Hyun Jae Oh — accepted AI-conference papers

2025

WINS: Winograd Structured Pruning for Fast Winograd Convolution

Cheonjun Park, Hyun Jae Oh, Mincheol Park, Hyunchan Moon, Minsik Kim, Suhyun Kim +2

ICCV 2025poster

Recent GPUs leverage Winograd convolution and structured pruning to significantly accelerate inference. First, Winograd convolution is theoretically 2.25x faster than standard convolution. Second, structured pruning reduces inference time without additional overhead as the pruning ratio increases. H…

Cited by 0 Source ↗PDF ↗Scholar ↗

2023

Balanced Column-Wise Block Pruning for Maximizing GPU Parallelism

Cheonjun Park, Mincheol Park, Hyun Jae Oh, Minkyu Kim, Myung Kuk Yoon, Suhyun Kim +1

AAAI 2023technical

Pruning has been an effective solution to reduce the number of computations and the memory requirement in deep learning. The pruning unit plays an important role in exploiting the GPU resources efficiently. The filter is proposed as a simple pruning unit of structured pruning. However, since the fi…

Cited by 10 Source ↗PDF ↗code ↗Scholar ↗