4 citations · 4 across the 5 of their papers we have counts for
5 papers
HBQ: Hierarchical Scaling Block Quantization with Hardware-Efficiency-Aware Design for Accurate LLM Inference
Chun-Ting Chen, Dongmin Han, Hangyeol Mun +6
Block Quantization (BQ) enables efficient LLM inference by quantizing both weights and activations, but its design space remains underexplored. Through hardware-accuracy design spa…
RaZeR: Pushing the Limits of NVFP4 Quantization with Redundant Zero Remapping
Yuzong Chen, Xilai Dai, Jake Hyun +6
The recently introduced NVFP4 format demonstrates remarkable performance and memory benefits for quantized large language model (LLM) inference. However, we observe two types of re…
Log-Time K-Means Clustering for 1D Data: Novel Approaches with Proof and Implementation
Jake Hyun
Clustering is a key task in machine learning, with -means being widely used for its simplicity and effectiveness. While 1D clustering is common, existing methods often fail to e…
DecDEC: A Systems Approach to Advancing Low-Bit LLM Quantization
Yeonhong Park, Jake Hyun, Hojoon Kim +1
Quantization of Large Language Models (LLMs) has recently gained popularity, particularly for on-device settings with limited hardware resources. While efficient, quantization inev…
Any-Precision LLM: Low-Cost Deployment of Multiple, Different-Sized LLMs
Yeonhong Park, Jake Hyun, SangLyul Cho +2
Recently, considerable efforts have been directed towards compressing Large Language Models (LLMs), which showcase groundbreaking capabilities across diverse applications but entai…