TY - GEN
T1 - Low-latency MLLM Inference with Spatiotemporal Heterogeneous Distributed Multimodal Data
AU - Xu, Xiangrui
AU - Liu, Sicong
AU - Yu, Zhiwen
AU - Wang, Lehao
AU - Guo, Bin
N1 - Publisher Copyright:
© 2024 IEEE.
PY - 2024
Y1 - 2024
N2 - Distributed sensing systems have been widely applied in various Internet of Things (IoT) scenarios, and the emergence of the Multimodal Large Language Model (MLLM) has opened up new possibilities for these systems. However, the spatiotemporal heterogeneity and asynchronous arrival of distributed mobile data make achieving low-latency, high-accuracy MLLM inference extremely challenging. In this paper, we propose a framework of MLLM inference with spatiotemporal heterogeneous distributed data to achieve low-latency, high-accuracy MLLM inference in distributed sensing systems.
AB - Distributed sensing systems have been widely applied in various Internet of Things (IoT) scenarios, and the emergence of the Multimodal Large Language Model (MLLM) has opened up new possibilities for these systems. However, the spatiotemporal heterogeneity and asynchronous arrival of distributed mobile data make achieving low-latency, high-accuracy MLLM inference extremely challenging. In this paper, we propose a framework of MLLM inference with spatiotemporal heterogeneous distributed data to achieve low-latency, high-accuracy MLLM inference in distributed sensing systems.
KW - distributed multimodal system
KW - low-latency inference
KW - MLLM
KW - multimodal inference
UR - https://www.scopus.com/pages/publications/85200793056
U2 - 10.1109/CSCAIoT62585.2024.00009
DO - 10.1109/CSCAIoT62585.2024.00009
M3 - 会议稿件
AN - SCOPUS:85200793056
T3 - Proceedings - 2024 IEEE Coupling of Sensing and Computing in AIoT Systems, CSCAIoT 2024
SP - 19
EP - 20
BT - Proceedings - 2024 IEEE Coupling of Sensing and Computing in AIoT Systems, CSCAIoT 2024
PB - Institute of Electrical and Electronics Engineers Inc.
T2 - 2024 IEEE Coupling of Sensing and Computing in AIoT Systems, CSCAIoT 2024
Y2 - 13 May 2024
ER -