| 2026 | AAAI | SpecQuant: Spectral Decomposition and Adaptive Truncation for Ultra-Low-Bit LLMs Quantization. | Zhixiong Zhao, Fangxin Liu, Junjie Wang, Chenyang Guan, Zongwu Wang, Li Jiang, Haibing Guan |
| 2026 | ASPDAC | TFLOP: Towards Energy-Efficient LLM Inference An FPGA-Affinity Accelerator with Unified LUT-based OPtimization. | Zongwu Wang, Zhongyi Tang, Fangxin Liu, Chenyang Guan, Li Jiang, Haibing Guan |
| 2026 | ASPDAC | BLADE: Boosting LLM Decoding's Communication Efficiency in DRAM-based PIM. | Yilong Zhao, Fangxin Liu, Zongwu Wang, Mingjian Li, Mingxing Zhang, Chixiao Chen, Li Jiang |
| 2026 | ASPLOS | EARTH: An Efficient MoE Accelerator with Entropy-Aware Speculative Prefetch and Result Reuse. | Fangxin Liu, Ning Yang, Jingkui Yang, Zongwu Wang, Chenyang Guan, Yu Feng, Li Jiang, Haibing Guan |
| 2026 | DATE | LaMoS: Enabling Efficient Large Number Modular Multiplication through SRAM-based CiM Acceleration. | Haomin Li, Fangxin Liu, Chenyang Guan, Zongwu Wang, Li Jiang, Haibing Guan |
| 2026 | HPCA | ORANGE: Exploring Ockham's Razor for Neural Rendering by Accelerating 3DGS on NPUs with GEMM-Friendly Blending and Balanced Workloads. | Haomin Li, Yun Liang, Fangxin Liu, Bowen Zhu, Zongwu Wang, Yu Feng, Liqiang Lu, Li Jiang, Haibing Guan |
| 2026 | ISCA | STEP: Adaptive Spatio-Temporal Expert Prefetching for Low-Latency and Memory-Efficient MoE Inference. | Fangxin Liu, Ning Yang, Zongwu Wang, Chenyang Guan, Haomin Li, Yu Feng, Liqiang Lu, Xiang Li, Siran Yang, Jiamang Wang, Lin Qu, Li Jiang, Haibing Guan |
| 2026 | ISCA | Harmonia: A Unified Hierarchical Scheduling Framework for Sparse Matrix Multiplication. | Jingkui Yang, Fangxin Liu, Xin Ju, Ning Yang, Chenyang Guan, Junjie Wang, Zongwu Wang, Mei Wen, Jian Liu, Li Jiang, Haibing Guan |
| 2025 | ASPDAC | NeuronQuant: Accurate and Efficient Post-Training Quantization for Spiking Neural Networks. | Haomin Li, Fangxin Liu, Zewen Sun, Zongwu Wang, Shiyuan Huang, Ning Yang, Li Jiang |
| 2025 | ASPDAC | Exploiting Differential-Based Data Encoding for Enhanced Query Efficiency. | Fangxin Liu, Zongwu Wang, Peng Xu, Shiyuan Huang, Li Jiang |
| 2025 | ASPLOS | ASDR: Exploiting Adaptive Sampling and Data Reuse for CIM-based Instant Neural Rendering. | Fangxin Liu, Haomin Li, Bowen Zhu, Zongwu Wang, Zhuoran Song, Haibing Guan, Li Jiang |
| 2025 | DAC | ALLMod: Exploring Area-Efficiency of LUT-based Large Number Modular Reduction via Hybrid Workloads. | Fangxin Liu, Haomin Li, Zongwu Wang, Bo Zhang, Mingzhe Zhang, Shoumeng Yan, Li Jiang, Haibing Guan |
| 2025 | DAC | BLOOM: Bit-Slice Framework for DNN Acceleration with Mixed-Precision. | Fangxin Liu, Ning Yang, Zongwu Wang, Xuanpeng Zhu, Haidong Yao, Xiankui Xiong, Li Jiang, Haibing Guan |
| 2025 | DAC | MILLION: MasterIng Long-Context LLM Inference Via Outlier-Immunized KV Product QuaNtization. | Zongwu Wang, Peng Xu, Fangxin Liu, Yiwei Hu, Qingxiao Sun, Gezi Li, Cheng Li, Xuan Wang, Li Jiang, Haibing Guan |
| 2025 | DAC | PISA: Efficient Precision-Slice Framework for LLMs with Adaptive Numerical Type. | Ning Yang, Zongwu Wang, Qingxiao Sun, Liqiang Lu, Fangxin Liu |
| 2025 | DATE | TAIL: Exploiting Temporal Asynchronous Execution for Efficient Spiking Neural Networks with Inter-Layer Parallelism. | Haomin Li, Fangxin Liu, Zongwu Wang, Dongxu Lyu, Shiyuan Huang, Ning Yang, Qi Sun, Zhuoran Song, Li Jiang |
| 2025 | DATE | HyperDyn: Dynamic Dimensional Masking for Efficient Hyper-Dimensional Computing. | Fangxin Liu, Haomin Li, Zongwu Wang, Dongxu Lyu, Li Jiang |
| 2025 | DATE | OPS: Outlier-Aware Precision-Slice Framework for LLM Acceleration. | Fangxin Liu, Ning Yang, Zongwu Wang, Xuanpeng Zhu, Haidong Yao, Xiankui Xiong, Qi Sun, Li Jiang |
| 2025 | DATE | EVASION: Efficient KV CAche CompreSsion vIa PrOduct QuaNtization. | Zongwu Wang, Fangxin Liu, Peng Xu, Qingxiao Sun, Junping Zhao, Li Jiang |
| 2025 | EMNLP | FlexQuant: A Flexible and Efficient Dynamic Precision Switching Framework for LLM Quantization. | Fangxin Liu, Zongwu Wang, JinHong Xia, Junping Zhao, Shouren Zhao, Jinjin Li, Jian Liu, Li Jiang, Haibing Guan |
| 2025 | HPCA | CROSS: Compiler-Driven Optimization of Sparse DNNs Using Sparse/Dense Computation Kernels. | Fangxin Liu, Shiyuan Huang, Ning Yang, Zongwu Wang, Haomin Li, Li Jiang |
| 2025 | ICCAD | PLAIN: Leveraging High Internal Bandwidth in PIM for Accelerating Large Language Model Inference via Mixed-Precision Quantization. | Yiwei Hu, Fangxin Liu, Zongwu Wang, Yilong Zhao, Tao Yang, Li Jiang, Haibing Guan |
| 2025 | ICCAD | QUARK: Quantization-Enabled Circuit Sharing for Transformer Acceleration by Exploiting Common Patterns in Nonlinear Operations. | Zhixiong Zhao, Haomin Li, Fangxin Liu, Yuncheng Lu, Zongwu Wang, Tao Yang, Li Jiang, Haibing Guan |
| 2025 | ISCA | FATE: Boosting the Performance of Hyper-Dimensional Computing Intelligence with Flexible Numerical DAta TypE. | Haomin Li, Fangxin Liu, Yichi Chen, Zongwu Wang, Shiyuan Huang, Ning Yang, Dongxu Lyu, Li Jiang |
| 2024 | ASPDAC | TSTC: Enabling Efficient Training via Structured Sparse Tensor Compilation. | Shiyuan Huang, Fangxin Liu, Tian Li, Zongwu Wang, Haomin Li, Li Jiang |
| 2024 | ASPDAC | PAAP-HD: PIM-Assisted Approximation for Efficient Hyper-Dimensional Computing. | Fangxin Liu, Haomin Li, Ning Yang, Yichi Chen, Zongwu Wang, Tao Yang, Li Jiang |
| 2024 | ASPDAC | TEAS: Exploiting Spiking Activity for Temporal-wise Adaptive Spiking Neural Networks. | Fangxin Liu, Haomin Li, Ning Yang, Zongwu Wang, Tao Yang, Li Jiang |
| 2024 | DAC | INSPIRE: Accelerating Deep Neural Networks via Hardware-friendly Index-Pair Encoding. | Fangxin Liu, Ning Yang, Zhiyan Song, Zongwu Wang, Haomin Li, Shiyuan Huang, Zhuoran Song, Songwen Pei, Li Jiang |
| 2024 | DAC | EOS: An Energy-Oriented Attack Framework for Spiking Neural Networks. | Ning Yang, Fangxin Liu, Zongwu Wang, Haomin Li, Zhuoran Song, Songwen Pei, Li Jiang |
| 2024 | HPCA | SPARK: Scalable and Precision-Aware Acceleration of Neural Networks via Efficient Encoding. | Fangxin Liu, Ning Yang, Haomin Li, Zongwu Wang, Zhuoran Song, Songwen Pei, Li Jiang |
| 2024 | ICCD | HOLES: Boosting Large Language Models Efficiency with Hardware-Friendly Lossless Encoding. | Fangxin Liu, Ning Yang, Zhiyan Song, Zongwu Wang, Li Jiang |
| 2024 | ICCD | PS4: A Low Power SNN Accelerator with Spike Speculative Scheme. | Zongwu Wang, Fangxin Liu, Xin Tang, Li Jiang |
| 2024 | ICCD | T-BUS: Taming Bipartite Unstructured Sparsity for Energy-Efficient DNN Acceleration. | Ning Yang, Fangxin Liu, Zongwu Wang, Zhiyan Song, Tao Yang, Li Jiang |
| 2024 | ICCD | Ninja: A Hardware Assisted System for Accelerating Nested Address Translation. | Longyu Zhao, Zongwu Wang, Fangxin Liu, Li Jiang |
| 2024 | ISCA | UM-PIM: DRAM-based PIM with Uniform & Shared Memory Space. | Yilong Zhao, Mingyu Gao, Fangxin Liu, Yiwei Hu, Zongwu Wang, Han Lin, Jin Li, He Xian, Hanlin Dong, Tao Yang, Naifeng Jing, Xiaoyao Liang, Li Jiang |
| 2024 | ISLPED | LowPASS: A Low power PIM-based accelerator with Speculative Scheme for SNNs. | Fangxin Liu, Shiyuan Huang, Longyu Zhao, Li Jiang, Zongwu Wang |
| 2024 | MICRO | COMPASS: SRAM-Based Computing-in-Memory SNN Accelerator with Adaptive Spike Speculation. | Zongwu Wang, Fangxin Liu, Ning Yang, Shiyuan Huang, Haomin Li, Li Jiang |
| 2023 | DATE | SIMSnn: A Weight-Agnostic ReRAM-based Search-In-Memory Engine for SNN Acceleration. | Fangxin Liu, Wenbo Zhao, Zongwu Wang, Xiaokang 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 | ASPDAC | HAWIS: Hardware-Aware Automated WIdth Search for Accurate, Energy-Efficient and Robust Binary Neural Network on ReRAM Dot-Product Engine. | Qidong Tang, Zhezhi He, Fangxin Liu, Zongwu Wang, Yiyuan Zhou, Yinghuan Zhang, 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 | DATE | Self-Terminating Write of Multi-Level Cell ReRAM for Efficient Neuromorphic Computing. | Zongwu Wang, Zhezhi He, Rui Yang, Shiquan Fan, Jie Lin, Fangxin Liu, Yueyang Jia, Chenxi Yuan, Qidong Tang, Li Jiang |
| 2022 | DATE | DTQAtten: Leveraging Dynamic Token-based Quantization for Efficient Attention Architecture. | Tao Yang, Dongyue Li, Zhuoran Song, Yilong Zhao, Fangxin Liu, Zongwu Wang, Zhezhi He, 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 | ICCD | SME: ReRAM-based Sparse-Multiplication-Engine to Squeeze-Out Bit Sparsity of Neural Network. | Fangxin Liu, Wenbo Zhao, Zhezhi He, Zongwu Wang, Yilong Zhao, Tao Yang, Jingnai Feng, Xiaoyao Liang, Li Jiang |
| 2021 | ICCV | Improving Neural Network Efficiency via Post-training Quantization with Adaptive Floating-Point. | Fangxin Liu, Wenbo Zhao, Zhezhi He, Yanzhi Wang, Zongwu Wang, Changzhi Dai, Xiaoyao Liang, Li Jiang |