2 citations · 2 across the 5 of their papers we have counts for
6 papers
OpWeave: Flexible Operator Disaggregation for Heterogeneous LLM Serving
Zikun Li, Yixuan Mei, Shiqi Pan +9
LLM serving systems increasingly disaggregate inference into finer-grained stages, with recent approaches separating attention from FFN or MoE execution during decode. This operato…
Coral: Cost-Efficient Multi-LLM Serving over Heterogeneous Cloud GPUs
Yixuan Mei, Zikun Li, Zixuan Chen +5
The landscape of large language models (LLMs) has become increasingly fragmented, with no single model dominating the field. Meanwhile, cloud providers offer a diverse array of mid…
AdaServe: Accelerating Multi-SLO LLM Serving with SLO-Customized Speculative Decoding
Zikun Li, Zhuofu Chen, Remi Delacourt +11
Modern large language model (LLM) applications exhibit diverse service-level objectives (SLOs), from low-latency requirements in interactive coding assistants to more relaxed const…
TidalDecode: Fast and Accurate LLM Decoding with Position Persistent Sparse Attention
Lijie Yang, Zhihao Zhang, Zhuofu Chen +2
Large language models (LLMs) have driven significant advancements across diverse NLP tasks, with long-context models gaining prominence for handling extended inputs. However, the e…
Quarl: A Learning-Based Quantum Circuit Optimizer
Zikun Li, Jinjun Peng, Yixuan Mei +4
Optimizing quantum circuits is challenging due to the very large search space of functionally equivalent circuits and the necessity of applying transformations that temporarily dec…
Quartz: Superoptimization of Quantum Circuits (Extended Version)
Mingkuan Xu, Zikun Li, Oded Padon +8
Existing quantum compilers optimize quantum circuits by applying circuit transformations designed by experts. This approach requires significant manual effort to design and impleme…