9 citations · 19 across the 8 of their papers we have counts for
4 papers · 1 filter
LightRot: A Light-Weighted Rotation Scheme and Architecture for Accurate Low-Bit Large Language Model Inference
Sangjin Kim, Yuseon Choi, Jungjun Oh +2
As large language models (LLMs) continue to demonstrate exceptional capabilities across various domains, the challenge of achieving energy-efficient and accurate inference becomes…
GyRot: Leveraging Hidden Synergy between Rotation and Fine-grained Group Quantization for Low-bit LLM Inference
Sangjin Kim, Yuseon Choi, Byeongcheol Kim +2
Low-bit quantization is essential for efficient LLM inference, and both rotation and fine-grained group quantization have shown individual promise. However, their combination often…
SliceMoE: Bit-Sliced Expert Caching under Miss-Rate Constraints for Efficient MoE Inference
Yuseon Choi, Sangjin Kim, Jungjun Oh +3
MoE models offer efficient scaling through conditional computation, but their large parameter size and expensive expert offloading make on-device deployment challenging. Existing a…
Two-Step Spike Encoding Scheme and Architecture for Highly Sparse Spiking-Neural-Network
Sangyeob Kim, Sangjin Kim, Soyeon Um +2
This paper proposes a two-step spike encoding scheme, which consists of the source encoding and the process encoding for a high energy-efficient spiking-neural-network (SNN) accele…