Thesis/Project Final Defense Schedule
Join us as the School of STEM master’s degree candidates present their culminating thesis and project work. The schedule is updated throughout the quarter, check back for new defenses.
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Master of Science in Computer Science & Software Engineering
AUTUMN 2024
Monday, October 14
YANG YU
Chair: Dr. Yang Peng
Candidate: Master of Science in Computer Science & Software Engineering
9:00 A.M.; Join Yang Yu’s Online Defense
Project: Design and Development of a Scalable Communication Plane for Efficient TinyML Model Deployment on IoT Devices
This project focuses on designing and developing an efficient model deployment service for TinyML on IoT devices. The system addresses the critical challenges of scalability, end-to-end communication, and model deployment in resource-constrained environments. The proposed solution provides flexibility for various IoT use cases by incorporating both device-as-client and device-as-server operation modes. The device-as-client mode allows edge devices to check with a central server for new models periodically. In contrast, the device-as-server mode enables real-time model deployment via a lightweight HTTP server running on the edge devices.
A key feature of the system is the Over-The-Air (OTA) download update mechanism, which allows remote models to be updated, thus reducing the need for physical device access. The combination of HTTP server processing of requests and the integration of heartbeat monitoring and Redis-based data management enhances the system’s robustness. It ensures efficient communication between devices and servers. Performance evaluations have shown that the system can handle large-scale deployments while maintaining low-latency, energy-efficient operation. Future work may include further optimizations for scalability, model versioning, and enhanced security protocols to better support more extensive and complex IoT systems.
Friday, October 25
SHENG WANG
Chair: Dr. Yang Peng
Candidate: Master of Science in Computer Science & Software Engineering
1:00 P.M.; Join Sheng Wang’s Online Defense
Project: TinyML Deployment Optimizer: A Management System Solution for IoT and Embedded Ecosystems
With the rapid advancement of machine learning and the widespread adoption of Internet of Things (IoT) devices, deploying artificial intelligence on resource-constrained devices has become an inevitable topic. This paper addresses the critical challenge of efficiently deploying and managing machine learning models on small, resource-constrained Internet of Things and embedded devices, known as Tiny Machine Learning. It presents a novel, comprehensive management system streamlining the deployment process, ensuring compatibility between models and devices in resource-limited environments. It introduces a comprehensive platform that integrates model conversion, compatibility checking, and deployment tracking, brings tools for converting standard machine learning models to formats suitable for embedded system devices, and prepares device firmware for over-the-air updates. The platform’s architecture, built on a microservices framework and utilizing in-memory data storage, ensures scalability and real-time responsiveness. The system demonstrates improvements in deployment efficiency and management capabilities throughout the rigid simulations. It shows significant success in model-device compatibility matching, considerably reducing model deployment time compared to manual methods. The results demonstrate the robustness of the methodology in tackling critical challenges related to the deployment and management of TinyML systems. This work contributes to TinyML by providing a scalable, user-friendly solution that addresses the complexities of managing machine learning deployments on resource-constrained devices, paving the way for widespread adoption of TinyML and enabling applications ranging from smart home devices to advanced industrial sensors.
Wednesday, November 27
JACOB CHESNUT
Chair: Dr. Kelvin Sung
Candidate: Master of Science in Computer Science & Software Engineering
3:30 P.M.; Join Jacob Chesnut’s Online Defense
Project: Achieving Real Time Ray Traced Rendering for a Virtual Reality Headset Using Foveated Rendering
Virtual reality (VR) is a system which supports immersive observation and interaction of 3D virtual scenes. Ray tracing is a rendering method which is capable of producing high-quality images of 3D virtual scenes. Ray tracing in VR allows realistic lighting effects to be combined with immersive 3D. However, rendering to VR hardware using a ray tracing renderer is an expensive task which can be especially challenging to meet the performance requirements for real-time rendering. This is because of the high-resolution screens of most VR headsets and the high cost per-pixel computations of ray tracing.
Foveated rendering, using the user focusing position to inform the distribution of rendering resources across the screen, is a technique which can reduce the cost of rendering and allow the use of both VR and ray tracing. This method has its own challenges including the need for eye-tracking hardware, the need to balance quality drop-off and performance, and the need to adjust to each user. Modern VR systems have eye tracking capabilities, and as such are a good choice for supporting foveated rendering systems.
This project explores the use of adaptive resolution foveated rendering in order to provide real-time ray tracing for VR systems. To better address differences between users, my system supports the use of user calibration and postprocessing effects to overcome common quality issues resulting from foveated rendering. The final system is capable of real-time ray tracing of a moderately complex environment to a VR device. The final image is of high quality with virtually unnoticeable effects from foveated rendering. Future work includes improvements to handling of dynamic objects and fine tuning of runtime performance by balancing CPU and GPU processing.