15 citations · 19 across the 12 of their papers we have counts for
5 papers · 1 filter
QTALE: Quantization-Robust Token-Adaptive Layer Execution for LLMs
Kanghyun Noh, Jinheon Choi, Yulhwa Kim
Large language models (LLMs) demand substantial computational and memory resources, posing challenges for efficient deployment. Two complementary approaches have emerged to address…
FastKV: Decoupling of Context Reduction and KV Cache Compression for Prefill-Decoding Acceleration
Dongwon Jo, Jiwon Song, Yulhwa Kim +1
While large language models (LLMs) excel at handling long-context sequences, they require substantial prefill computation and key-value (KV) cache, which can heavily burden computa…
Mixture of Scales: Memory-Efficient Token-Adaptive Binarization for Large Language Models
Dongwon Jo, Taesu Kim, Yulhwa Kim +1
Binarization, which converts weight parameters to binary values, has emerged as an effective strategy to reduce the size of large language models (LLMs). However, typical binarizat…
L4Q: Parameter Efficient Quantization-Aware Fine-Tuning on Large Language Models
Hyesung Jeon, Yulhwa Kim, Jae-joon Kim
Due to the high memory and computational costs associated with large language models (LLMs), model compression techniques such as quantization, which reduces inference costs, and 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…