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전체 가 나 다 라 마 바 사 아 자 차 카 타 파 하

Showing results 1 to 18 of 18

Thesis
A Deep Learning-based Reference Search for Delta Compression
  • Jeonggyun Kim
  • 2021
  • Jeonggyun Kim. (2021). A Deep Learning-based Reference Search for Delta Compression. doi: 10.22677/thesis.200000497147
  • DGIST
  • View : 176
  • Download : 0
Thesis
A Diffusion-Based Framework for Configurable and Realistic Storage Trace Generation
  • Seohyun Kim
  • 2026
  • DGIST
  • View : 12
  • Download : 0
Thesis
A Lightweight CXL Memory Emulation Framework for Modern AI Workload Exploration
  • HOYEON LEE
  • 2025
  • DGIST
  • View : 19
  • Download : 0
Thesis
Adaptive Real-Time Scheduling for Open Systems via Reinforcement Learning
  • Jun Won Park
  • 2026
  • DGIST
  • View : 11
  • Download : 0

Analyzing Causes of Metadata Service Overheads in Ceph File System

Thesis
Analyzing Causes of Metadata Service Overheads in Ceph File System
  • Hojun Kim
  • 2021
  • Hojun Kim. (2021). Analyzing Causes of Metadata Service Overheads in Ceph File System. doi: 10.22677/thesis.200000497155
  • DGIST
  • View : 298
  • Download : 347
Thesis
Building Efficient Key-value Storage Systems with Emerging Storage Technologies
  • Jinhyung Koo
  • 2025
  • Jinhyung Koo. (2025). Building Efficient Key-value Storage Systems with Emerging Storage Technologies. doi: 10.22677/THESIS.200000837359
  • DGIST
  • View : 211
  • Download : 0
Thesis
Challenges and Opportunities of using KV SSDs for Ceph File System
  • Phil Moon
  • 2023
  • Phil Moon. (2023). Challenges and Opportunities of using KV SSDs for Ceph File System. doi: 10.22677/THESIS.200000688146
  • DGIST
  • View : 163
  • Download : 0
Thesis
diffusion-based generative system surrogates for scalable learning-driven optimization in virtual playgrounds
  • Junyoung Lee
  • 2026
  • DGIST
  • View : 9
  • Download : 0

Efficient Real-Time Object Detection with Adaptive Image Scaling and Cropping

Thesis
Efficient Real-Time Object Detection with Adaptive Image Scaling and Cropping
  • Siwoo Chung
  • 2021
  • Siwoo Chung. (2021). Efficient Real-Time Object Detection with Adaptive Image Scaling and Cropping. doi: 10.22677/thesis.200000497157
  • DGIST
  • View : 195
  • Download : 174
Thesis
Energy-Efficient Accelerator Design for Lightweight Deep Learning Workloads
  • Seock-Hwan Noh
  • 2025
  • DGIST
  • View : 9
  • Download : 0
Thesis
LoRA-RAG: Scalable Long-Context Augmentation for LLM Serving
  • Chanwoo Moon
  • 2026
  • DGIST
  • View : 22
  • Download : 0
Thesis
Mitigating Address Translation Overhead in Near- Data Processing on CXL Memory
  • Gwangin Kim
  • 2026
  • DGIST
  • View : 36
  • Download : 0
Thesis
MX format Quantization Optimization for Lightning-NeRF
  • Sungju Kim
  • 2025
  • Sungju Kim. (2025). MX format Quantization Optimization for Lightning-NeRF. doi: 10.22677/THESIS.200000828493
  • DGIST
  • View : 279
  • Download : 0
Thesis
Near Data Processing for Clustering Enhanced by Automatic Memory Disaggregation
  • Sanghoon Lee
  • 2023
  • Sanghoon Lee. (2023). Near Data Processing for Clustering Enhanced by Automatic Memory Disaggregation. doi: 10.22677/THESIS.200000656260
  • DGIST
  • View : 295
  • Download : 0
Thesis
On the Improvement of Hardware Utilization of Sparse GEMM Accelerators
  • Banseok Shin
  • 2023
  • Banseok Shin. (2023). On the Improvement of Hardware Utilization of Sparse GEMM Accelerators. doi: 10.22677/THESIS.200000657310
  • DGIST
  • View : 328
  • Download : 0
Thesis
Paste-and-Cut : Collective Image Localization and Classification for Real-Time Multi-Camera Object Detection
  • Young Eun Kang
  • 2023
  • Young Eun Kang. (2023). Paste-and-Cut : Collective Image Localization and Classification for Real-Time Multi-Camera Object Detection. doi: 10.22677/THESIS.200000684351
  • DGIST
  • View : 153
  • Download : 0
Thesis
Real-Time Scheduling Framework for Multi-DNN Inference
  • Woosung Kang
  • 2026
  • DGIST
  • View : 14
  • Download : 0
Thesis
Toward Fast and Scalable Transfer Learning with In-storage Processing
  • Gwonhyu Jin
  • 2021
  • Gwonhyu Jin. (2021). Toward Fast and Scalable Transfer Learning with In-storage Processing. doi: 10.22677/thesis.200000362196
  • DGIST
  • View : 188
  • Download : 0
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