Orchestrating Multi-Objective Neural Network Deployment in Heterogeneous Edge Systems
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| Publicado en: | ProQuest Dissertations and Theses (2025) |
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| Acceso en línea: | Citation/Abstract Full Text - PDF |
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| Resumen: | With the widespread adoption of Deep Neural Networks (DNNs) in modern embedded applications, there has been a substantial increase in computationally intensive and power-hungry workloads, necessitating more sophisticated approaches to efficiently deploy these models on resource-constrained devices. The inherent limitations of edge computing platforms-including restricted memory capacity, limited processing capabilities, and stringent power constraints-pose substantial challenges for the deployment of complex DNN workloads. While recent advances in model compression and hardware acceleration have partially addressed these challenges, there remains a critical need for comprehensive solutions that systematically optimize both the pre-deployment design and runtime management of DNNs on heterogeneous edge systems. This dissertation aims to address these challenges by presenting a comprehensive approach that systematically optimizes DNN deployment at the edge, focusing on both offline model design and online resource management. Specifically, this dissertation focuses on four key areas: (i) dimensionality reduction through latent imagination for efficient AI-powered computer vision, where our methodology achieves over 45% improvement in prediction accuracy; (ii) a composite reinforcement learning controller for joint DNN pruning and quantization, which achieves 39% average energy reduction with only 1.7% average accuracy loss; (iii) efficient multi-DNN management via DNN partitioning for heterogeneous embedded systems, with our approach resulting in x4.6 average throughput improvement for multi-DNN workloads; and (iv) multi-objective optimization methodologies for balancing competing performance metrics in multi-DNN deployment scenarios through three complementary frameworks that address throughput-fairness balancing via reinforcement learning, throughput-power efficiency co-optimization through heterogeneity-aware techniques, and priority-aware resource allocation, collectively yielding significant improvements in system performance and responsiveness. In summary, this dissertation provides a comprehensive set of methodologies that enable the efficient deployment of complex DNN workloads on edge devices, systematically addressing the challenges of pre-deployment optimization and runtime management. The demonstrated results highlight the effectiveness of our proposed solutions and their potential for practical applications across various edge computing environments. |
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| ISBN: | 9798293857821 |
| Fuente: | ProQuest Dissertations & Theses Global |