default search action
Google
Google Scholar
Semantic Scholar
Internet Archive Scholar
CiteSeerX
ORCIDYongjun Park 0001
Person information
- affiliation: Yonsei University, Seoul, South Korea
- affiliation (former): Hanyang University, Seoul, South Korea
- affiliation (former): Hongik University, Seoul, South Korea
Other persons with the same name
- Yongjun Park 0002 — Seoul National University of Science and Technology, Seoul, South Korea
- Yongjun Park 0003 — Korea University of Technology and Education, Korea
- Yongjun Park 0004 — VisualCamp, Seoul, South Korea
SPARQL queries 
Refine list
refinements active!
zoomed in on ?? of ?? records
view refined list in
export refined list as
2020 – today
- 2024
[j12]Seokwon Kang
, Jongbin Kim, Gyeongyong Lee, Jeongmyung Lee, Jiwon Seo, Hyungsoo Jung, Yong Ho Song, Yongjun Park:
ISP Agent: A Generalized In-storage-processing Workload Offloading Framework by Providing Multiple Optimization Opportunities. ACM Trans. Archit. Code Optim. 21(1): 11:1-11:24 (2024)- [c44]Younghyun Lee, Hyejun Kim, Yongseung Yu, Myeongjin Cho, Jiwon Seo, Yongjun Park:
Discovering Efficient Fused Layer Configurations for Executing Multi-Workloads on Multi-Core NPUs. DATE 2024: 1-6 - [c43]Kiung Jung, Seok Namkoong, Hongjun Um, Hyejun Kim, Youngsok Kim, Yongjun Park:
Orchestrating Multiple Mixed Precision Models on a Shared Precision-Scalable NPU. LCTES 2024: 72-82 - 2023
- [j11]Deok-Jae Oh, Yaebin Moon, Do Kyu Ham, Tae Jun Ham, Yongjun Park, Jae W. Lee, Jung Ho Ahn, Eojin Lee:
MaPHeA: A Framework for Lightweight Memory Hierarchy-aware Profile-guided Heap Allocation. ACM Trans. Embed. Comput. Syst. 22(1): 2:1-2:28 (2023) - [c42]Donghyeon Kim, Taehoon Kim, Inyong Hwang, Taehyeong Park, Hanjun Kim, Youngsok Kim, Yongjun Park:
Virtual PIM: Resource-Aware Dynamic DPU Allocation and Workload Scheduling Framework for Multi-DPU PIM Architecture. PACT 2023: 112-123 - [c41]Myung-Hwan Jang, Yun-Yong Ko, Hyuck-Moo Gwon, Ikhyeon Jo, Yongjun Park, Sang-Wook Kim:
SAGE: A Storage-Based Approach for Scalable and Efficient Sparse Generalized Matrix-Matrix Multiplication. CIKM 2023: 923-933 - [c40]Seokho Lee, Younghyun Lee, Hyejun Kim, Taehoon Kim, Yongjun Park:
Block Group Scheduling: A General Precision-scalable NPU Scheduling Technique with Capacity-aware Memory Allocation. DATE 2023: 1-6 - [c39]Yongseung Yu, Donghyun Son, Younghyun Lee, Sunghyun Park, Giha Ryu, Myeongjin Cho, Jiwon Seo, Yongjun Park:
Tailoring CUTLASS GEMM using Supervised Learning. ICCD 2023: 465-474 - [c38]Taehyeong Park, Seokwon Kang, Myung-Hwan Jang, Sang-Wook Kim, Yongjun Park:
Orchestrating Large-Scale SpGEMMs using Dynamic Block Distribution and Data Transfer Minimization on Heterogeneous Systems. ICDE 2023: 2456-2459 - [c37]Beom Woo Kang, Junho Wohn, Seongju Lee, Sunghyun Park, Yung-Kyun Noh, Yongjun Park:
Synchronization-Aware NAS for an Efficient Collaborative Inference on Mobile Platforms. LCTES 2023: 13-25 - [i1]Myung-Hwan Jang, Yun-Yong Ko, Hyuck-Moo Gwon, Ikhyeon Jo, Yongjun Park, Sang-Wook Kim:
SAGE: A Storage-Based Approach for Scalable and Efficient Sparse Generalized Matrix-Matrix Multiplication. CoRR abs/2308.13626 (2023) - 2022
- [j10]Inho Lee, Yangki Lee, Hongjun Um, Seongmin Hong, Yongjun Park:
Dynamic Rate Neural Acceleration Using Multiprocessing Mode Support. IEEE Trans. Very Large Scale Integr. Syst. 30(10): 1461-1472 (2022) - [c36]Sunghyun Park, Salar Latifi, Yongjun Park, Armand Behroozi, Byungsoo Jeon, Scott A. Mahlke:
SRTuner: Effective Compiler Optimization Customization by Exposing Synergistic Relations. CGO 2022: 118-130 - [c35]Jiho Kim, Seokwon Kang, Yongjun Park, John Kim:
Networked SSD: Flash Memory Interconnection Network for High-Bandwidth SSD. MICRO 2022: 388-403 - 2021
- [c34]Kyunghwan Choi, Seongju Lee, Beom Woo Kang, Yongjun Park:
Legion: Tailoring Grouped Neural Execution Considering Heterogeneity on Multiple Edge Devices. ICCD 2021: 383-390 - [c33]Yun-Yong Ko, Jae-Seo Yu, Hong-Kyun Bae, Yongjun Park, Dongwon Lee, Sang-Wook Kim:
MASCOT: A Quantization Framework for Efficient Matrix Factorization in Recommender Systems. ICDM 2021: 290-299 - [c32]Deok-Jae Oh, Yaebin Moon, Eojin Lee, Tae Jun Ham, Yongjun Park, Jae W. Lee, Jung Ho Ahn:
MaPHeA: a lightweight memory hierarchy-aware profile-guided heap allocation framework. LCTES 2021: 24-36 - 2020
- [c31]Seokwon Kang, Kyunghwan Choi, Yongjun Park:
PreScaler: an efficient system-aware precision scaling framework on heterogeneous systems. CGO 2020: 280-292 - [c30]Jiho Kim, John Kim, Yongjun Park:
Navigator: Dynamic Multi-kernel Scheduling to Improve GPU Performance. DAC 2020: 1-6 - [c29]Hyungjun Oh, Yongseung Yu, Giha Ryu, Gunjoo Ahn, Yuri Jeong, Yongjun Park, Jiwon Seo:
Convergence-Aware Neural Network Training. DAC 2020: 1-6 - [c28]Jeongmyung Lee, Seokwon Kang, Yongseung Yu, Yong-Yeon Jo, Sang-Wook Kim, Yongjun Park:
Optimization of GPU-based Sparse Matrix Multiplication for Large Sparse Networks. ICDE 2020: 925-936 - [c27]Kyoungho Koo, Yongjun Park, Youjip Won:
LOCKED-Free Journaling: Improving the Coalescing Degree in EXT4 Journaling. NVMSA 2020: 1-6 - [c26]Dokeun Lee, Youjip Won, Yongjun Park, Seongjin Lee:
Two-tier garbage collection for persistent object. SAC 2020: 1246-1255
2010 – 2019
- 2019
- [j9]Junmo Park, Yongin Kwon, Yongjun Park, Dongsuk Jeon:
Microarchitecture-Aware Code Generation for Deep Learning on Single-ISA Heterogeneous Multi-Core Mobile Processors. IEEE Access 7: 52371-52378 (2019) - [j8]Jiho Kim, Jehee Cha, Jason Jong Kyu Park, Dongsuk Jeon, Yongjun Park:
Improving GPU Multitasking Efficiency Using Dynamic Resource Sharing. IEEE Comput. Archit. Lett. 18(1): 1-5 (2019) - [j7]Yunho Oh, Keunsoo Kim, Myung Kuk Yoon, Jong Hyun Park, Yongjun Park, Murali Annavaram, Won Woo Ro:
Adaptive Cooperation of Prefetching and Warp Scheduling on GPUs. IEEE Trans. Computers 68(4): 609-616 (2019) - [c25]Seokwon Kang, Yongseung Yu, Jiho Kim, Yongjun Park:
GATE: A Generalized Dataflow-level Approximation Tuning Engine For Data Parallel Architectures. DAC 2019: 24 - [c24]Yongseung Yu, Seokwon Kang, Yongjun Park:
A compiler-based approach for GPGPU performance calibration using TLP modulation (WIP paper). LCTES 2019: 193-197 - 2018
- [j6]Yunho Oh, Myung Kuk Yoon, Jong Hyun Park, Yongjun Park, Won Woo Ro:
WASP: Selective Data Prefetching with Monitoring Runtime Warp Progress on GPUs. IEEE Trans. Computers 67(9): 1366-1373 (2018) - [c23]Seongmin Hong, Inho Lee, Yongjun Park:
NN compactor: Minimizing memory and logic resources for small neural networks. DATE 2018: 581-584 - [c22]Jinsoo Yoo, Yongjun Park, Seongjin Lee, Youjip Won:
Automatic code conversion for non-volatile memory. SAC 2018: 1071-1076 - [c21]Yongseung Yu, Seokwon Kang, Yongjun Park:
Runtime Profiling of OpenCL Workloads Using LLVM-based Code Instrumentation. TENCON 2018: 1520-1524 - [c20]Jehee Cha, Jiho Kim, Yongjun Park:
Core-level DVFS for Spatial Multitasking GPUs. TENCON 2018: 1525-1528 - [c19]Inho Lee, Seongmin Hong, Giha Ryu, Yongjun Park:
Automated Neural Network Accelerator Generation Framework for Multiple Neural Network Applications. TENCON 2018: 2287-2290 - 2017
- [j5]Bongsuk Ko, Seunghun Han, Yongjun Park, Moongu Jeon, Byeongcheol Lee:
A Comparative Study of Programming Environments Exploiting Heterogeneous Systems. IEEE Access 5: 10081-10092 (2017) - [j4]Jiho Kim, Minsung Chu, Yongjun Park:
Efficient GPU multitasking with latency minimization and cache boosting. IEICE Electron. Express 14(7): 20161158 (2017) - [j3]Yuhwan Ro, Min Chul Sung, Yongjun Park, Jung Ho Ahn:
Selective DRAM cache bypassing for improving bandwidth on DRAM/NVM hybrid main memory systems. IEICE Electron. Express 14(11): 20170437 (2017) - [c18]Jason Jong Kyu Park, Yongjun Park, Scott A. Mahlke:
Dynamic Resource Management for Efficient Utilization of Multitasking GPUs. ASPLOS 2017: 527-540 - [c17]Seongmin Hong, Yongjun Park:
A FPGA-based neural accelerator for small IoT devices. ISOCC 2017: 294-295 - 2016
- [j2]Donghwan Jeong, Young H. Oh, Jae W. Lee, Yongjun Park:
An eDRAM-Based Approximate Register File for GPUs. IEEE Des. Test 33(1): 23-31 (2016) - [c16]Yunho Oh, Keunsoo Kim, Myung Kuk Yoon, Jong Hyun Park, Yongjun Park, Won Woo Ro, Murali Annavaram:
APRES: Improving Cache Efficiency by Exploiting Load Characteristics on GPUs. ISCA 2016: 191-203 - [c15]Jason Jong Kyu Park, Yongjun Park, Scott A. Mahlke:
A bypass first policy for energy-efficient last level caches. SAMOS 2016: 63-70 - 2015
- [j1]Janghaeng Lee, Mehrzad Samadi, Yongjun Park, Scott A. Mahlke:
SKMD: Single Kernel on Multiple Devices for Transparent CPU-GPU Collaboration. ACM Trans. Comput. Syst. 33(3): 9:1-9:27 (2015) - [c14]Jason Jong Kyu Park, Yongjun Park, Scott A. Mahlke:
Fine Grain Cache Partitioning Using Per-Instruction Working Blocks. PACT 2015: 305-316 - [c13]Jason Jong Kyu Park, Yongjun Park, Scott A. Mahlke:
Chimera: Collaborative Preemption for Multitasking on a Shared GPU. ASPLOS 2015: 593-606 - [c12]Soumyadeep Ghosh, Yongjun Park, Arun Raman:
Enabling Efficient Alias Speculation. LCTES 2015: 7:1-7:10 - [c11]Jason Jong Kyu Park, Yongjun Park, Scott A. Mahlke:
ELF: maximizing memory-level parallelism for GPUs with coordinated warp and fetch scheduling. SC 2015: 18:1-18:12 - 2013
- [c10]Janghaeng Lee, Mehrzad Samadi, Yongjun Park, Scott A. Mahlke:
Transparent CPU-GPU collaboration for data-parallel kernels on heterogeneous systems. PACT 2013: 245-255 - [c9]Jason Jong Kyu Park, Yongjun Park, Scott A. Mahlke:
Efficient execution of augmented reality applications on mobile programmable accelerators. FPT 2013: 176-183 - 2012
- [c8]Yongjun Park, Sangwon Seo, Hyunchul Park, Hyoun Kyu Cho, Scott A. Mahlke:
SIMD defragmenter: efficient ILP realization on data-parallel architectures. ASPLOS 2012: 363-374 - [c7]Sangwon Seo, Ronald G. Dreslinski, Mark Woh, Yongjun Park, Chaitali Chakrabarti, Scott A. Mahlke, David T. Blaauw, Trevor N. Mudge:
Process variation in near-threshold wide SIMD architectures. DAC 2012: 980-987 - [c6]Yongjun Park, Jason Jong Kyu Park, Scott A. Mahlke:
Efficient performance scaling of future CGRAs for mobile applications. FPT 2012: 335-342 - [c5]Yongjun Park, Jason Jong Kyu Park, Hyunchul Park, Scott A. Mahlke:
Libra: Tailoring SIMD Execution Using Heterogeneous Hardware and Dynamic Configurability. MICRO 2012: 84-95 - 2010
- [c4]Yongjun Park, Hyunchul Park, Scott A. Mahlke, Sukjin Kim:
Resource recycling: putting idle resources to work on a composable accelerator. CASES 2010: 21-30
2000 – 2009
- 2009
- [c3]Yongjun Park, Hyunchul Park, Scott A. Mahlke:
CGRA express: accelerating execution using dynamic operation fusion. CASES 2009: 271-280 - [c2]Hyunchul Park, Yongjun Park, Scott A. Mahlke:
Polymorphic pipeline array: a flexible multicore accelerator with virtualized execution for mobile multimedia applications. MICRO 2009: 370-380 - [c1]Hyunchul Park, Yongjun Park, Scott A. Mahlke:
A dataflow-centric approach to design low power control paths in CGRAs. SASP 2009: 15-20
Coauthor Index
manage site settings
To protect your privacy, all features that rely on external API calls from your browser are turned off by default. You need to opt-in for them to become active. All settings here will be stored as cookies with your web browser. For more information see our F.A.Q.
Unpaywalled article links
Add open access links from 
to the list of external document links (if available).
Privacy notice: By enabling the option above, your browser will contact the API of unpaywall.org to load hyperlinks to open access articles. Although we do not have any reason to believe that your call will be tracked, we do not have any control over how the remote server uses your data. So please proceed with care and consider checking the Unpaywall privacy policy.
Archived links via Wayback Machine
For web page which are no longer available, try to retrieve content from the 
of the Internet Archive (if available).
Privacy notice: By enabling the option above, your browser will contact the API of archive.org to check for archived content of web pages that are no longer available. Although we do not have any reason to believe that your call will be tracked, we do not have any control over how the remote server uses your data. So please proceed with care and consider checking the Internet Archive privacy policy.
Reference lists
Add a list of references from 
, 
, and 
to record detail pages.
load references from crossref.org and opencitations.net
Privacy notice: By enabling the option above, your browser will contact the APIs of crossref.org, opencitations.net, and semanticscholar.org to load article reference information. Although we do not have any reason to believe that your call will be tracked, we do not have any control over how the remote server uses your data. So please proceed with care and consider checking the Crossref privacy policy and the OpenCitations privacy policy, as well as the AI2 Privacy Policy covering Semantic Scholar.
Citation data
Add a list of citing articles from and to record detail pages.
load citations from opencitations.net
Privacy notice: By enabling the option above, your browser will contact the API of opencitations.net and semanticscholar.org to load citation information. Although we do not have any reason to believe that your call will be tracked, we do not have any control over how the remote server uses your data. So please proceed with care and consider checking the OpenCitations privacy policy as well as the AI2 Privacy Policy covering Semantic Scholar.
OpenAlex data
Load additional information about publications from 
.
Privacy notice: By enabling the option above, your browser will contact the API of openalex.org to load additional information. Although we do not have any reason to believe that your call will be tracked, we do not have any control over how the remote server uses your data. So please proceed with care and consider checking the information given by OpenAlex.
last updated on 2024-10-08 20:35 CEST by the dblp team
all metadata released as open data under CC0 1.0 license
see also: Terms of Use | Privacy Policy | Imprint
dblp was originally created in 1993 at:
since 2018, dblp has been operated and maintained by:
the dblp computer science bibliography is funded and supported by:
