption when training machine learning models : Sustainable Machine Learning

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

   Författare :Khalid El Yaacoub; [2024]
   Nyckelord :Sustainable AI; Machine learning; Quantization-Aware Training; Model Distillation; Quantized Distillation; Siamese Neural Networks; Continual Learning; Experience Replay; Data Efficient AI; Energy Consumption; Energy-Savings; Sustainable ML; Computation resources; Hållbar maskin inlärning; Hållbar AI; Maskininlärning; Quantization-Aware Training; Model Distillation; Quantized Distillation; siamesiska neurala nätverk; Continual Learning; Experience Replay; Dataeffektiv AI; Energiförbrukning; Energibesparingar; Beräkningsresurser;

   Sammanfattning : Emerging machine learning (ML) techniques are showing great potential in prediction performance. However, research and development is often conducted in an environment with extensive computational resources and blinded by prediction performance. LÄS MER

3. 2. Code Synthesis for Heterogeneous Platforms

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

   Författare :Zhouxiang Fu; [2023]
   Nyckelord :Code Synthesis; Heterogeneous Platform; Zero-Overhead Topology Infrastructure; Kodsyntes; Heterogen plattform; Zero-Overhead Topologi Infrastruktur;

   Sammanfattning : Heterogeneous platforms, systems with both general-purpose processors and task-specific hardware, are largely used in industry to increase efficiency, but the heterogeneity also increases the difficulty of design and verification. We often need to wait for the completion of all the modules to know whether the functionality of the design is correct or not, which can cause costly and tedious design iteration cycles. LÄS MER

4. 3. Exploration and Evaluation of RNN Models on Low-Resource Embedded Devices for Human Activity Recognition

   Master-uppsats, KTH/Mekatronik och inbyggda styrsystem

   Författare :Helgi Hrafn Björnsson; Jón Kaldal; [2023]
   Nyckelord :Recurrent Neural Networks; Long Short-Term Memory Networks; Embedded Systems; Human Activity Recognition; Edge AI; TensorFlow Lite Micro; Recurrent Neural Networks; Long Short-Term Memory Networks; Innbyggda systyem; Mänsklig aktivitetsigenkänning; Edge AI; TensorFlow Lite Micro;

   Sammanfattning : Human activity data is typically represented as time series data, and RNNs, often with LSTM cells, are commonly used for recognition in this field. However, RNNs and LSTM-RNNs are often too resource-intensive for real-time applications on resource constrained devices, making them unsuitable. LÄS MER

5. 4. A Comparison Between Implementations of Cryptographic Algorithms and Their Efficiency in Smartphone Computing Environments : Exploring the Performance Trade-offs of Performing Cryptographic Work in Different Smartphone Development Frameworks

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

   Författare :Ossian Dillner; [2023]
   Nyckelord :Cryptography; Security; Privacy; Smartphones; Kryptografi; Säkerhet; Integritet; Smartphones;

   Sammanfattning : In the recent decade concern for privacy has massively increased in the public consciousness. As a result of this development, demand for end-to-end secure communications in all manner of applications has also seen a rapid increase. This development can however often be at odds with other consumer demands. LÄS MER

7. 5. Comparative Study of the Inference of an Image Quality Assessment Algorithm : Inference Benchmarking of an Image Quality Assessment Algorithm hosted on Cloud Architectures

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

   Författare :Jesper Petersson; [2023]
   Nyckelord :Machine learning; Cloud Computing; Benchmark; Image Quality Assessment; Maskininlärning; Molntjänster; Jämförelse; Bildkvalitetsbedömning;

   Sammanfattning : an instance has become exceedingly more time and resource consuming. To solve this issue, cloud computing is being used to train and serve the models. However, there’s a gap in research where these cloud computing platforms have been evaluated for these tasks. LÄS MER