10 citations · 19 across the 16 of their papers we have counts for
6 papers · 1 filter
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…
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…
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…
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…
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…
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…