Graduation Semester and Year
Spring 2024
Language
English
Document Type
Thesis
Degree Name
Master of Science in Computer Science
Department
Computer Science and Engineering
First Advisor
Dr. Phuc VP Nguyen
Second Advisor
Dr. Jacob M Luber
Third Advisor
Dr. Yilun Xu
Abstract
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.
Keywords
Machine Learning, Embedded ML, Optimization
Disciplines
Hardware Systems | Other Computer Engineering | VLSI and Circuits, Embedded and Hardware Systems
License
This work is licensed under a Creative Commons Attribution 4.0 International License.
Recommended Citation
Vora, Neel R., "EXPLORING MACHINE LEARNING TECHNIQUES FOR EMBEDDED HARDWARE" (2024). Computer Science and Engineering Theses. 2.
https://mavmatrix.uta.edu/cse_theses/2
Included in
Hardware Systems Commons, Other Computer Engineering Commons, VLSI and Circuits, Embedded and Hardware Systems Commons