| 2023 | DAC | HyperAttack: An Efficient Attack Framework for HyperDimensional Computing. | Fangxin Liu, Haomin Li, Yongbiao Chen, Tao Yang, Li Jiang |
| 2022 | AAAI | SpikeConverter: An Efficient Conversion Framework Zipping the Gap between Artificial Neural Networks and Spiking Neural Networks. | Fangxin Liu, Wenbo Zhao, Yongbiao Chen, Zongwu Wang, Li Jiang |
| 2022 | DAC | PIM-DH: ReRAM-based processing-in-memory architecture for deep hashing acceleration. | Fangxin Liu, Wenbo Zhao, Yongbiao Chen, Zongwu Wang, Zhezhi He, Rui Yang, Qidong Tang, Tao Yang, Cheng Zhuo, Li Jiang |
| 2022 | DAC | EBSP: evolving bit sparsity patterns for hardware-friendly inference of quantized deep neural networks. | Fangxin Liu, Wenbo Zhao, Zongwu Wang, Yongbiao Chen, Zhezhi He, Naifeng Jing, Xiaoyao Liang, Li Jiang |
| 2022 | DAC | SATO: spiking neural network acceleration via temporal-oriented dataflow and architecture. | Fangxin Liu, Wenbo Zhao, Zongwu Wang, Yongbiao Chen, Tao Yang, Zhezhi He, Xiaokang Yang, Li Jiang |
| 2022 | ICASSP | DynSNN: A Dynamic Approach to Reduce Redundancy in Spiking Neural Networks. | Fangxin Liu, Wenbo Zhao, Yongbiao Chen, Zongwu Wang, Fei Dai |
| 2022 | ICCD | Randomize and Match: Exploiting Irregular Sparsity for Energy Efficient Processing in SNNs. | Fangxin Liu, Zongwu Wang, Wenbo Zhao, Yongbiao Chen, Tao Yang, Xiaokang Yang, Li Jiang |
| 2021 | ICCAD | Bit-Transformer: Transforming Bit-level Sparsity into Higher Preformance in ReRAM-based Accelerator. | Fangxin Liu, Wenbo Zhao, Zhezhi He, Zongwu Wang, Yilong Zhao, Yongbiao Chen, Li Jiang |
| 2021 | VCIP | Seq-Masks: Bridging the gap between appearance and gait modeling for video-based person re-identification. | Zhigang Chang, Zhao Yang, Yongbiao Chen, Qin Zhou, Shibao Zheng |