5 papers
Physical Self-Supervised Learning: IMU Sensing without Manual Labels
Yuyang Leng, Renyuan Liu, Shaohan Hu +4
Deep neural networks have become a promising approach for IMU-based sensing, but their scalability is fundamentally limited by costly labeled data and poor robustness to heterogene…
On Exploring Input Resolution Scaling For Anytime LiDAR Object Detection
Ahmet Soyyigit, Shuochao Yao, Heechul Yun
Making tradeoffs between execution latency and result utility (i.e., anytime computing) for adapting to dynamic operational requirements has been shown to enhance the performance o…
TokenFlow: Responsive LLM Text Streaming Serving under Request Burst via Preemptive Scheduling
Junyi Chen, Chuheng Du, Renyuan Liu +6
Real-time LLM interactions demand streamed token generations, where text tokens are progressively generated and delivered to users while balancing two objectives: responsiveness (i…
DAF: An Efficient End-to-End Dynamic Activation Framework for on-Device DNN Training
Renyuan Liu, Yuyang Leng, Kaiyan Liu +6
Recent advancements in on-device training for deep neural networks have underscored the critical need for efficient activation compression to overcome the memory constraints of mob…
VALO: A Versatile Anytime Framework for LiDAR-based Object Detection Deep Neural Networks
Ahmet Soyyigit, Shuochao Yao, Heechul Yun
This work addresses the challenge of adapting dynamic deadline requirements for LiDAR object detection deep neural networks (DNNs). The computing latency of object detection is cri…