175 citations · 262 across the 4 of their papers we have counts for
4 papers
Tiny Machine Learning: Progress and Futures
Ji Lin, Ligeng Zhu, Wei-Ming Chen +2
Tiny Machine Learning (TinyML) is a new frontier of machine learning. By squeezing deep learning models into billions of IoT devices and microcontrollers (MCUs), we expand the scop…
PockEngine: Sparse and Efficient Fine-tuning in a Pocket
Ligeng Zhu, Lanxiang Hu, Ji Lin +4
On-device learning and efficient fine-tuning enable continuous and privacy-preserving customization (e.g., locally fine-tuning large language models on personalized data). However,…
On-Device Training Under 256KB Memory
Ji Lin, Ligeng Zhu, Wei-Ming Chen +3
On-device training enables the model to adapt to new data collected from the sensors by fine-tuning a pre-trained model. Users can benefit from customized AI models without having…
Lite Pose: Efficient Architecture Design for 2D Human Pose Estimation
Yihan Wang, Muyang Li, Han Cai +2
Pose estimation plays a critical role in human-centered vision applications. However, it is difficult to deploy state-of-the-art HRNet-based pose estimation models on resource-cons…