Global ETD Search
Search theses and dissertations gathered from participating repositories worldwide. Every result links back to the library that holds it. No account is needed.
Results
Showing 1 to 20 of 92 for “"Edge Devices"”.
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Slimmable neural networks for edge devices
… to run neural networks within latency budget for edge devices remains unsolved. This thesis presents a new approach to train a single neural network executable at arbitrary widths for instant and adaptive accuracy-efficiency trade-offs at runtime. First a simple and general method is presented to …
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Communication-efficient personalization in federated learning for edge devices
… and a compact, shared adapter for knowledge transfer, augmented with selective pruning to balance local adaptation and global generalization for vision and language tasks. Then, we present a lightweight, convolution-based approach to time-series forecasting that pairs learnable …
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Neural Network Reduction for Efficient Execution on Edge Devices
… neural networks to be grown and trained within edge devices, Artificial Neurogenesis and Synaptic Input Consolidation.
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Flexible and lightweight toolbox for federated learning on edge devices
Submission original under an indefinite embargo labeled 'Open Access'. The submission was exported from vireo on 2023-04-12 without embargo terms
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Converting Autoencoder Based Energy-Efficient and Secure DNN Inference on Edge Devices
… and depth estimation. With the rise of embedded devices, there is a growing demand for lightweight and energy-efficient models. While DNNs outperform traditional machine learning methods, their deep architectures pose challenges regarding energy consumption, latency, and computational efficiency. …
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Methods and Applications for Low-power Deep Neural Networks on Edge Devices
L'abstract è presente nell'allegato / the abstract is in the attachment
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LPC: Lossless parameter compression for deploying large language model inference on edge devices
Submission original under an indefinite embargo labeled 'Open Access'. The submission was exported from vireo on 2025-03-28 without embargo terms
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Improving Security of Edge Devices by Offloading Computations to Remote, Trusted Execution Environments
… (ISA) heterogeneous systems by adopting an edge-computing approach. As the embedded devices market grows, such systems remain affected by a wide range of attacks and are particularly vulnerable to techniques that render the operating system or hypervisor untrusted. The usage of Trusted …
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Hardware/Neural Network Codesign for Energy-Efficient Inference on Edge Devices with Optimal Mapping and Compression
L'abstract è presente nell'allegato / the abstract is in the attachment
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Resource and data optimization for hardware implementation of deep neural networks targeting FPGA-based edge devices
… on an FPGA. Our motivation is to target embedded devices that operate as edge devices. Recently, as machine learning algorithms have become more practical, there have been much effort to implement them on devices that can be used in our daily lives. However, unlike server devices, edge devices are …
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GraphDHT: Scaling Graph Neural Networks' Distributed Training on Edge Devices on a Peer-to-Peer Distributed Hash Table Network
… a peer-to-peer network of heterogeneous edge devices interconnected through a Distributed Hash Table (DHT). As GNNs become increasingly vital in analyzing graph-structured data across various domains, they pose unique challenges in computational demands and privacy preservation, …
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Continuous Learning for Lightweight Machine Learning Inference at the Edge
With the proliferation of edge devices such as mobile phones, consumer robots, drones, wearables, and IoT devices, the generation of data at the edge of the internet network has been increasing exponentially. Machine Learning (ML) models, particularly Deep Neural Networks (DNNs), have the ability …
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Federated Learning for Resource Constrained Devices
As resource constrained edge devices become increasingly more powerful, they are able to provide a larger quantity of higher quality data. However, as these devices are decentralized, it becomes difficult to gain insights from multiple devices at the same time. Federated learning allows us to learn …
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Delocalized Photonic Deep Learning on the Internet's Edge
… to enable computing systems on lightweight edge devices that were previously infeasible by orders of magnitude. First, we consider a system where all metallic interconnects above the digital logic are replaced by optical fan-out. I propose a freely scalable digital optical neural network …
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MODEL ADAPTATION FOR EDGE AI
Deployment of Deep Neural Networks (DNNs) on edge devices presents significant challenges due to their computational demands. Existing model compression techniques often fall short by being oblivious to downstream user-specific tasks. This thesis addresses the challenge of adapting DNN models …
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Enable Intelligence on Billion Devices with Deep Learning
<p>With the proliferation of edge computing and Internet of Things (IoT), billions of edge devices (e.g., smartphone, AR/VR headset, autonomous car, etc) are deployed in our daily life and constantly generating the gigantic amount of data at the network edge. Bringing deep learning to such huge …
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NON-VOLATILE IN-MEMORY COMPUTING WITH SKYRMIONS AND PHASE CHANGE MEMORIES
… for enabling ultra-low power intelligent edge devices. Due to power/area versus throughput trade-offs (such as to increase the complexity of periphery circuitry to enable higher parallelism or result precision), the memory subsystem in NVIMCE needs to be carefully designed to avoid …
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Energy-Efficient Neural Network Hardware Design and Circuit Techniques to Enhance Hardware Security
… future, more AI workloads are expected to run on edge devices. To fulfill this goal, innovative design techniques are needed to improve energy-efficiency, form factor, and as well as the security of AI chips. In this dissertation, two topics are focused on to address these challenges: building …
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Energy and time efficient federated learning
… the past decade, the volume of data generated by edge devices has grown exponentially as the number of edge devices surges. Federated learning (FL) enables on-device training while preserving privacy, but edge devices typically operate under tight time and energy budgets, highlighting the need for …
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LightMARL : smart swarm coordination in urban spaces
… multi-agent coordination on resource-constrained edge devices while ensuring coordination quality and real-time performance. LightMARL tackles three primary challenges: computational efficiency, communication overhead, and scalability. To improve computation, the framework utilizes neural network …
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