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Author

Daegun Yoon

Observed name: Yoon, Daegun
ORCID: 0000-0002-7520-1144
Observed ORCID: 0000-0002-7520-1144
OpenAlex ID: A5035408067
Mitigating Cold Start Problem in Serverless Computing with Function Fusion
2021 · Seungjun Lee, Daegun Yoon, et al. · Sensors · 59
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AMBLE: Adjusting mini-batch and local epoch for federated learning with heterogeneous devices
2022 · Juwon Park, Daegun Yoon, et al. · Journal of Parallel and Distributed Computing · 38
WAVE: designing a heuristics-based three-way breadth-first search on GPUs
2022 · Daegun Yoon, Minjoong Jeong, et al. · The Journal of Supercomputing · 8
Preserving Near-Optimal Gradient Sparsification Cost for Scalable Distributed Deep Learning
2024 · Daegun Yoon, Sangyoon Oh · IEEE/ACM International Symposium on Cluster, Cloud and Grid Computing (CCGrid) · 6
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DEFT: Exploiting Gradient Norm Difference between Model Layers for Scalable Gradient Sparsification
2023 · Daegun Yoon, Sangyoon Oh · Proceedings of the International Conference on Parallel Processing · 6
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HPU: High-Bandwidth Processing Unit for Scalable, Cost-effective LLM Inference via GPU Co-processing
2025 · Rhee, Myunghyun, Sim, Joonseop, et al. · arXiv (Cornell University) · 5
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Exploring a system architecture of content‐based publish/subscribe system for efficient on‐the‐fly data dissemination
2020 · Daegun Yoon, Gyudong Park, et al. · Concurrency and Computation Practice and Experience · 3
MiCRO: Near-Zero Cost Gradient Sparsification for Scaling and Accelerating Distributed DNN Training
2023 · Daegun Yoon, Sangyoon Oh · IEEE International Conference on High Performance Computing, Data, and Analytics · 2
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Can hierarchical client clustering mitigate the data heterogeneity effect in federated learning?
2023 · Seungjun Lee, Miri Yu, et al. · IEEE International Symposium on Parallel & Distributed Processing, Workshops and Phd Forum · 2
CPartition: a Correlation-Based Space Partitioning for Content-Based Publish/Subscribe Systems with Skewed Workload
2020 · Daegun Yoon, Gyudong Park, et al. · IEEE International Conference on Big Data and Smart Computing (BigComp) · 2
StreamDQ: HBM-Integrated On-the-Fly DeQuantization via Memory Load for Large Language Models
2025 · Minki Jeong, Daegun Yoon, et al. · IEEE Computer Architecture Letters · 1
SAGE: toward on-the-fly gradient compression ratio scaling
2023 · Daegun Yoon, Minjoong Jeong, et al. · The Journal of Supercomputing · 1
SURF: Direction-Optimizing Breadth-First Search Using Workload State on GPUs
2022 · Daegun Yoon, Sangyoon Oh · Sensors · 1
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Balanced content space partitioning for pub/sub: a study on impact of varying partitioning granularity
2021 · Daegun Yoon, Zhetao Li, et al. · The Journal of Supercomputing · 1
StreamDQ: Near-Memory Weight DeQuantization in Custom HBM for Scalable AI Inference Acceleration
2026 · Jeong, Minki, Yoon, Daegun, et al. · arXiv (Cornell University)
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CASCADE: A Memory-Centric Chiplet-Array Accelerator for Energy-Efficient LLM Decoding
2026 · Daegun Yoon, Soohong Ahn, et al. · IEEE Computer Architecture Letters
Performance-Index Based Rule Engine for Improving Routing Performance of a Tactical Network
2022 · Daegun Yoon, Byeong-hee Roh, et al. · 한국통신학회논문지
Request Definition Language for the User Module in the SDN Policy Engine
2022 · Seungjun Lee, Daegun Yoon, et al. · 한국통신학회논문지
Empirical Analysis on Top-k Gradient Sparsification for Distributed Deep Learning in a Supercomputing Environment
2022 · Yoon, Daegun, Oh, Sangyoon · arXiv (Cornell University)
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Works count: 19
Citation count: 135
H-index: 5
I10-index: 2
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