ORCID Identifier(s)


Graduation Semester and Year

Spring 2024



Document Type


Degree Name

Master of Science in Computer Science


Computer Science and Engineering

First Advisor

Dr. Phuc VP Nguyen

Second Advisor

Dr. Jacob M Luber

Third Advisor

Dr. Yilun Xu


This thesis delves into the intricate symbiosis between machine learning (ML) methodologies and embedded hardware systems, with a primary focus on augmenting efficiency and real-time processing capabilities across diverse application domains. It confronts the formidable challenge of deploying sophisticated ML algorithms on resource-constrained embedded hardware, aiming not only to optimize performance but also to minimize energy consumption. Innovative strategies are explored to tailor ML models for streamlined execution on embedded platforms, with validation conducted across various real-world application domains. Notable contributions include the development of a deep-learning framework leveraging a variational autoencoder (VAE) for compressing physiological signals from wearables while preserving critical diagnostic information. Moreover, transfer learning and cross-modal learning techniques are investigated, specifically customized for embedded systems in drone-tracking applications, achieving unprecedented accuracy and real-time responsiveness. Furthermore, a field-programmable gate array (FPGA)-based ML solution is proposed, particularly for high-speed control systems such as quantum qubit readout, showcasing the potential of amalgamating ML techniques with embedded hardware systems at the hardware level.


Machine Learning, Embedded ML, Optimization


Hardware Systems | Other Computer Engineering | VLSI and Circuits, Embedded and Hardware Systems


Creative Commons Attribution 4.0 International License
This work is licensed under a Creative Commons Attribution 4.0 International License.



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