Sökning: "hardware resources"

Visar resultat 1 - 5 av 299 uppsatser innehållade orden hardware resources.

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. Difference Between Memory-based Storage and Register-based Storage on FPGAs

   Master-uppsats, Linköpings universitet/Institutionen för systemteknik

   Författare :Yiqian Cui; [2023]
   Nyckelord :;

   Sammanfattning : Memory-based storage and register-based storage are commonly used storagetypes in fpgas. This thesis aims to build up the architecture of memory-basedstorage and register-based storage, implement the corresponding methods, compare the difference between them and determine which kind of storage workswell under different circumstances. LÄS MER

4. 3. EMONAS : Evolutionary Multi-objective Neuron Architecture Search of Deep Neural Network

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

   Författare :Jiayi Feng; [2023]
   Nyckelord :DNN Deep Neural Network ; NAS Neural Architecture Search ; EA Evolutionary Algorithm ; Multi-Objective Optimization; Binary One Optimization; Embedded Systems; DNN Deep Neural Network ; NAS Neural Architecture Search ; EA Evolutionary Algorithm ; Multi-Objective Optimization; Binary One Optimization; Inbyggda system;

   Sammanfattning : Customized Deep Neural Network (DNN) accelerators have been increasingly popular in various applications, from autonomous driving and natural language processing to healthcare and finance, etc. However, deploying them directly on embedded system peripherals within real-time operating systems (RTOS) is not easy due to the paradox of the complexity of DNNs and the simplicity of embedded system devices. LÄS MER

5. 4. Minimizing the clock drift in partially synchronized heterogeneous TSN networks

   Uppsats för yrkesexamina på grundnivå, Mälardalens universitet/Akademin för innovation, design och teknik

   Författare :Balqis Yusuf; [2023]
   Nyckelord :;

   Sammanfattning : The new generation of embedded systems will increase interaction between the environment, people, and autonomous devices. This will increase their need for communication, particularly in meeting real-time requirements. LÄS MER

7. 5. A comparative performance analysis of Fast Fourier Transformation and Gerstner waves

   Kandidat-uppsats, Blekinge Tekniska Högskola/Institutionen för datavetenskap

   Författare :Morgan Westerberg; Oliver Olguin Jönsson; [2023]
   Nyckelord :Water simulation; Procedural methods; Ocean waves; Fast Fourier Transformation; Gerstner waves;

   Sammanfattning : Background:  As time moves on hardware is able to tackle heavier and more complex computations in real-time systems. This means that more realistic and stylistic environments can be computed. One of these environments is the ocean. LÄS MER