Jiwon Song
Ph.D. Student @ SNU VLSI Lab
Electrical and Computer Engineering, Seoul National University
Building efficient AI systems across models, hardware, and deployment.
Research Focus
Efficient AI systems across model optimization, hardware-aware acceleration, and practical deployment.
Quantization, pruning, sparsity, and token-efficient computation for scalable AI.
Hardware-aware methods that turn algorithmic efficiency into practical speedups.
Serving and deployment paths that reduce inference cost in real workloads.
News
Recent updates.
Token Sparse Attention is accepted to ICML 2026.
Publications
Research on efficient inference, reasoning acceleration, and practical system optimization for large language models.
LiteStage: Latency-aware Layer Skipping for Multi-stage Reasoning
arXiv preprint arXiv:2510.14211
Education
Ph.D. Student (Integrated M.S./Ph.D. Program)
Electrical and Computer Engineering, Seoul National University
Advisor: Jae-Joon Kim
Bachelor of Science
Electrical and Computer Engineering, Seoul National University
Summa Cum Laude 3.9/4.3
Experiences
Research Intern
2022 Summer- DNN quantization
Technical Writer
2024- Blog posts comparing vLLM and TensorRT-LLM
- vLLM vs TensorRT-LLM #6: Weight-Only Quantization
- vLLM vs TensorRT-LLM #8: KV Cache Quantization
Undergraduate Researcher
2021-2022, 2023-2024Seoul National University
- LLM quantization and pruning
- Post-training quantization of vision transformers
Teaching Assistant
2024 SpringDept. of Electrical and Computer Engineering, Seoul National University
- 430.207B 기초전자회로 및 실험 (Introduction to Electronic Circuits)
Military Service
2020-2021Republic of Korea Army
- Sergeant