Yuanchun Li
MobiLLMs 2024
The advent of large foundation models in the realm of AI, including large language models (LLMs), large vision models (LVMs), large multimodal models (LMMs), and diffusion models, has opened new frontiers in various applications. However, deploying these resource-intensive models on edge and mobile devices poses significant challenges due to their limited computing power and storage capacity. Their interaction with mobile devices and applications also needs to be reshaped. This workshop aims to explore the
The workshop welcomes contributions dealing with all facets of edge foundation models, including system aspects, theoretical studies, algorithm and hardware design, as well as measurements, ethicality, humanity, and sustainability. We are particularly looking for papers reporting on experimental results of deployed systems, summaries of challenges or advancements, measurements, and innovative applications. We welcome in particular also contributions from interdisciplinary teams to present algorithm-system-h
Topics of Interests : Novel mobile applications empowered by foundation models Compression of foundation models Efficient inference of on-device foundation models Federated learning of large foundation models Cloud-edge collaborative inference and training of foundation models Hardware Accelerators for on-device foundation models Foundation models for sensing Foundation models as a system service Measurement and empirical studies of edge foundation models Deployment of edge foundation models Foundation mode
Yuanchun Li
MobiLLMs 2024