Yilun Xie

About

Yilun Xie is a leading researcher in the intersection of deep learning and edge computing, with a primary focus on the efficient deployment of neural networks on resource-constrained devices. His most influential work, "Characterizing the Deployment of Deep Neural Networks on Commercial Edge Devices" (2019), has garnered 109 citations and stands as a foundational study in the field. In this paper, Xie systematically analyzed the performance bottlenecks—such as memory bandwidth, latency, and energy consumption—that arise when running DNNs on commercial edge hardware like smartphones and IoT devices. By providing a comprehensive benchmark and practical insights, his research has directly guided engineers in optimizing model architectures for real-world, in-the-edge inference. This work addresses the critical contradiction between the computational demands of deep neural networks and the limited resources of edge platforms, a challenge that has hindered widespread adoption. Xie’s contributions are essential for advancing applications in computer vision and beyond, making him a key figure in bridging the gap between powerful AI models and practical, on-device deployment.

Research Focus

Key Achievements

Top Papers

1. 1
   Characterizing the Deployment of Deep Neural Networks on Commercial Edge Devices

   109 citations · 2019

Key Collaborators

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