1 citations · 1 across the 8 of their papers we have counts for
8 papers
RAC: Reference-Aware Activation Compression for Communication-Efficient Split LLM Inference
Guotao Yang, Mingxi Zhao, Haopeng Li +4
Large language model (LLM) agents repeatedly process long, privacy-sensitive contexts, while cloud-only deployment exposes user data beyond the trusted endpoint and fully local dep…
AsymSpec: Efficient Cloud-Edge Speculative Decoding over Asymmetric Networks
Guotao Yang, Hao Chen, Rui Guo +5
Cloud-edge speculative decoding places a lightweight draft model at an edge gateway and a higher-quality target model in the cloud, but inserts communication into every speculative…
AESOP: Adversarial Execution-path Selection to Overload Deep Learning Pipelines
Tingxi Li, Mingfang Ji, Ravishka Shemal Rathnasuriya +3
Modern machine learning deployments increasingly compose specialized models into dynamic inference pipelines, where upstream components produce intermediate predictions that determ…
Mosaic: Unlocking Long-Context Inference for Diffusion LLMs via Global Memory Planning and Dynamic Peak Taming
Liang Zheng, Bowen Shi, Yitao Hu +5
Diffusion-based large language models (dLLMs) have emerged as a promising paradigm, utilizing simultaneous denoising to enable global planning and iterative refinement. While these…
RAGPulse: An Open-Source RAG Workload Trace to Optimize RAG Serving Systems
Zhengchao Wang, Yitao Hu, Jianing Ye +4
Retrieval-Augmented Generation (RAG) is a critical paradigm for building reliable, knowledge-intensive Large Language Model (LLM) applications. However, the multi-stage pipeline (r…
ServerlessLoRA: Minimizing Latency and Cost in Serverless Inference for LoRA-Based LLMs
Yifan Sui, Hao Wang, Hanfei Yu +2
Serverless computing has grown rapidly for serving Large Language Model (LLM) inference due to its pay-as-you-go pricing, fine-grained GPU usage, and rapid scaling. However, our an…