10 citations · 19 across the 16 of their papers we have counts for
17 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…
AdaFlow: Opportunistic Inference on Asynchronous Mobile Data with Generalized Affinity Control
Fenmin Wu, Sicong Liu, Kehao Zhu +7
The rise of mobile devices equipped with numerous sensors, such as LiDAR and cameras, has spurred the adoption of multi-modal deep intelligence for distributed sensing tasks, such…
UbiHR: Resource-efficient Long-range Heart Rate Sensing on Ubiquitous Devices
Haoyu Bian, Bin Guo, Sicong Liu +3
Ubiquitous on-device heart rate sensing is vital for high-stress individuals and chronic patients. Non-contact sensing, compared to contact-based tools, allows for natural user mon…
AdaShadow: Responsive Test-time Model Adaptation in Non-stationary Mobile Environments
Cheng Fang, Sicong Liu, Zimu Zhou +4
On-device adapting to continual, unpredictable domain shifts is essential for mobile applications like autonomous driving and augmented reality to deliver seamless user experiences…
AdaBridge: Dynamic Data and Computation Reuse for Efficient Multi-task DNN Co-evolution in Edge Systems
Lehao Wang, Zhiwen Yu, Sicong Liu +3
Running multi-task DNNs on mobiles is an emerging trend for various applications like autonomous driving and mobile NLP. Mobile DNNs are often compressed to fit the limited resourc…
Deep Learning Inference on Heterogeneous Mobile Processors: Potentials and Pitfalls
Sicong Liu, Wentao Zhou, Zimu Zhou +5
There is a growing demand to deploy computation-intensive deep learning (DL) models on resource-constrained mobile devices for real-time intelligent applications. Equipped with a v…
AdaOper: Energy-efficient and Responsive Concurrent DNN Inference on Mobile Devices
Zheng Lin, Bin Guo, Sicong Liu +4
Deep neural network (DNN) has driven extensive applications in mobile technology. However, for long-running mobile apps like voice assistants or video applications on smartphones,…
AdaMEC: Towards a Context-Adaptive and Dynamically-Combinable DNN Deployment Framework for Mobile Edge Computing
Bowen Pang, Sicong Liu, Hongli Wang +6
With the rapid development of deep learning, recent research on intelligent and interactive mobile applications (e.g., health monitoring, speech recognition) has attracted extensiv…
AdaEvo: Edge-Assisted Continuous and Timely DNN Model Evolution for Mobile Devices
Lehao Wang, Zhiwen Yu, Haoyi Yu +4
Mobile video applications today have attracted significant attention. Deep learning model (e.g. deep neural network, DNN) compression is widely used to enable on-device inference f…
Enabling Resource-efficient AIoT System with Cross-level Optimization: A survey
Sicong Liu, Bin Guo, Cheng Fang +4
The emerging field of artificial intelligence of things (AIoT, AI+IoT) is driven by the widespread use of intelligent infrastructures and the impressive success of deep learning (D…
AdaEnlight: Energy-aware Low-light Video Stream Enhancement on Mobile Devices
Sicong Liu, Xiaochen Li, Zimu Zhou +4
The ubiquity of camera-embedded devices and the advances in deep learning have stimulated various intelligent mobile video applications. These applications often demand on-device p…
Decentralized Multi-AGV Task Allocation based on Multi-Agent Reinforcement Learning with Information Potential Field Rewards
Mengyuan Li, Bin Guo, Jiangshan Zhang +5
Automated Guided Vehicles (AGVs) have been widely used for material handling in flexible shop floors. Each product requires various raw materials to complete the assembly in produc…