Yinwei Dai

I am a third-year Computer Science Ph.D. student at Princeton University, working with Prof. Ravi Netravali. I am affiliated with Princeton Systems for AI Lab (SAIL).

I obtained my M.S.E. and B.S.E. in Computer Science at University of Michigan, where I worked with Prof. Mosharaf Chowdhury and Prof. Harsha V. Madhyastha on projects related to networked systems, and B.S.E in Electrical and Computer Engineering from Shanghai Jiao Tong University

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My research interests are at the intersection of networked systems and machine learning. Recently, my work has focused on improving inference efficiency and scalability in systems that serve large language models and their applications.

We present Legilimens, which embraces the inverted resource profile of SoC-class devices—shifting the cost of adaptation for video analytics from compute to memory.

We introduce SpecReason, a system that automatically accelerates LRM inference by using a lightweight model to (speculatively) carry out simpler intermediate reasoning steps and reserving the costly base model only to assess (and potentially correct) the speculated outputs

We present Apparate, the first system that automatically injects and manages Early Exits for serving a wide range of models.

We present E3 to address the detrimental trade-off that Early Exits introduce between compute savings (from exits) and resource utilization (from batching) in EE-DNNs.

We propose Auxo, a scalable FL system that enables the server to decompose the large-scale FL task into groups with smaller intra-cohort heterogeneity.

We introduce ModelKeeper, a cluster-scale model service framework to accelerate DNN training, by reducing the computation needed for achieving the same model performance via automated model transformation.

We present FedScale, a diverse set of challenging and realistic benchmark datasets to facilitate scalable, comprehensive, and reproducible federated learning (FL) research.