8 citations · 17 across the 6 of their papers we have counts for
6 papers
EcoServe: Designing Carbon-Aware AI Inference Systems
Yueying Li, Zhanqiu Hu, Esha Choukse +3
The rapid increase in LLM ubiquity and scale levies unprecedented demands on computing infrastructure. These demands not only incur large compute and memory resources but also sign…
JoinGym: An Efficient Query Optimization Environment for Reinforcement Learning
Kaiwen Wang, Junxiong Wang, Yueying Li +3
Join order selection (JOS) is the problem of ordering join operations to minimize total query execution cost and it is the core NP-hard combinatorial optimization problem of query…
End-to-End Application Cloning for Distributed Cloud Microservices with Ditto
Mingyu Liang, Yu Gan, Yueying Li +4
We present Ditto, an automated framework for cloning end-to-end cloud applications, both monolithic and microservices, which captures I/O and network activity, as well as kernel op…
Efficient Privacy-Preserving Machine Learning with Lightweight Trusted Hardware
Pengzhi Huang, Thang Hoang, Yueying Li +2
In this paper, we propose a new secure machine learning inference platform assisted by a small dedicated security processor, which will be easier to protect and deploy compared to…
Efficient Planning in a Compact Latent Action Space
Zhengyao Jiang, Tianjun Zhang, Michael Janner +4
Planning-based reinforcement learning has shown strong performance in tasks in discrete and low-dimensional continuous action spaces. However, planning usually brings significant c…
AutoCAT: Reinforcement Learning for Automated Exploration of Cache-Timing Attacks
Mulong Luo, Wenjie Xiong, Geunbae Lee +6
The aggressive performance optimizations in modern microprocessors can result in security vulnerabilities. For example, timing-based attacks in processor caches can steal secret ke…