20 citations · 50 across the 17 of their papers we have counts for
19 papers
BV Loss: Block Verification-Aware Loss for Block Diffusion Speculative Decoding
Suyoung Kim, Jahyun Koo, Hyeonjin Kim +5
Diffusion drafters accelerate speculative decoding by proposing multiple tokens in parallel. Despite recent advances in speculative decoding through sequence-level drafting and ver…
SlimWise: Decoupling Expert Pruning Across Prefill and Decode for Efficient MoE Serving
Gunho Park, Kyoungho Jeun, Juntaek Oh +3
Mixture-of-experts (MoE) models activate few experts per token, yet batched decoding can access nearly the entire expert pool, making expert-weight traffic a major bottleneck. Expe…
SUN: Shared Use of Next-token Prediction for Efficient Multi-LLM Disaggregated Serving
Sunghyeon Woo, Ahreum Seo, Jaegwang Lee +6
In multi-model LLM serving, decode execution remains inefficient due to model-specific resource partitioning: since cross-model batching is not possible, memory-bound decoding ofte…
ICaRus: Identical Cache Reuse for Efficient Multi Model Inference
Sunghyeon Woo, Jaeeun Kil, Hoseung Kim +9
Multi model inference has recently emerged as a prominent paradigm, particularly in the development of agentic AI systems. However, in such scenarios, each model must maintain its…
Affine-Scaled Attention: Towards Flexible and Stable Transformer Attention
Jeongin Bae, Baeseong Park, Gunho Park +7
Transformer attention is typically implemented using softmax normalization, which enforces attention weights with unit sum normalization. While effective in many settings, this con…
PrefillShare: A Shared Prefill Module for KV Reuse in Multi-LLM Disaggregated Serving
Sunghyeon Woo, Hoseung Kim, Sunghwan Shim +9
Multi-agent systems increasingly orchestrate multiple specialized language models to solve complex real-world problems, often invoking them over a shared context. This execution pa…
CodeGEMM: A Codebook-Centric Approach to Efficient GEMM in Quantized LLMs
Gunho Park, Jeongin Bae, Byeongwook Kim +5
Weight-only quantization is widely used to mitigate the memory-bound nature of LLM inference. Codebook-based methods extend this trend by achieving strong accuracy in the extremely…
FIGLUT: An Energy-Efficient Accelerator Design for FP-INT GEMM Using Look-Up Tables
Gunho Park, Hyeokjun Kwon, Jiwoo Kim +4
Weight-only quantization has emerged as a promising solution to the deployment challenges of large language models (LLMs). However, it necessitates FP-INT operations, which make im…
HyperCLOVA X Technical Report
Kang Min Yoo, Jaegeun Han, Sookyo In +393
We introduce HyperCLOVA X, a family of large language models (LLMs) tailored to the Korean language and culture, along with competitive capabilities in English, math, and coding. H…
DropBP: Accelerating Fine-Tuning of Large Language Models by Dropping Backward Propagation
Sunghyeon Woo, Baeseong Park, Byeongwook Kim +4
Large language models (LLMs) have achieved significant success across various domains. However, training these LLMs typically involves substantial memory and computational costs du…
AlphaTuning: Quantization-Aware Parameter-Efficient Adaptation of Large-Scale Pre-Trained Language Models
Se Jung Kwon, Jeonghoon Kim, Jeongin Bae +7
There are growing interests in adapting large-scale language models using parameter-efficient fine-tuning methods. However, accelerating the model itself and achieving better infer…
LUT-GEMM: Quantized Matrix Multiplication based on LUTs for Efficient Inference in Large-Scale Generative Language Models
Gunho Park, Baeseong Park, Minsub Kim +7
Recent advances in self-supervised learning and the Transformer architecture have significantly improved natural language processing (NLP), achieving remarkably low perplexity. How…
Modulating Regularization Frequency for Efficient Compression-Aware Model Training
Dongsoo Lee, Se Jung Kwon, Byeongwook Kim +3
While model compression is increasingly important because of large neural network size, compression-aware training is challenging as it needs sophisticated model modifications and…
Q-Rater: Non-Convex Optimization for Post-Training Uniform Quantization
Byeongwook Kim, Dongsoo Lee, Yeonju Ro +4
Various post-training uniform quantization methods have usually been studied based on convex optimization. As a result, most previous ones rely on the quantization error minimizati…
Encoding Weights of Irregular Sparsity for Fixed-to-Fixed Model Compression
Baeseong Park, Se Jung Kwon, Daehwan Oh +2
Even though fine-grained pruning techniques achieve a high compression ratio, conventional sparsity representations (such as CSR) associated with irregular sparsity degrade paralle…
FleXOR: Trainable Fractional Quantization
Dongsoo Lee, Se Jung Kwon, Byeongwook Kim +3
Quantization based on the binary codes is gaining attention because each quantized bit can be directly utilized for computations without dequantization using look-up tables. Previo…
Extremely Low Bit Transformer Quantization for On-Device Neural Machine Translation
Insoo Chung, Byeongwook Kim, Yoonjung Choi +5
The deployment of widely used Transformer architecture is challenging because of heavy computation load and memory overhead during inference, especially when the target device is l…
BiQGEMM: Matrix Multiplication with Lookup Table For Binary-Coding-based Quantized DNNs
Yongkweon Jeon, Baeseong Park, Se Jung Kwon +3
The number of parameters in deep neural networks (DNNs) is rapidly increasing to support complicated tasks and to improve model accuracy. Correspondingly, the amount of computation…
Structured Compression by Weight Encryption for Unstructured Pruning and Quantization
Se Jung Kwon, Dongsoo Lee, Byeongwook Kim +3
Model compression techniques, such as pruning and quantization, are becoming increasingly important to reduce the memory footprints and the amount of computations. Despite model si…