Sökning: "circuit optimization"

Visar resultat 1 - 5 av 66 uppsatser innehållade orden circuit optimization.

1. 1. Optical Communication using Nanowires and Molecular Memory Systems

   Master-uppsats, Lunds universitet/Fysiska institutionen; Lunds universitet/Synkrotronljusfysik

   Författare :Thomas Kjellberg Jensen; [2024]
   Nyckelord :neuromorphic computing; nanowire; molecular dye; DASA photoswitch; OBIC; Physics and Astronomy;

   Sammanfattning : Neuromorphic computational networks, inspired by biological neural networks, provide a possible way of lowering computational energy cost, while at the same time allowing for much more sophisticated devices capable of real-time inferences and learning. Since simulating artificial neural networks on conventional computers is particularly inefficient, the development of neuromorphic devices is strongly motivated as the reliance on AI-models increases. LÄS MER

3. 2. 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

4. 3. Quantum Reinforcement Learning for Sensor-Assisted Robot Navigation Tasks

   Master-uppsats, Lunds universitet/Fysiska institutionen

   Författare :Joyce Cobussen; [2023]
   Nyckelord :Physics and Astronomy;

   Sammanfattning : Quantum computing has advanced rapidly throughout the past decade, both from a hardware and software point of view. A variety of algorithms have been developed that are suitable for the current generation of quantum devices, which are referred to as noisy intermediate-scale quantum devices. LÄS MER

5. 4. Quantum Algorithms for Feature Selection and Compressed Feature Representation of Data

   Master-uppsats, KTH/Fysik

   Författare :William Laius Lundgren; [2023]
   Nyckelord :Feature selection; autoencoders; quantum machine learning; quantum circuits; quantum annealing; Funktionsval; datakompression; kvantmaskininlärning; kvantalgoritmer; kvantkretsar;

   Sammanfattning : Quantum computing has emerged as a new field that may have the potential to revolutionize the landscape of information processing and computational power, although physically constructing quantum hardware has proven difficult,and quantum computers in the current Noisy Intermediate Scale Quantum (NISQ) era are error prone and limited in the number of qubits they contain.A sub-field within quantum algorithms research which holds potential for the NISQ era, and which has seen increasing activity in recent years, is quantum machine learning, where researchers apply approaches from classical machine learning to quantum computing algorithms and explore the interplay between the two. LÄS MER

7. 5. Estimation of Voltage Drop in Power Circuits using Machine Learning Algorithms : Investigating potential applications of machine learning methods in power circuits design

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

   Författare :Dimitrios Koutlis; [2023]
   Nyckelord :Voltage drop estimation; Application-specific Integrated Circuits ASICs ; Machine learning algorithms; XGBoost; Convolutional Neural Networks; Graph Neural Networks; Power circuit optimization; Uppskattning av spänningsfall; applikationsspecifika integrerade kretsar ASIC ; maskininlärningsalgoritmer; XGBoost; konvolutionella neurala nätverk; optimering av strömkretsar;

   Sammanfattning : Accurate estimation of voltage drop (IR drop), in Application-Specific Integrated Circuits (ASICs) is a critical challenge, which impacts their performance and power consumption. As technology advances and die sizes shrink, predicting IR drop fast and accurate becomes increasingly challenging. LÄS MER