3 citations · 4 across the 7 of their papers we have counts for
7 papers
SlimPipe: Memory-Thrifty and Efficient Pipeline Parallelism for Long-Context LLM Training
Zhouyang Li, Yuliang Liu, Wei Zhang +4
Pipeline Parallelism (PP) serves as a crucial technique for training Large Language Models (LLMs), owing to its capability to alleviate memory pressure from model states with relat…
Decoding at the Speed of Thought: Harnessing Parallel Decoding of Lexical Units for LLMs
Chenxi Sun, Hongzhi Zhang, Zijia Lin +8
Large language models have demonstrated exceptional capability in natural language understanding and generation. However, their generation speed is limited by the inherently sequen…
KwaiYiiMath: Technical Report
Jiayi Fu, Lei Lin, Xiaoyang Gao +18
Recent advancements in large language models (LLMs) have demonstrated remarkable abilities in handling a variety of natural language processing (NLP) downstream tasks, even on math…
ASP: Automatic Selection of Proxy dataset for efficient AutoML
Peng Yao, Chao Liao, Jiyuan Jia +4
Deep neural networks have gained great success due to the increasing amounts of data, and diverse effective neural network designs. However, it also brings a heavy computing burden…
USDC: Unified Static and Dynamic Compression for Visual Transformer
Huan Yuan, Chao Liao, Jianchao Tan +5
Visual Transformers have achieved great success in almost all vision tasks, such as classification, detection, and so on. However, the model complexity and the inference speed of t…
Unified Language-Vision Pretraining in LLM with Dynamic Discrete Visual Tokenization
Yang Jin, Kun Xu, Liwei Chen +12
Recently, the remarkable advance of the Large Language Model (LLM) has inspired researchers to transfer its extraordinary reasoning capability to both vision and language data. How…
Resource Constrained Model Compression via Minimax Optimization for Spiking Neural Networks
Jue Chen, Huan Yuan, Jianchao Tan +3
Brain-inspired Spiking Neural Networks (SNNs) have the characteristics of event-driven and high energy-efficient, which are different from traditional Artificial Neural Networks (A…