8 citations · 15 across the 5 of their papers we have counts for
6 papers
GraLoRA: Granular Low-Rank Adaptation for Parameter-Efficient Fine-Tuning
Yeonjoon Jung, Daehyun Ahn, Hyungjun Kim +2
Low-Rank Adaptation (LoRA) is a popular method for parameter-efficient fine-tuning (PEFT) of generative models, valued for its simplicity and effectiveness. Despite recent enhancem…
Debunking the CUDA Myth Towards GPU-based AI Systems
Yunjae Lee, Juntaek Lim, Jehyeon Bang +10
This paper presents a comprehensive evaluation of Intel Gaudi NPUs as an alternative to NVIDIA GPUs, which is currently the de facto standard in AI system design. First, we create…
QUICK: Quantization-aware Interleaving and Conflict-free Kernel for efficient LLM inference
Taesu Kim, Jongho Lee, Daehyun Ahn +4
We introduce QUICK, a group of novel optimized CUDA kernels for the efficient inference of quantized Large Language Models (LLMs). QUICK addresses the shared memory bank-conflict p…
Squeezing Large-Scale Diffusion Models for Mobile
Jiwoong Choi, Minkyu Kim, Daehyun Ahn +6
The emergence of diffusion models has greatly broadened the scope of high-fidelity image synthesis, resulting in notable advancements in both practical implementation and academic…
Temporal Dynamic Quantization for Diffusion Models
Junhyuk So, Jungwon Lee, Daehyun Ahn +2
The diffusion model has gained popularity in vision applications due to its remarkable generative performance and versatility. However, high storage and computation demands, result…
OWQ: Outlier-Aware Weight Quantization for Efficient Fine-Tuning and Inference of Large Language Models
Changhun Lee, Jungyu Jin, Taesu Kim +2
Large language models (LLMs) with hundreds of billions of parameters require powerful server-grade GPUs for inference, limiting their practical deployment. To address this challeng…