A Survey of Resource-efficient LLM and Multimodal Foundation Models

1 code implementation • 16 Jan 2024 • Mengwei Xu, Wangsong Yin, Dongqi Cai, Rongjie Yi, Daliang Xu, QiPeng Wang, Bingyang Wu, Yihao Zhao, Chen Yang, Shihe Wang, Qiyang Zhang, Zhenyan Lu, Li Zhang, Shangguang Wang, Yuanchun Li, Yunxin Liu, Xin Jin, Xuanzhe Liu

Large foundation models, including large language models (LLMs), vision transformers (ViTs), diffusion, and LLM-based multimodal models, are revolutionizing the entire machine learning lifecycle, from training to deployment.

EdgeMoE: Fast On-Device Inference of MoE-based Large Language Models

no code implementations • 28 Aug 2023 • Rongjie Yi, Liwei Guo, Shiyun Wei, Ao Zhou, Shangguang Wang, Mengwei Xu

Large Language Models (LLMs) such as GPTs and LLaMa have ushered in a revolution in machine intelligence, owing to their exceptional capabilities in a wide range of machine learning tasks.

Computational Efficiency

Boosting DNN Cold Inference on Edge Devices

1 code implementation • 15 Jun 2022 • Rongjie Yi, Ting Cao, Ao Zhou, Xiao Ma, Shangguang Wang, Mengwei Xu

DNNs are ubiquitous on edge devices nowadays.

Scheduling