Search Results for author: Wei-Ming Chen

Found 7 papers, 4 papers with code

Tiny Machine Learning: Progress and Futures

no code implementations28 Mar 2024 Ji Lin, Ligeng Zhu, Wei-Ming Chen, Wei-Chen Wang, Song Han

By squeezing deep learning models into billions of IoT devices and microcontrollers (MCUs), we expand the scope of AI applications and enable ubiquitous intelligence.

PockEngine: Sparse and Efficient Fine-tuning in a Pocket

no code implementations26 Oct 2023 Ligeng Zhu, Lanxiang Hu, Ji Lin, Wei-Chen Wang, Wei-Ming Chen, Chuang Gan, Song Han

On-device learning and efficient fine-tuning enable continuous and privacy-preserving customization (e. g., locally fine-tuning large language models on personalized data).

Privacy Preserving

On-Device Training Under 256KB Memory

1 code implementation30 Jun 2022 Ji Lin, Ligeng Zhu, Wei-Ming Chen, Wei-Chen Wang, Chuang Gan, Song Han

To reduce the memory footprint, we propose Sparse Update to skip the gradient computation of less important layers and sub-tensors.

Quantization Transfer Learning

Lite Pose: Efficient Architecture Design for 2D Human Pose Estimation

1 code implementation CVPR 2022 Yihan Wang, Muyang Li, Han Cai, Wei-Ming Chen, Song Han

Inspired by this finding, we design LitePose, an efficient single-branch architecture for pose estimation, and introduce two simple approaches to enhance the capacity of LitePose, including Fusion Deconv Head and Large Kernel Convs.

Ranked #5 on Multi-Person Pose Estimation on MS COCO (Validation AP metric)

2D Human Pose Estimation Multi-Person Pose Estimation

Memory-efficient Patch-based Inference for Tiny Deep Learning

no code implementations NeurIPS 2021 Ji Lin, Wei-Ming Chen, Han Cai, Chuang Gan, Song Han

We further propose receptive field redistribution to shift the receptive field and FLOPs to the later stage and reduce the computation overhead.

Image Classification Neural Architecture Search +3

MCUNetV2: Memory-Efficient Patch-based Inference for Tiny Deep Learning

1 code implementation28 Oct 2021 Ji Lin, Wei-Ming Chen, Han Cai, Chuang Gan, Song Han

We further propose network redistribution to shift the receptive field and FLOPs to the later stage and reduce the computation overhead.

Image Classification Neural Architecture Search +3

MCUNet: Tiny Deep Learning on IoT Devices

1 code implementation NeurIPS 2020 Ji Lin, Wei-Ming Chen, Yujun Lin, John Cohn, Chuang Gan, Song Han

Machine learning on tiny IoT devices based on microcontroller units (MCU) is appealing but challenging: the memory of microcontrollers is 2-3 orders of magnitude smaller even than mobile phones.

BIG-bench Machine Learning Neural Architecture Search +1

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