1 citations · 1 across the 4 of their papers we have counts for
4 papers
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…
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…
LightNorm: Area and Energy-Efficient Batch Normalization Hardware for On-Device DNN Training
Seock-Hwan Noh, Junsang Park, Dahoon Park +3
When training early-stage deep neural networks (DNNs), generating intermediate features via convolution or linear layers occupied most of the execution time. Accordingly, extensive…
ZeBRA: Precisely Destroying Neural Networks with Zero-Data Based Repeated Bit Flip Attack
Dahoon Park, Kon-Woo Kwon, Sunghoon Im +1
In this paper, we present Zero-data Based Repeated bit flip Attack (ZeBRA) that precisely destroys deep neural networks (DNNs) by synthesizing its own attack datasets. Many prior w…