94 citations · 245 across the 21 of their papers we have counts for
4 papers · 1 filter
EdgeFM: Leveraging Foundation Model for Open-set Learning on the Edge
Bufang Yang, Lixing He, Neiwen Ling +5
Deep Learning (DL) models have been widely deployed on IoT devices with the help of advancements in DL algorithms and chips. However, the limited resources of edge devices make the…
CAME: Confidence-guided Adaptive Memory Efficient Optimization
Yang Luo, Xiaozhe Ren, Zangwei Zheng +3
Adaptive gradient methods, such as Adam and LAMB, have demonstrated excellent performance in the training of large language models. Nevertheless, the need for adaptivity requires m…
Response Length Perception and Sequence Scheduling: An LLM-Empowered LLM Inference Pipeline
Zangwei Zheng, Xiaozhe Ren, Fuzhao Xue +3
Large language models (LLMs) have revolutionized the field of AI, demonstrating unprecedented capacity across various tasks. However, the inference process for LLMs comes with sign…
PanGu-Σ: Towards Trillion Parameter Language Model with Sparse Heterogeneous Computing
Xiaozhe Ren, Pingyi Zhou, Xinfan Meng +14
The scaling of large language models has greatly improved natural language understanding, generation, and reasoning. In this work, we develop a system that trained a trillion-param…