Thesis / Dissertation

2024

  • [M.S. Thesis] Yonwoo Seong. Deep Learning Model Quantization and Heterogeneous Computing for Mobile ISP Optimization. M.S. Thesis, February 2024.
  • [M.S. Thesis] Minjung Ko. GPU Server Workload Prediction using Deep Learning for Cloud Gaming. M.S. Thesis, February 2024.
  • [M.S. Thesis (DS)] Sangsoo Im. Outlier-Aware Quantization for Language Models. M.S. Thesis, February 2024.
  • [M.S. Thesis (DS)] Dongyoung Lee. Efficient and Scalable Retraining of Autoregressive Language Models for Mitigating Bias. M.S. Thesis, February 2024.
  • [M.S. Thesis (DS)] Gwangho Choi. Document-Level Similarity Analysis on Pretraining Language Models. M.S. Thesis, February 2024.
  • [M.S. Thesis (DS)] Gyuseong Lee. Efficient Two-staged Pre-training of Decoder-based Language Models. M.S. Thesis, February 2024.
  • [M.S. Thesis (DS)] Woojin Kim. Deep Learning-based Text De-identification Model via Augmentation of De-identified Data. M.S. Thesis, February 2024

2023

  • [M.S. Thesis (DS)] Youngeun Choi. Prompt-Guided Dataset Generation to Mitigate Bias in a Language Model. M.S. Thesis, August 2023.
  • [M.S. Thesis] Hyungdal Kwon. Greedy-K: A New Local Optimization Algorithm for Solving 1D Circuit Placement Problems. M.S. Thesis, February 2023.
  • [M.S. Thesis (DS)] Jongwon Kim. Shaking Attention Scores in Pretrained Transformers. M.S. Thesis, February 2023.
  • [M.S. Thesis (DS)] Soram Lee. Analysis of Tokenizers for Various Korean NLP Tasks. M.S. Thesis, February 2023.
  • [M.S. Thesis (DS)] Dayeon Kang. Effects of duplicated data in language modeling. M.S. Thesis, February 2023.
  • [M.S. Thesis (DS)] Gyeongje Jo. Efficient Methods for Integer only Quantization. M.S. Thesis, February 2023.

2022

  • [Ph.D. Dissertation] Jaehoon Jung. Data Management and Prefetching Techniques for CUDA Unified Memory. Ph.D. Thesis, August 2022.
  • [M.S. Thesis] Minhee Han. Reducing the Cost of Training a Transformer Model by Using a Trained Model. M.S. Thesis, August 2022.
  • [M.S. Thesis] Sooyeon Kang. Memory Management and Optimization for Deep Learning Model Inference on Mobile GPU. M.S. Thesis, August 2022.
  • [M.S. Thesis (DS)] Minhye Park. Improving the Embedding Representation and Reducing Learning Time for Sequential Recommendation. M.S. Thesis, August 2022.
  • [M.S. Thesis] Jaeki Hong. Offloading Encryption Overhead for Distributed Filesystem using SmartNIC. M.S. Thesis, February 2022.
  • [M.S. Thesis (DS)] Youngjun Son. Contrastive Learning with Graph Neural Networks for Session-based Recommendation. M.S. Thesis, February 2022
  • [M.S. Thesis (DS)] Sunyoo Kim. A Transformer Model for Korean Morphological Analysis. M.S. Thesis, February 2022.

2021

  • [M.S. Thesis] Hyunjae Lee. Inference Method for Password Guessing Model with Tree Search. M.S. Thesis, August 2021.
  • [M.S. Thesis] Jeesoo Lee. Memory management technique for deep learning training with GPU. M.S. Thesis, February 2021.
  • [M.S. Thesis] Dongjin Na. Techniques to accelerate on-device inference of transformer models using Google Edge TPUs. M.S. Thesis, February 2021.

2020

  • [M.S. Thesis] Daeyoung Park. Translating OpenMP Device Constructs for NVIDIA GPUs. M.S. Thesis, August 2020.
  • [M.S. Thesis] Jungwook Kim. Optimizing ELF Binaries on NUMA Systems. M.S. Thesis, August 2020.
  • [Ph.D. Dissertation] Jungho Park. Optimizing GPU-accelerated Applications using Workload Scheduling and Memory Management. Ph.D. Thesis, February 2020.
  • [Ph.D. Dissertation] Gangwon Jo. High Level Synthesis of OpenCL Kernels for FPGAs. Ph.D. Thesis, February 2020.
  • [M.S. Thesis] Hyungmo Kim. Library-level Distributed Processing Method for CNN Training using GPU Clusters. M.S. Thesis, February 2020.
  • [M.S. Thesis] Janghyun Son. A Semantic Segmentation Network and Synthetic Data Generation for Defects Detection. M.S. Thesis, February 2020.
  • [M.S. Thesis] PyeongSeok Oh. Collective Learning Techniques using Ensembles of Generators and Discriminators for GANs. M.S. Thesis, August 2019.
  • [M.S. Thesis] Youngdong Do. Performance Characterization of High-Performance Computing Applications. M.S. Thesis, February 2019.

2018

  • [M.S. Thesis] Jiyoung Park. FPGA Accelerator Design of a Deep Reinforcement Learning Model for Playing Atari Games. M.S. Thesis, February 2018.

2017

  • [M.S. Thesis] Jaeho Shin. Auto-Optimization of Image Processing Program using OpenCL Through Dynamic Work-load Distribution. M.S. Thesis, February 2017

2016

  • [M.S. Thesis] Bojun Seo. Reducing memory usage by sharing code on V8 JavaScript Engine. M.S. Thesis, August 2016.
  • [Ph.D. Dissertation] Junghyun Kim. Techniques for Ease of OpenCL Programming. Ph.D. Thesis, February 2016.

2014

  • [M.S. Thesis] Jinyoung Joo. Bi-directional Source-to-source Translator Between CUDA and OpenCL. M.S. Thesis, February 2014.
  • [M.S. Thesis] Seonmyeong Bak. Lightweight Block-level Concurrent Sweeping for JavaScript Garbage Collection. M.S. Thesis, February 2014.

2013

  • [Ph.D. Dissertation] Jungwon Kim. An OpenCL Framework for Heterogeneous Clusters. Ph.D. Thesis, August 2013.
  • [Ph.D. Dissertation] Sangmin Seo. Enhancing Performance Portability of OpenCL for Multicore CPUs. Ph.D. Thesis, August 2013.

2012

  • [M.S. Thesis] Jeongho Nah. Implementation of a Reigster Allocator for a Javascript JIT Compiler. M.S. Thesis, February 2012.

2011

  • [Ph.D. Dissertation] Choonki Jang. Optimization and Management Techniques for Local Memeory Architectures. Ph.D. Thesis, August 2011.
  • [M.S. Thesis] Hongjune Kim. JavaScript Compilation and Optimization Techniques for Multicores. M.S. Thesis, August 2011.
  • [M.S. Thesis] Joo Hwan Lee. Reducing JavaScript Compilation Time by Caching Code in Flash Memory. M.S. Thesis, August 2011.
  • [M.S. Thesis] Thanh Tuan Dao. A Machine Learning Approach to Select Appropriate Target Computer Devices in Heterogeneous Parallel Computing. M.S. Thesis, August 2011.
  • [M.S. Thesis] Eunbyung Park. Fast and Space-Efficient Virtual Machine Checkpointing. M.S. Thesis, February 2011.
  • [M.S. Thesis] Yong-Jun Lee. A Stub Inlining Technique for JavaScript Engines in Multi-core Embedded Platforms. M.S. Thesis, February 2011.

2010

  • [M.S. Thesis] Jinho Pak. The Design and Implementation of UI for Architecture Simulator. M.S. Thesis, February 2010.
  • [M.S. Thesis] Honggyu Kim. Adaptive Execution Techniques of Parallel Programs for SMT Multicore Processors. M.S. Thesis, February 2010.

    2008

    • [Ph.D. Dissertation] Bernhard Egger. Dynamic Scratchpad Memory Management. Ph.D. Thesis, February 2008.
    • [M.S. Thesis] Taejun Ha.  An Automatic Memory Subsystem Parameter Detection Program. M.S. Thesis, February 2008.
    • [M.S. Thesis] Chihun Kim. A Dynamic Code Placement Technique for Scratchpad Memory Using Postpass Optimization. M.S. Thesis, February 2008.
    • [M.S. Thesis] Kwangsub Kim. Optimization Techniques for Cycle-Accurate Instruction Set Simulator. M.S. Thesis, February 2008.

    2007

    • [M.S. Thesis] Yoonsung Nam. Cycle-Accurate and Fast Simulation Techniques for ARM Processors. M.S. Thesis, February 2007.

    2006

    • [M.S. Thesis] Jongyoung Lee. Reducing Execution Time of Memory Test Programs using SIMD Instructions and Caches in 64-bit Computing Environments. M.S. Thesis, August 2006.

    2005

    • [M.S. Thesis] Seokho Choi. An Intermediate Representation for Preserving Source Level Information and Optimization. M.S. Thesis, August 2005.
    • [M.S. Thesis] Kiwon Kwon. SNACK-pop: A Postpass Optimizer for Embedded Systems. M.S. Thesis, February 2005.
    • [M.S. Thesis] Changhee Jung. Helper Thread Prefetching for a Loosely-Coupled Multiprocessor System. M.S. Thesis, February 2005.

    2002

    • [M.S. Thesis] Xing Fang. Inserting Fences to Guarantee Sequential Consistency. M.S. Thesis, August 2002.
    • [M.S. Thesis] H. D. K. Moonesinghe. M.S. Thesis, August 2002.

    2000

    • [Ph.D. Dissertation] Jaejin Lee. Compilation Techniques for Explicitly Parallel Programs. Ph.D. Thesis, October 1999.