1 citations · 1 across the 11 of their papers we have counts for
11 papers
TIDE: Efficient and Lossless MoE Diffusion LLM Inference with I/O-aware Expert Offload
Zhiben Chen, Youpeng Zhao, Yang Sui +2
Diffusion Large Language Models (dLLMs) have emerged as a competitive alternative to autoregressive (AR) models, offering better hardware utilization and bidirectional context thro…
GhostServe: A Lightweight Checkpointing System in the Shadow for Fault-Tolerant LLM Serving
Shakya Jayakody, Youpeng Zhao, Chinmay Dhanraj Nehate +1
The rise of million-token, agent-based applications has placed unprecedented demands on large language model (LLM) inference services. The long-running nature of these tasks increa…
Classifier Enhancement Using Extended Context and Domain Experts for Semantic Segmentation
Huadong Tang, Youpeng Zhao, Min Xu +2
Prevalent semantic segmentation methods generally adopt a vanilla classifier to categorize each pixel into specific classes. Although such a classifier learns global information fr…
A.I.R.: Enabling Adaptive, Iterative, and Reasoning-based Frame Selection For Video Question Answering
Yuanhao Zou, Shengji Jin, Andong Deng +3
Effectively applying Vision-Language Models (VLMs) to Video Question Answering (VideoQA) hinges on selecting a concise yet comprehensive set of frames, as processing entire videos…
Are We Scaling the Right Thing? A System Perspective on Test-Time Scaling
Youpeng Zhao, Jinpeng LV, Di Wu +2
Test-time scaling (TTS) has recently emerged as a promising direction to exploit the hidden reasoning capabilities of pre-trained large language models (LLMs). However, existing sc…
AIRES: Accelerating Out-of-Core GCNs via Algorithm-System Co-Design
Shakya Jayakody, Youpeng Zhao, Jun Wang
Graph convolutional networks (GCNs) are fundamental in various scientific applications, ranging from biomedical protein-protein interactions (PPI) to large-scale recommendation sys…
Understanding and Tackling Label Errors in Individual-Level Nature Language Understanding
Yunpeng Xiao, Youpeng Zhao, Kai Shu
Natural language understanding (NLU) is a task that enables machines to understand human language. Some tasks, such as stance detection and sentiment analysis, are closely related…
LMSeg: Unleashing the Power of Large-Scale Models for Open-Vocabulary Semantic Segmentation
Huadong Tang, Youpeng Zhao, Yan Huang +3
It is widely agreed that open-vocabulary-based approaches outperform classical closed-set training solutions for recognizing unseen objects in images for semantic segmentation. Exi…
ALISE: Accelerating Large Language Model Serving with Speculative Scheduling
Youpeng Zhao, Jun Wang
Large Language Models (LLMs) represent a revolutionary advancement in the contemporary landscape of artificial general intelligence (AGI). As exemplified by ChatGPT, LLM-based appl…
ALISA: Accelerating Large Language Model Inference via Sparsity-Aware KV Caching
Youpeng Zhao, Di Wu, Jun Wang
The Transformer architecture has significantly advanced natural language processing (NLP) and has been foundational in developing large language models (LLMs) such as LLaMA and OPT…
Merino: Entropy-driven Design for Generative Language Models on IoT Devices
Youpeng Zhao, Ming Lin, Huadong Tang +2
Generative Large Language Models (LLMs) stand as a revolutionary advancement in the modern era of artificial intelligence (AI). However, scaling down LLMs for resource-constrained…