9 papers
AdaSprite: Resource-efficient Online Co-Adaptation for V2I Systems Under Large-scale Data Drifts
Lehao Wang, Zhiwen Yu, Sicong Liu +3
The rise of vehicle-infrastructure (V2I) collaboration enables safer and broader perception. To process large-scale V2I video streams, vision-language models (VLMs) are promising a…
AppFlow: Memory Scheduling for Cold Launch of Large Apps on Mobile and Vehicle Systems
Xiaochen Li, Sicong Liu, Bin Guo +4
GB-scale large apps like on-device LLMs and rich media editors are becoming the next-generation trend, but their heavy memory and I/O demands, especially during multitasking, cause…
Adaptive and Resource-efficient Agentic AI Systems for Mobile and Embedded Devices: A Survey
Sicong Liu, Weiye Wu, Xiangrui Xu +4
Foundation models have reshaped AI by unifying fragmented architectures into scalable backbones with multimodal reasoning and contextual adaptation. In parallel, the long-standing…
SURGEON: Memory-Adaptive Fully Test-Time Adaptation via Dynamic Activation Sparsity
Ke Ma, Jiaqi Tang, Bin Guo +8
Despite the growing integration of deep models into mobile terminals, the accuracy of these models declines significantly due to various deployment interferences. Test-time adaptat…
CrowdHMTware: A Cross-level Co-adaptation Middleware for Context-aware Mobile DL Deployment
Sicong Liu, Bin Guo, Shiyan Luo +7
There are many deep learning (DL) powered mobile and wearable applications today continuously and unobtrusively sensing the ambient surroundings to enhance all aspects of human liv…
AdaScale: Dynamic Context-aware DNN Scaling via Automated Adaptation Loop on Mobile Devices
Yuzhan Wang, Sicong Liu, Bin Guo +6
Deep learning is reshaping mobile applications, with a growing trend of deploying deep neural networks (DNNs) directly to mobile and embedded devices to address real-time performan…