Sökning: "strömförbrukning"

Visar resultat 1 - 5 av 80 uppsatser innehållade ordet strömförbrukning.

1. 1. 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

3. 2. Performance Evaluation of LoRa networks for Air-to-Ground Communications

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

   Författare :Kiana Khorsandi; Sareh Jalalizad; [2023]
   Nyckelord :IoT; LPWAN; LoRaWan; UAV; LAP; Bit error rate; Line of sight;

   Sammanfattning : The current focus on the Internet of Things (IoT) has led to the emergence of many network scenarios with unlimited use cases, including smart homes, smart cities, smart agriculture, and more. Unmanned aerial vehicles (UAVs), also known as drones, have become increasingly popular due to their versatility and ability to collect and transmit data through various sensors and cameras. LÄS MER

4. 3. Low-power Implementation of Neural Network Extension for RISC-V CPU

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

   Författare :Dario Lo Presti Costantino; [2023]
   Nyckelord :Artificial intelligence; Deep learning; Neural networks; Edge computing; Convolutional neural networks; Low-power electronics; RISC-V; AI accelerators; Parallel processing; Artificiell intelligens; Deep learning; Neurala nätverk; Edge computing; konvolutionella neurala nätverk; Lågeffektelektronik; RISC-V; AI-acceleratorer; Parallell bearbetning;

   Sammanfattning : Deep Learning and Neural Networks have been studied and developed for many years as of today, but there is still a great need of research on this field, because the industry needs are rapidly changing. The new challenge in this field is called edge inference and it is the deployment of Deep Learning on small, simple and cheap devices, such as low-power microcontrollers. LÄS MER

5. 4. Design space exploration for co-mapping of periodic and streaming applications in a shared platform

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

   Författare :Zhang Yuhan; [2023]
   Nyckelord :Design Space Exploration; Periodically activated tasks; Synchronous dataflow; IDeSyDe; Designutrymmesutforskning; Periodiskt aktiverade uppgifter; Synkron data-flöde; IDeSyDe;

   Sammanfattning : As embedded systems advance, the complexity and multifaceted requirements of products have increased significantly. A trend in this domain is the selection of different types of application models and multiprocessors as the platform. LÄS MER

7. 5. Mobile Traffic Classification and Multi-Cell Base Station Control for Energy-Efficient 5G Networks

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

   Författare :Cai Tianzhang; [2023]
   Nyckelord :;

   Sammanfattning : The global energy consumption of mobile networks is rapidly increasing due to the exponential growth of mobile network traffic. The advent of next-generation cellular technologies such as fifth-generation (5G) and beyond promises higher network throughput and lower latency but also demands higher power consumption for its denser base station (BS) deployment and more energy-intensive processors. LÄS MER