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QuantumNAS: Noise-Adaptive Search for Robust Quantum Circuits


Jul 22, 2021
Hanrui Wang, Yongshan Ding, Jiaqi Gu, Yujun Lin, David Z. Pan, Frederic T. Chong, Song Han

* 15 pages, 23 figures. Code available at https://github.com/mit-han-lab/pytorch-quantum 

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NAAS: Neural Accelerator Architecture Search


May 27, 2021
Yujun Lin, Mengtian Yang, Song Han

* Accepted by DAC'21 

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Searching Efficient 3D Architectures with Sparse Point-Voxel Convolution


Aug 13, 2020
Haotian Tang, Zhijian Liu, Shengyu Zhao, Yujun Lin, Ji Lin, Hanrui Wang, Song Han

* ECCV 2020. The first two authors contributed equally to this work. Project page: http://spvnas.mit.edu/ 

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Hardware-Centric AutoML for Mixed-Precision Quantization


Aug 11, 2020
Kuan Wang, Zhijian Liu, Yujun Lin, Ji Lin, Song Han

* International Journal of Computer Vision (IJCV), 2020 
* Journal preprint of arXiv:1811.08886 (IJCV, 2020). The first three authors contributed equally to this work. Project page: https://hanlab.mit.edu/projects/haq/ 

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MCUNet: Tiny Deep Learning on IoT Devices


Jul 20, 2020
Ji Lin, Wei-Ming Chen, Yujun Lin, John Cohn, Chuang Gan, Song Han

* Demo video available here: https://youtu.be/YvioBgtec4U 

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Lite Transformer with Long-Short Range Attention


Apr 24, 2020
Zhanghao Wu, Zhijian Liu, Ji Lin, Yujun Lin, Song Han

* ICLR 2020. The first two authors contributed equally to this work 

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Point-Voxel CNN for Efficient 3D Deep Learning


Jul 08, 2019
Zhijian Liu, Haotian Tang, Yujun Lin, Song Han

* The first two authors contributed equally to this work 

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Design Automation for Efficient Deep Learning Computing


Apr 24, 2019
Song Han, Han Cai, Ligeng Zhu, Ji Lin, Kuan Wang, Zhijian Liu, Yujun Lin


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HAQ: Hardware-Aware Automated Quantization


Dec 06, 2018
Kuan Wang, Zhijian Liu, Yujun Lin, Ji Lin, Song Han

* The first three authors contributed equally to this work. Project page: https://hanlab.mit.edu/projects/haq/ 

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Deep Gradient Compression: Reducing the Communication Bandwidth for Distributed Training


Feb 05, 2018
Yujun Lin, Song Han, Huizi Mao, Yu Wang, William J. Dally

* ICLR 2018 
* we find 99.9% of the gradient exchange in distributed SGD is redundant; we reduce the communication bandwidth by two orders of magnitude without losing accuracy 

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