5 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…
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…
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…
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…
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…