The emerging field of artificial intelligence of things (AIoT, AI+IoT) is driven by the widespread use of intelligent mobile & embedded infrastructures and the impressive success of deep learning(DL). With the deployment of DL on various intelligent embedded infrastructures featuring rich sensors and weak DL computing capabilities, a diverse range of AIoT applications has become possible. However, DL models are notoriously resource-intensive. Existing research strives to realize near-/realtime inference of AIoT live data and low-cost training using AIoT datasets on resource-scare infrastructures. Accordingly, the accuracy and responsiveness of DL models are bounded by resource availability. To this end, the algorithm-system co-design that jointly optimizes the resource-friendly DL models and model-adaptive system scheduling improves the runtime resource availability and thus pushes the performance boundary set by the standalone level. The cross-level optimization landscape involves various granularity, including sensor data acquisition, DL model compression, computation graph, operator, memory schedule, or hardware instructor in both on-device and distributed networked paradigms. It adaptively scales the cross-level AIoT system from on-device to distributed schemes for achieving better performance-resource efficiency trade-off. The distributed AIoT devices collaborate on demand for two motivations: On-demand sensing source association and on-demand computing resource aggregation. Furthermore, due to the dynamic nature of AIoT context, which includes heterogeneous hardware, agnostic sensing data, varying user-specified performance demands, and resource constraints, this workshop also explores the context-aware inter-/intra-device automatic cross-level adaptation.

This workshop serves as an excellent platform for researchers, system developers, and practitioners to explore the design, development, deployment, and operational challenges of adaptive AIoT systems. Contributions in theoretical and practical aspects, including embedded and edge AI systems, on-device machine learning, and methodological issues in AIoT systems, are highly encouraged.


The focus areas include, but not limited to:

✔Mobile connectivity communication paradigms
✔ Novel AIoT applications
✔ Techniques and systems for novel human-machine interactions and experiences
✔ Intelligent data processing in AIoT systems
✔ Adaptive perception and computing in resource-constrained environments
✔ Distributed multi-modal data fusion
✔ Real-time computing and data privacy in AIoT
✔ Personalized AI model evolutions on IoT devices
✔ Computational robustness in dynamic AIoT systems
✔ Energy-efficient designs and strategies in AIoT
✔ AI-based edge resource scheduling
✔ Lightweight LLM models in AIoT
✔ Lightweight Vision Transformer models in AIoT


Workshop papers will be included with the MobiSys proceedings and posted in the ACM Digital Library. The workshop will run as a hybrid event in the time zone of the conference’s original venue (i.e. CET)