Ji Lin
- mit-han-lab/mcunet
- mit-han-lab/llm-awq
- tonylins/pytorch-mobilenet-v2
- mit-han-lab/anycost-gan
- mit-han-lab/smoothquant
- mit-han-lab/gan-compression
- mit-han-lab/tinyengine
- mit-han-lab/tiny-training
[MLSys 2024 Best Paper Award] AWQ: Activation-aware Weight Quantization for LLM Compression and Acceleration
[ICML 2023] SmoothQuant: Accurate and Efficient Post-Training Quantization for Large Language Models
A PyTorch implementation of MobileNet V2 architecture and pretrained model.
[CVPR 2020] GAN Compression: Efficient Architectures for Interactive Conditional GANs
[NeurIPS 2020] MCUNet: Tiny Deep Learning on IoT Devices; [NeurIPS 2021] MCUNetV2: Memory-Efficient Patch-based Inference for Tiny Deep Learning; [NeurIPS 2022] MCUNetV3: On-Device Training Under 256KB Memory
[CVPR 2021] Anycost GANs for Interactive Image Synthesis and Editing
[NeurIPS 2020] MCUNet: Tiny Deep Learning on IoT Devices; [NeurIPS 2021] MCUNetV2: Memory-Efficient Patch-based Inference for Tiny Deep Learning
On-Device Training Under 256KB Memory [NeurIPS'22]