6 papers
LeanStream: A Speculate-and-Refine Streaming Framework for Efficient on-Device LLM Inference
Renyuan Liu, Yuyang Leng, Kaiyan Liu +7
On-device LLM inference is attractive for privacy and responsiveness, but remains challenging on mobile and embedded devices because model weights far exceed available DRAM. Prior…
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…
Multi-Resolution End-to-End Deep Neural Network for Optimizing Latency-Accuracy Tradeoff in Autonomous Driving
Qitao Weng, Heechul Yun
Latency-accuracy tradeoffs are fundamental in real-time applications of deep neural networks (DNNs) for cyber-physical systems. In autonomous driving, in particular, safety depends…
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…
TinyLidarNet: 2D LiDAR-based End-to-End Deep Learning Model for F1TENTH Autonomous Racing
Mohammed Misbah Zarrar, Qitao Weng, Bakhbyergyen Yerjan +2
Prior research has demonstrated the effectiveness of end-to-end deep learning for robotic navigation, where the control signals are directly derived from raw sensory data. However,…
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…