Jungjun Oh

According to our database, Jungjun Oh authored at least 11 papers between 2025 and 2026.

Collaborative distances:

• Dijkstra number of five.
• Erdős number of five.

2026

ELMoE-3D: Leveraging Intrinsic Elasticity of MoE for Hybrid-Bonding-Enabled Self-Speculative Decoding in On-Premises Serving.

Yuseon Choi, Jingu Lee, Jungjun Oh, Sunjoo Whang, Byeongcheol Kim, Minsung Kim, Hoi-Jun Yoo, Sangjin Kim

CoRR, April, 2026

An Energy-Efficient High Resolution Vision Transformer Processor Exploiting Token Similarity Beyond Token Merging.

Jungjun Oh, Sangjin Kim, Jiwon Choi, Junha Ryu, Byeongcheol Kim, Yuseon Choi, Hoi-Jun Yoo

IEEE Trans. Very Large Scale Integr. Syst., January, 2026

EdgeDiff: Energy-Efficient Multi-Modal Few-Step Diffusion Model Accelerator Using Mixed-Precision and Reordered Group Quantization.

Sangjin Kim, Jungjun Oh, Jeonggyu So, Yuseon Choi, Sangyeob Kim, Dongseok Im, Gwangtae Park, Hoi-Jun Yoo

IEEE J. Solid State Circuits, January, 2026

31.2 Revolver: Low-Bit GenAI Accelerator for Distilled-Model and CoT with Phase-Aware-Quantization and Rotation-Based Integer-Scaled Group Quantization.

Sangjin Kim, Jungjun Oh, Byeongcheol Kim, Yuseon Choi, Gwangtae Park, Hoi-Jun Yoo

Proceedings of the IEEE International Solid-State Circuits Conference, 2026

GyRot: Leveraging Hidden Synergy Between Rotation and Fine-Grained Group Quantization for Low-Bit LLM Inference.

Sangjin Kim, Yuseon Chou, Byeongcheol Kim, Jungjun Oh, Hoi-Jun Yoo

Proceedings of the IEEE International Symposium on High Performance Computer Architecture, 2026

2025

SliceMoE: Bit-Sliced Expert Caching under Miss-Rate Constraints for Efficient MoE Inference.

Yuseon Choi, Sangjin Kim, Jungjun Oh, Gwangtae Park, Byeongcheol Kim, Hoi-Jun Yoo

CoRR, December, 2025

SeVeDo: A Heterogeneous Transformer Accelerator for Low-Bit Inference via Hierarchical Group Quantization and SVD-Guided Mixed Precision.

Yuseon Choi, Sangjin Kim, Jungjun Oh, Byeongcheol Kim, Hoi-Jun Yoo

CoRR, December, 2025

LightRot: A Light-Weighted Rotation Scheme and Architecture for Accurate Low-Bit Large Language Model Inference.

Sangjin Kim, Yuseon Choi, Jungjun Oh, Byeongcheol Kim, Hoi-Jun Yoo

IEEE J. Emerg. Sel. Topics Circuits Syst., June, 2025

23.3 EdgeDiff: 418.4mJ/Inference Multi-Modal Few-Step Diffusion Model Accelerator with Mixed-Precision and Reordered Group Quantization.

Sangjin Kim, Jungjun Oh, Jeonggyu So, Yuseon Choi, Sangyeob Kim, Dongseok Im, Gwangtae Park, Hoi-Jun Yoo

Proceedings of the IEEE International Solid-State Circuits Conference, 2025

A 9.6 TOPS/W Vision Transformer Processor with Hierarchical Token Merging for Similarity-Driven Difference Computing.

Jungjun Oh, Sangjin Kim, Jiwon Choi, Byeongcheol Kim, Hoi-Jun Yoo

Proceedings of the IEEE International Symposium on Circuits and Systems, 2025

EdgeDiff: Multi-modal Few-step Diffusion Model Accelerator with Mixed-Precision and Reordered Group-Quantization for On-device Generative AI Motivation.

Sangjin Kim, Jungjun Oh, Jeonggyu So, Yuseon Choi, Sangyeob Kim, Dongseo Kim, Gwangtae Park, Hoi-Jun Yoo

Proceedings of the IEEE Hot Chips 37 Symposium, 2025