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.
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Showing 1 to 20 of 21 for “"Edge-AI"”.
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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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Dora: QoE-aware hybrid parallelism for distributed edge AI
With the proliferation of edge AI applications, satisfying user quality of experience (QoE) requirements, such as model inference latency, has become a first-class objective, as these models operate in resource-constrained settings and directly interact with users. Yet, modern AI models routinely …
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Lightweight edge AI vision models for IoT-based insect monitoring
… With recent advances in Artificial Intelligence (AI) and Information and Communications Technology (ICT), there is an opportunity to automate the insect monitoring process effectively and directly in the field. However, key challenges persist, such as limited energy availability, unreliable …
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Resource efficient distributed inference of deep neural networks for Edge AI
… However, executing these models efficiently on edge devices remains a major challenge due to their high computational, energy, and bandwidth demands. This dissertation presents a unified framework for resource-efficient distributed inference of DNNs in Edge AI, built upon three tightly coupled …
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Resource-Efficient Collaborative Training and Inference of Foundation Models in Edge-AI
The convergence of Edge Artificial Intelligence (Edge-AI) and foundation models marks a transformative paradigm shift in the design of intelligent systems. Edge-AI enables computation to be performed closer to data sources and across distributed network edges, offering significant benefits in …
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EASE-E: Edge-AI based System for Energy-Efficiency in Autonomous Driving (ADAS/AD)
… architecture, powered by System-on-Module (SoM) Edge-AI boards. By facilitating efficient deep learning processing locally, the proposed EASE-E (Edge-AI based System for Energy Efficiency) solution achieves up to a 5x reduction in power consumption while maintaining high processing performance. …
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AI-Driven Fake News Detection: Trends, Techniques, and Experimental Analysis
… introducing a taxonomy that categorizes AI-driven detection methods into model-centric and process-centric approaches. We evaluate various approaches ranging from traditional machine learning to trending AI methodologies, focusing on techniques like data augmentation, information …
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Reliable and Trustworthy AI for Evidence-based Clinical Decision Support in Cancer Care
The integration of cutting-edge AI methods with real-world clinical data has moved from being a novelty to a necessity in oncology. However, the deployment of AI faces challenges, including the complexity of reliably modeling longitudinal Electronic Health Records (EHR) characterized by missing …
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Efficient Deep Learning Systems for Visual Perception on the Edge
Deep learning for visual perception on edge devices has become increasingly critical, driven by emerging applications in autonomous driving and AR/VR. Typically, sparse convolution on 3D point clouds and Visual Language Models (VLMs) for image processing are two important methods for visual …
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Privacy-Focused LLM for local data processing: Implementing OLLAMA and RAG to securely query personal files in closed environments
… privacy and security associated with cloud-based AI systems by developing a locally hosted, privacy-preserving AI framework. The solution is designed to provide advanced AI functionalities, ensuring organizations retain full control over their sensitive data while maintaining operational …
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Enabling Energy-Efficient Hybrid CMOS and Embedded Memory Accelerators for Neuromorphic Computing at the Edge
The deployment of deep learning at the edge promises advances in autonomous driving, computer vision, and IoT, but is limited by the inefficiencies of conventional von Neumann architectures. The physical separation of memory and processing creates a performance bottleneck, with high energy and …
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Urban Data Memory: Using Generative AI to Structure and Visualize Zoning Data for Urban Planning Evaluation
… This research examines whether generative AI can help bridge this gap and under what conditions - highlighting both challenges and opportunities - by introducing a system that responsively transforms qualitative zoning data into structured, queryable formats to support the quantitative …
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Acies-OS: a twin-assisted systems architecture for edge intelligence
… rapid proliferation of Artificial Intelligence (AI) within the Internet of Things (IoT) and Cyber-Physical Systems (CPS) has created new opportunities for intelligent sensing, perception, and control at the network edge. However, deploying deep learning-based intelligence on embedded platforms …
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AI Education for the AI Generation: A Study of Computer Science Student Attitudes Towards AI Ethics and Implications for CS Curricula
The proliferation of artificial intelligence (AI) technologies in recent years is evident across a variety of domains and settings, from AI voice assistants like Siri to an AI overview on Google searches to Waymo self-driving cars. Computer science (CS) students studying AI therefore find …
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NN2FPGA: Optimizing CNN Inference on FPGAs with Binary Integer Programming
L'abstract è presente nell'allegato / the abstract is in the attachment
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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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Machine Learning Algorithms for Robotic Navigation and Perception and Embedded Implementation Techniques
L'abstract è presente nell'allegato / the abstract is in the attachment
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Hardware-Aware Cross-Layer Optimizations of Deep Neural Networks for Embedded Systems
L'abstract è presente nell'allegato / the abstract is in the attachment
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Efficient and Robust Deep Learning for Robotics
L'abstract è presente nell'allegato / the abstract is in the attachment
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From Passive Data Collection to Sensor-Level Intelligence
… of connected devices operating across domains such as smart cities, agriculture, and environmental monitoring. Many of these devices are deployed in resource-constrained environments where stable power, high bandwidth, and continuous connectivity cannot be guaranteed. Traditional …
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