Publications
You can also find my articles on my Google Scholar profile.
{=} denotes equal contribution; {*} denotes corresponding author| ISCA 2024 (Top Conf. in Computer Architecture) | Yilong Zhao, Mingyu Gao, Fangxin Liu*, Yiwei Hu, Zongwu Wang, Han Lin, Ji Li, He Xian, Hanlin Dong, Tao Yang, Naifeng Jing, Xiaoyao Liang, Li Jiang UM-PIM: DRAM-based PIM with Uniform & Shared Memory Space (Acceptance Rate: 18%) |
| DAC 2024 (Top Conf. in Design Automation) | Fangxin Liu=, Ning Yang=, Haomin Li, Zongwu Wang, Zhuoran Song, Songwen Pei, Li Jiang INSPIRE: Accelerating Deep Neural Networks via Hardware-friendly Index-Pair Encoding (Acceptance Rate: 23%) |
| DAC 2024 (Top Conf. in Design Automation) | Fangxin Liu=, Ning Yang=, Haomin Li, Zongwu Wang, Zhuoran Song, Songwen Pei, Li Jiang EOS: An Energy-Oriented Attack Framework for Spiking Neural Networks (Acceptance Rate: 23%) |
| DATE 2024 (Top Conf. in Design Automation) | Jiahao Sun, Fangxin Liu=, Yijian Zhang, Li Jiang and Rui Yang RTSA: An RRAM-TCAM based In-Memory-Search Accelerator for Sub-100 μs Collision Detection (Acceptance Rate: 24%) |
| ASPLOS 2024 (Top Conf. in Computer Architecture) | Zhuoran Song, Chunyu Qi, Fangxin Liu=, Naifeng Jing, Xiaoyao Liang CMC: Video Transformer Acceleration via CODEC Assisted Matrix Condensing (Acceptance Rate: 24%) |
| HPCA 2024 (Top Conf. in Computer Architecture) | Fangxin Liu=, Ning Yang=, Haomin Li, Zongwu Wang, Zhuoran Song, Songwen Pei, Li Jiang SPARK: Scalable and Precision-Aware Acceleration of Neural Networks via Efficient Encoding (Acceptance Rate: 18%) |
| ASPDAC 2024 (Top Conf. in Design Automation) | Fangxin Liu=, Haomin Li=, Ning Yang, Yichi Chen, Zongwu Wang, Tao Yang, Li Jiang PAAP-HD: PIM-Assisted Approximation for Efficient Hyper-Dimensional Computing (Acceptance Rate: 29%) |
| ASPDAC 2024 (Top Conf. in Design Automation) | Fangxin Liu=, Haomin Li=, Ning Yang, Zongwu Wang, Tao Yang, Li Jiang TEAS: Exploiting Spiking Activity for Temporal-wise Adaptive Spiking Neural Networks (Acceptance Rate: 29%) |
| ASPDAC 2024 (Top Conf. in Design Automation) | Shiyuan Huang=, Fangxin Liu=, Tian Li, Zongwu Wang, Haomin Li, Li Jiang TSTC: Enabling Efficient Training via Structured Sparse Tensor Compilation (Acceptance Rate: 29%) |
| ASPDAC 2024 (Top Conf. in Design Automation) | Haomin Li=, Fangxin Liu=, Yichi Chen, Li Jiang HyperFeel: An Efficient Federated Learning Framework Using Hyperdimensional Computing (Acceptance Rate: 29%) |
| ICCD 2023 | Fangxin Liu=, Ning Yang=, Li Jiang PSQ: An Automatic Search Framework for Data-Free Quantization on PIM-based Architecture (Acceptance Rate: 28%) |
| ICCAD 2023 (Top Conf. in Design Automation) | Haomin Li=, Fangxin Liu=, Yichi Chen, Li Jiang HyperNode: An Efficient Node Classification Framework Using HyperDimensional Computing (Acceptance Rate: 23%) |
| IEEE TC 2023 (Top Journal in Computer Architecture) | Fangxin Liu, Wenbo Zhao, Zongwu Wang, Yongbiao Chen, Xiaoyao Liang, Li Jiang ERA-BS: Boosting the Efficiency of ReRAM-based PIM Accelerator with Fine-Grained Bit-Level Sparsity (CCF Tier A) |
| DAC 2024 (Top Conf. in Design Automation) | Fangxin Liu=, Haomin Li=, Zongwu Wang, Yongbiao Chen, Li Jiang HyperAttack: An Efficient Attack Framework for HyperDimensional Computing (Acceptance Rate: 23%) |
| ICCD 2022 | Fangxin Liu, Zongwu Wang, Yongbiao Chen, Li Jiang Randomize and Match: Exploiting Irregular Sparsity for Energy Efficient Processing in SNNs (Acceptance Rate: 24%) |
| IEEE TCAD 2022 (Top Journal in Computer-Aided Design) | Fangxin Liu, Zongwu Wang, Yongbiao Chen, Zhezhi He, Tao Yang, Xiaoyao Liang, Li Jiang SoBS-X: Squeeze-Out Bit Sparsity for ReRAM-Crossbar-Based Neural Network Accelerator (CCF Tier A) |
| SIGIR 2022 (Top Conf. in Information Retrieval) | Fangxin Liu, Haomin Li, Xiaokang Yang, Li Jiang L3E-HD: A Framework Enabling Efficient Ensemble in High-Dimensional Space for Language Tasks (Acceptance Rate: 24%) |
| IEEE TCAD 2022 (Top Journal in Computer-Aided Design) | Fangxin Liu=, Wenbo Zhao, Zongwu Wang, Yilong Zhao, Tao Yang, Yiran Chen, Li Jiang IVQ: In-Memory Acceleration of DNN Inference Exploiting Varied Quantization (CCF Tier A) |
| DAC 2022 (Top Conf. in Design Automation) | Fangxin Liu, Wenbo Zhao, Zongwu Wang, Yongbiao Chen, Zhezhi He, Naifeng Jing, Xiaoyao Liang, Li Jiang EBSP: Evolving Bit Sparsity Patterns for Hardware Friendly Inference of Quantized Deep Neural Networks (Acceptance Rate: 24.7%) |
| DAC 2022 (Top Conf. in Design Automation) | Fangxin Liu=, Wenbo Zhao, Yongbiao Chen, Zongwu Wang, Zhezhi He, Rui Yang, Qidong Tang, Tao Yang, Cheng Zhuo PIM-DH: ReRAM based Processing in Memory Architecture for Deep Hashing Acceleration (Acceptance Rate: 24.7%) |
| DAC 2022 (Top Conf. in Design Automation) | Fangxin Liu, Wenbo Zhao, Zongwu Wang, Yongbiao Chen, Tao Yang, Zhezhi He, Xiaokang Yang, Li Jiang SATO: Spiking Neural Network Acceleration via Temporal Oriented Dataflow and Architecture (Acceptance Rate: 24.7%) |
| ICASSP 2022 (Top Conf. in Signal Processing) | Fangxin Liu=, Wenbo Zhao, Yongbiao Chen, Zongwu Wang, Fei Dai Dynsnn: A dynamic approach to reduce redundancy in spiking neural networks (CCF Tier B) |
| AAAI'22 (Oral) (Top Conf. in Artificial Intelligence) | Fangxin Liu, Wenbo Zhao*, Yongbiao Chen, Zongwu Wang, Li Jiang SpikeConverter: An Efficient Conversion Framework Zipping the Gap between Artificial Neural Networks and Spiking Neural Networks (Acceptance Rate: 15%) |
| Frontiers in Neuroscience, 2021 (SCI Tier 2) | Fangxin Liu=, Wenbo Zhao=, Yongbiao Chen, Zongwu Wang, Tao Yang, Li Jiang SSTDP: Supervised Spike Timing Dependent Plasticity for Efficient Spiking Neural Network Training (Impact Factor: 4.7) |
| ICCD 2021 | Fangxin Liu, Wenbo Zhao, Zhezhi He, Zongwu Wang, Yilong Zhao, Tao Yang, Jingnai Feng, Xiaoyao Liang, Li Jiang SME: ReRAM-based Sparse-Multiplication-Engine to Squeeze-Out Bit Sparsity of Neural Network (Acceptance Rate: 24.4%) |
| ICCV 2021 (Top Conf. in Computer Vision) | Fangxin Liu=, Wenbo Zhao, Zhezhi He, Yanzhi Wang, Zongwu Wang, Changzhi Dai, Xiaoyao Liang, Li Jiang Improving Neural Network Efficiency via Post-training Quantization with Adaptive Floating-Point (Acceptance Rate: 25.9%) |
| ICCAD 2021 (Top Conf. in Design Automation) | Fangxin Liu, Wenbo Zhao, Zhezhi He, Zongwu Wang, Yilong Zhao, Yongbiao Chen, Li Jiang Bit-Transformer: Transforming Bit-level Sparsity into Higher Preformance in ReRAM-based Accelerator (Acceptance Rate: 23.5%) |
| GLSVLSI 2021 | Fangxin Liu=, Wenbo Zhao, Zongwu Wang, Tao Yang, Li Jiang IM3A: Boosting Deep Neural Network Efficiency via In-Memory Addressing-Assisted Acceleration (Acceptance Rate: 24%) |
| ICMR 2022 | Yongbiao Chen, Fangxin Liu, et al. TransHash: Transformer-based Hamming Hashing for Efficient Image Retrieval |
| ICMR 2022 | Yongbiao Chen, Fangxin Liu, et al. Supervised Contrastive Vehicle Quantization for Efficient Vehicle Retrieval |
| DATE 2022 (Top Conf. in Design Automation) | Zongwu Wang, Fangxin Liu, et al. Self-Terminated Write of Multi-Level Cell ReRAM for Efficient Neuromorphic Computing (Best Paper Award) |
| DATE 2022 (Top Conf. in Design Automation) | Tao Yang, Fangxin Liu, et al. DTQAtten: Leveraging Dynamic Token-based Quantization for Efficient Attention Architecture (Nominated for Best Paper) |
| ASPDAC 2022 (Top Conf. in Design Automation) | Qidong Tang, Fangxin Liu, et al. HAWIS: Hardware-Aware Automated WIdth Search for Accurate, Energy-Efficient and Robust Binary Neural Network on ReRAM Dot-Product Engine |
| DAC 2022 (Top Conf. in Design Automation) | Tao Yang, Fangxin Liu, et al. PIMGCN: A ReRAM-based PIM Design for Graph Convolutional Network Acceleration |