| 2025 | ACL | Improving Efficiency in Large Language Models via Extendable Block Floating Point Representation. | Dongyang Li, Zeyang Li, Bosheng Liu, Jigang Wu |
| 2024 | ASPDAC | Quantization-aware Optimization Approach for CNNs Inference on CPUs. | Jiasong Chen, Zeming Xie, Weipeng Liang, Bosheng Liu, Xin Zheng, Jigang Wu, Xiaoming Xiong |
| 2024 | ISCAS | Accelerating Frequency-domain Convolutional Neural Networks Inference using FPGAs. | Yi Chen, Bosheng Liu, Yongqi Xu, Jigang Wu, Xiaoming Chen, Peng Liu, Qingguo Zhou, Yinhe Han |
| 2023 | ASPDAC | Accelerating Convolutional Neural Networks in Frequency Domain via Kernel-Sharing Approach. | Bosheng Liu, Hongyi Liang, Jigang Wu, Xiaoming Chen, Peng Liu, Yinhe Han |
| 2021 | DAC | F3D: Accelerating 3D Convolutional Neural Networks in Frequency Space Using ReRAM. | Bosheng Liu, Zhuoshen Jiang, Jigang Wu, Xiaoming Chen, Yinhe Han, Peng Liu |
| 2020 | ASPDAC | Search-free Accelerator for Sparse Convolutional Neural Networks. | Bosheng Liu, Xiaoming Chen, Yinhe Han, Ying Wang, Jiajun Li, Haobo Xu, Xiaowei Li |
| 2019 | ASPDAC | Addressing the issue of processing element under-utilization in general-purpose systolic deep learning accelerators. | Bosheng Liu, Xiaoming Chen, Ying Wang, Yinhe Han, Jiajun Li, Haobo Xu, Xiaowei Li |
| 2019 | ASPDAC | Simulate-the-hardware: training accurate binarized neural networks for low-precision neural accelerators. | Jiajun Li, Ying Wang, Bosheng Liu, Yinhe Han, Xiaowei Li |
| 2019 | DAC | Merging Everything (ME): A Unified FPGA Architecture Based on Logic-in-Memory Techniques. | Xiaoming Chen, Longxiang Yin, Bosheng Liu, Yinhe Han |
| 2019 | ICCAD | ACG-Engine: An Inference Accelerator for Content Generative Neural Networks. | Haobo Xu, Ying Wang, Yujie Wang, Jiajun Li, Bosheng Liu, Yinhe Han |
| 2016 | DAC | C-brain: a deep learning accelerator that tames the diversity of CNNs through adaptive data-level parallelization. | Lili Song, Ying Wang, Yinhe Han, Xin Zhao, Bosheng Liu, Xiaowei Li |