7 citations · 7 across the 11 of their papers we have counts for
12 papers
Hot-Cold Tiering of HBM and High Bandwidth Flash for Agentic LLM Serving
Jongjin Baek, Won Ji, Seungjae Yoo +1
Large language model (LLM) serving is increasingly agentic, with multi-turn sessions that idle between actions yet must retain their full context. Limited GPU memory capacity force…
Distance Is Not Enough: Forget-Retain Alignment Gap Predicts LLM Relearning Robustness
Yi Chen, Hanna Hsieh, Shuhong Liu +4
Machine unlearning aims to make a model forget specific data, yet unlearned LLMs often fail to stay unlearned: brief fine-tuning can revive removed knowledge. Existing robustness p…
APT: Accelerating Diffusion Transformers via Attention Probability-Guided Pruning and Quantization
Sungyeob Yoo, Seeyeon Kim, Joonyong Park +2
Recent advances in generative AI have significantly increased the demand for high-resolution image and video generation, positioning diffusion models as a core technology. Among th…
Beyond Capacity: Scalable MoE LLM Inference via High-Bandwidth Flash with Direct GPU and HBM Paths
Seeyeon Kim, Juhyeong Jin, Joo-Young Kim
Modern mixture-of-experts (MoE) language models increasingly strain the capacity and cost efficiency of high-bandwidth memory (HBM), as rapidly growing expert weights must be provi…
Cassandra: Enabling Reasoning LLMs at Edge via Self-Speculative Decoding
Soongyu Choi, Yuntae Kim, Muyoung Son +1
Speculative decoding has emerged as a promising lossless approach for accelerating Large Language Models (LLMs). As reasoning LLMs increasingly suffer from decode-stage overhead an…
DiSC: Resolution-Scalable Acceleration of Diffusion Models by Exploiting Sparsity and Cached Token Reuse with Hash-based Distribution
Jieon Yoon, Hangyeol Lee, Jaehoon Heo +1
Transformer-based diffusion models offer superior scalability and performance but suffer from high computational overhead due to the iterative nature and quadratic complexity of se…
MASQ: Accelerating Masked Diffusion via Stage-Wise Multi-Precision Quantization
Seeyeon Kim, Jaehun Lee, Sungyeob Yoo +1
Masked diffusion enables region-specific image synthesis but suffers from computational redundancy, since the entire image is processed each timestep even though only the masked re…
ORBIS: Output-Guided Token Reduction with Distribution-Aware Matching for Video Diffusion Acceleration
Hangyeol Lee, Joo-Young Kim
Diffusion Transformer (DiT) has emerged as a powerful model architecture for generating high-quality images and videos. In the case of video DiT, 3D Spatio-Temporal Attention incre…
Rethinking Token Reduction for Diffusion Models via Output-Similarity-Awareness
Hangyeol Lee, Hyojeong Lee, Joo-Young Kim
Diffusion Transformers (DiTs) achieve superior image generation quality but suffer from quadratic computational complexity relative to token count. While various token reduction (T…
CoX-MoE: Coalesced Expert Execution for High-Throughput MoE Inference with AMX-Enabled CPU-GPU Co-Execution
Muyoung Son, Yi Chen, Seungjae Yoo +2
The Mixture-of-Experts (MoE) architecture improves computational efficiency via sparse expert activation, but throughput-oriented inference faces substantial GPU memory pressure du…
AoP-SAM: Automation of Prompts for Efficient Segmentation
Yi Chen, Mu-Young Son, Chuanbo Hua +1
The Segment Anything Model (SAM) is a powerful foundation model for image segmentation, showing robust zero-shot generalization through prompt engineering. However, relying on manu…
ADOR: A Design Exploration Framework for LLM Serving with Enhanced Latency and Throughput
Junsoo Kim, Hunjong Lee, Geonwoo Ko +4
The growing adoption of Large Language Models (LLMs) across various domains has driven the demand for efficient and scalable AI-serving solutions. Deploying LLMs requires optimizat…