1 citations · 1 across the 12 of their papers we have counts for
13 papers
Analytical Power-Aware Provisioning for Prefill-Decode Disaggregated AI Inference
Mingyuan Yan, Haiyu Wang, Linxuan Biao +3
Power availability increasingly constrains the operation of AI inference fleets, creating a need for provisioning methods that jointly consider serving capacity and power consumpti…
Acceptance-Aware Draft Model Training for Speculative Decoding
Tianhua Xia, Mugilan Ganesan, Yifei Feng +3
Speculative decoding accelerates large language model (LLM) inference by using a lightweight draft model to generate multiple candidate tokens that are verified by the target model…
LSVD: Loss-Aware Low-Rank Approximation for Efficient Low-Precision Vision-Language Models
Haiyu Wang, Yutong Wang, Leshu Li +2
Vision-language models (VLMs) deliver strong multimodal reasoning capabilities, but their large computational cost and high parameter counts make deployment challenging on resource…
DREAM-S: Speculative Decoding with Searchable Drafting and Target-Aware Refinement for Multimodal Generation
Zining Liu, Yunhai Hu, Tianhua Xia +4
Speculative decoding (SD) has proven to be an effective technique for accelerating autoregressive generation in large language models (LLMs) however, its application to vision-lang…
DREAM-R: Multimodal Speculative Reasoning with RL-Based Refined Drafting, Precise Verification, and Fully Parallel Execution
Yunhai Hu, Zining Liu, Xiangyang Yin +5
Speculative reasoning has recently been proposed as a means to accelerate reasoning-intensive generation in large multimodal models, but its effectiveness is often constrained by m…
LipoAgent: Coordinating Fine-Tuned LLM Agents for Safer Lipid Design
Leshu Li, An Lu, Haiyu Wang +5
Lipid nanoparticles (LNPs) are among the most clinically mature platforms for nucleic acid delivery, yet designing lipids that are both effective and biologically safe remains a ma…
CodeQuant: Unified Clustering and Quantization for Enhanced Outlier Smoothing in Low-Precision Mixture-of-Experts
Xiangyang Yin, Xingyu Liu, Tianhua Xia +5
Outliers have emerged as a fundamental bottleneck in preserving accuracy for low-precision large models, particularly within Mixture-of-Experts (MoE) architectures that are increas…
pFedNavi: Structure-Aware Personalized Federated Vision-Language Navigation for Embodied AI
Qingqian Yang, Hao Wang, Sai Qian Zhang +6
Vision-Language Navigation VLN requires large-scale trajectory instruction data from private indoor environments, raising significant privacy concerns. Federated Learning FL mitiga…
DSD: A Distributed Speculative Decoding Solution for Edge-Cloud Agile Large Model Serving
Fengze Yu, Leshu Li, Brad McDanel +1
Large language model (LLM) inference often suffers from high decoding latency and limited scalability across heterogeneous edge-cloud environments. Existing speculative decoding (S…
PipeSpec: Breaking Stage Dependencies in Hierarchical LLM Decoding
Bradley McDanel, Sai Qian Zhang, Yunhai Hu +1
Speculative decoding accelerates large language model inference by using smaller draft models to generate candidate tokens for parallel verification. However, current approaches ar…
Unlocking Visual Secrets: Inverting Features with Diffusion Priors for Image Reconstruction
Sai Qian Zhang, Ziyun Li, Chuan Guo +5
Inverting visual representations within deep neural networks (DNNs) presents a challenging and important problem in the field of security and privacy for deep learning. The main go…
DFRot: Achieving Outlier-Free and Massive Activation-Free for Rotated LLMs with Refined Rotation
Jingyang Xiang, Sai Qian Zhang
Rotating the activation and weight matrices to reduce the influence of outliers in large language models (LLMs) has recently attracted significant attention, particularly in the co…
HQ-DiT: Efficient Diffusion Transformer with FP4 Hybrid Quantization
Wenxuan Liu, Sai Qian Zhang
Diffusion Transformers (DiTs) have recently gained substantial attention in both industrial and academic fields for their superior visual generation capabilities, outperforming tra…