2 papers
cs.AR2026
MX-SAFE: Versatile Inference- and Training-Proof Microscaling Format with On-the-Fly Exponent and Mantissa Bit Allocation
Dahoon Park, Jahyun Koo, Sangwoo Hwang +1
As the demand for deep learning grows, cost reduction through quantization has become essential for both training and inference. In 2022, the Open Compute Project (OCP) consortium…
cs.LG2024
OPAL: Outlier-Preserved Microscaling Quantization Accelerator for Generative Large Language Models
Jahyun Koo, Dahoon Park, Sangwoo Jung +1
To overcome the burden on the memory size and bandwidth due to ever-increasing size of large language models (LLMs), aggressive weight quantization has been recently studied, while…