11 citations · 26 across the 12 of their papers we have counts for
5 papers · 1 filter
Pipeline Parallelism is All You Need for Optimized Early-Exit Based Self-Speculative Decoding
Ruanjun Li, Ziheng Liu, Yuanming Shi +3
Large language models (LLMs) deliver impressive generation quality, but incur very high inference cost because each output token is generated auto-regressively through all model la…
Edge Large AI Models: Collaborative Deployment and IoT Applications
Zixin Wang, Yuanming Shi, Khaled. B. Letaief
Large artificial intelligence models (LAMs) emulate human-like problem-solving capabilities across diverse domains, modalities, and tasks. By leveraging the communication and compu…
Edge Large AI Models: Revolutionizing 6G Networks
Zixin Wang, Yuanming Shi, Yong Zhou +2
Large artificial intelligence models (LAMs) possess human-like abilities to solve a wide range of real-world problems, exemplifying the potential of experts in various domains and…
Satellite Edge Artificial Intelligence with Large Models: Architectures and Technologies
Yuanming Shi, Jingyang Zhu, Chunxiao Jiang +2
Driven by the growing demand for intelligent remote sensing applications, large artificial intelligence (AI) models pre-trained on large-scale unlabeled datasets and fine-tuned for…
Satellite Federated Fine-Tuning for Foundation Models in Space Computing Power Networks
Yan Zhu, Jingyang Zhu, Ting Wang +3
Advancements in artificial intelligence (AI) and low-earth orbit (LEO) satellites have promoted the application of large remote sensing foundation models for various downstream tas…