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2020 – today
- 2025
[j8]Haomin Li
, Fangxin Liu, Zongwu Wang, Ning Yang, Shiyuan Huang, Xiaoyao Liang, Haibing Guan, Li Jiang:
Attack and Defense: Enhancing Robustness of Binary Hyper-Dimensional Computing. ACM Trans. Archit. Code Optim. 22(3): 85:1-85:25 (2025)- [j7]Shiyuan Huang, Fangxin Liu, Tao Yang, Zongwu Wang, Ning Yang, Li Jiang:
SpMMPlu-Pro: An Enhanced Compiler Plug-In for Efficient SpMM and Sparsity Propagation Algorithm. IEEE Trans. Comput. Aided Des. Integr. Circuits Syst. 44(2): 669-683 (2025) - [j6]Shiyuan Huang, Fangxin Liu, Tian Li, Zongwu Wang, Ning Yang, Haomin Li, Li Jiang:
STCO: Enhancing Training Efficiency via Structured Sparse Tensor Compilation Optimization. ACM Trans. Design Autom. Electr. Syst. 30(1): 1-22 (2025) - [c46]Fangxin Liu, Zongwu Wang, Ning Yang, Haomin Li, Tao Yang, Haibing Guan, Li Jiang:
Irregular Sparsity-Enabled Search-in-Memory Engine for Accelerating Spiking Neural Networks. APPT 2025: 99-109 - [c45]Fangxin Liu, Zongwu Wang, Peng Xu, Shiyuan Huang, Li Jiang:
Exploiting Differential-Based Data Encoding for Enhanced Query Efficiency. ASP-DAC 2025: 594-600 - [c44]Haomin Li, Fangxin Liu, Zewen Sun, Zongwu Wang, Shiyuan Huang, Ning Yang, Li Jiang:
NeuronQuant: Accurate and Efficient Post-Training Quantization for Spiking Neural Networks. ASP-DAC 2025: 734-740 - [c43]Fangxin Liu, Haomin Li, Bowen Zhu, Zongwu Wang, Zhuoran Song, Haibing Guan, Li Jiang:
ASDR: Exploiting Adaptive Sampling and Data Reuse for CIM-based Instant Neural Rendering. ASPLOS (3) 2025: 18-33 - [c42]Fangxin Liu, Haomin Li, Zongwu Wang, Bo Zhang, Mingzhe Zhang, Shoumeng Yan, Li Jiang, Haibing Guan:
ALLMod: Exploring Area-Efficiency of LUT-based Large Number Modular Reduction via Hybrid Workloads. DAC 2025: 1-7 - [c41]Fangxin Liu, Ning Yang, Zongwu Wang, Xuanpeng Zhu, Haidong Yao, Xiankui Xiong, Li Jiang, Haibing Guan:
BLOOM: Bit-Slice Framework for DNN Acceleration with Mixed-Precision. DAC 2025: 1-7 - [c40]Zongwu Wang, Peng Xu, Fangxin Liu, Yiwei Hu, Qingxiao Sun, Gezi Li, Cheng Li, Xuan Wang, Li Jiang, Haibing Guan:
MILLION: MasterIng Long-Context LLM Inference Via Outlier-Immunized KV Product QuaNtization. DAC 2025: 1-7 - [c39]Ning Yang, Zongwu Wang, Qingxiao Sun, Liqiang Lu, Fangxin Liu:
PISA: Efficient Precision-Slice Framework for LLMs with Adaptive Numerical Type. DAC 2025: 1-7 - [c38]Haomin Li, Fangxin Liu, Zongwu Wang, Dongxu Lyu, Shiyuan Huang, Ning Yang, Qi Sun, Zhuoran Song, Li Jiang:
TAIL: Exploiting Temporal Asynchronous Execution for Efficient Spiking Neural Networks with Inter-Layer Parallelism. DATE 2025: 1-7 - [c37]Fangxin Liu, Haomin Li, Zongwu Wang, Dongxu Lyu, Li Jiang:
HyperDyn: Dynamic Dimensional Masking for Efficient Hyper-Dimensional Computing. DATE 2025: 1-7 - [c36]Fangxin Liu, Ning Yang, Zongwu Wang, Xuanpeng Zhu, Haidong Yao, Xiankui Xiong, Qi Sun, Li Jiang:
OPS: Outlier-Aware Precision-Slice Framework for LLM Acceleration. DATE 2025: 1-2 - [c35]Zongwu Wang, Fangxin Liu, Peng Xu, Qingxiao Sun, Junping Zhao, Li Jiang:
EVASION: Efficient KV CAche CompreSsion vIa PrOduct QuaNtization. DATE 2025: 1-2 - [c34]Fangxin Liu, Zongwu Wang, JinHong Xia, Junping Zhao, Shouren Zhao, Jinjin Li, Jian Liu, Li Jiang, Haibing Guan:
FlexQuant: A Flexible and Efficient Dynamic Precision Switching Framework for LLM Quantization. EMNLP (Findings) 2025: 4152-4161 - [c33]Fangxin Liu, Shiyuan Huang, Ning Yang, Zongwu Wang, Haomin Li, Li Jiang:
CROSS: Compiler-Driven Optimization of Sparse DNNs Using Sparse/Dense Computation Kernels. HPCA 2025: 963-976 - [c32]Yiwei Hu, Fangxin Liu, Zongwu Wang, Yilong Zhao, Tao Yang, Li Jiang, Haibing Guan:
PLAIN: Leveraging High Internal Bandwidth in PIM for Accelerating Large Language Model Inference via Mixed-Precision Quantization. ICCAD 2025: 1-9 - [c31]Zhixiong Zhao, Haomin Li, Fangxin Liu, Yuncheng Lu, Zongwu Wang, Tao Yang, Li Jiang, Haibing Guan:
QUARK: Quantization-Enabled Circuit Sharing for Transformer Acceleration by Exploiting Common Patterns in Nonlinear Operations. ICCAD 2025: 1-9 - [c30]Haomin Li, Fangxin Liu, Yichi Chen, Zongwu Wang, Shiyuan Huang, Ning Yang, Dongxu Lyu, Li Jiang:
FATE: Boosting the Performance of Hyper-Dimensional Computing Intelligence with Flexible Numerical DAta TypE. ISCA 2025: 1269-1282 - [c29]Fangxin Liu, Junjie Wang, Ning Yang, Zongwu Wang, Junping Zhao, Li Jiang, Haibing Guan:
ASTER: Adaptive Dynamic Layer-Skipping for Efficient Transformer Inference via Markov Decision Process. ACM Multimedia 2025: 11853-11861 - [i9]Fangxin Liu, Haomin Li, Zongwu Wang, Bo Zhang, Mingzhe Zhang, Shoumeng Yan, Li Jiang, Haibing Guan:
ALLMod: Exploring Area-Efficiency of LUT-based Large Number Modular Reduction via Hybrid Workloads. CoRR abs/2503.15916 (2025) - [i8]Zongwu Wang, Peng Xu, Fangxin Liu, Yiwei Hu, Qingxiao Sun, Gezi Li, Cheng Li, Xuan Wang, Li Jiang, Haibing Guan:
MILLION: Mastering Long-Context LLM Inference Via Outlier-Immunized KV Product Quantization. CoRR abs/2504.03661 (2025) - [i7]Fangxin Liu, Zongwu Wang, JinHong Xia, Junping Zhao, Jian Liu, Haibing Guan, Li Jiang:
FlexQuant: A Flexible and Efficient Dynamic Precision Switching Framework for LLM Quantization. CoRR abs/2506.12024 (2025) - [i6]Fangxin Liu, Haomin Li, Bowen Zhu, Zongwu Wang, Zhuoran Song, Habing Guan, Li Jiang:
ASDR: Exploiting Adaptive Sampling and Data Reuse for CIM-based Instant Neural Rendering. CoRR abs/2508.02304 (2025) - [i5]Haomin Li, Fangxin Liu, Chenyang Guan, Zongwu Wang, Li Jiang, Haibing Guan:
LaMoS: Enabling Efficient Large Number Modular Multiplication through SRAM-based CiM Acceleration. CoRR abs/2511.03341 (2025) - [i4]Zhixiong Zhao, Haomin Li, Fangxin Liu, Yuncheng Lu, Zongwu Wang, Tao Yang, Li Jiang, Haibing Guan:
QUARK: Quantization-Enabled Circuit Sharing for Transformer Acceleration by Exploiting Common Patterns in Nonlinear Operations. CoRR abs/2511.06767 (2025) - [i3]Zhixiong Zhao, Fangxin Liu, Junjie Wang, Chenyang Guan, Zongwu Wang, Li Jiang, Haibing Guan:
SpecQuant: Spectral Decomposition and Adaptive Truncation for Ultra-Low-Bit LLMs Quantization. CoRR abs/2511.11663 (2025) - 2024
- [j5]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. IEEE Trans. Computers 73(9): 2320-2334 (2024) - [j4]Ning Yang, Fangxin Liu, Zongwu Wang, Junping Zhao, Li Jiang:
SearchQ: Search-Based Fine-Grained Quantization for Data-Free Model Compression. IEEE Trans. Circuits Syst. Artif. Intell. 1(2): 220-228 (2024) - [j3]Fangxin Liu, Zongwu Wang, Wenbo Zhao, Ning Yang, Yongbiao Chen, Shiyuan Huang, Haomin Li, Tao Yang, Songwen Pei, Xiaoyao Liang, Li Jiang:
Exploiting Temporal-Unrolled Parallelism for Energy-Efficient SNN Acceleration. IEEE Trans. Parallel Distributed Syst. 35(10): 1749-1764 (2024) - [c28]Fangxin Liu, Haomin Li, Ning Yang, Yichi Chen, Zongwu Wang, Tao Yang, Li Jiang:
PAAP-HD: PIM-Assisted Approximation for Efficient Hyper-Dimensional Computing. ASPDAC 2024: 46-51 - [c27]Fangxin Liu, Haomin Li, Ning Yang, Zongwu Wang, Tao Yang, Li Jiang:
TEAS: Exploiting Spiking Activity for Temporal-wise Adaptive Spiking Neural Networks. ASPDAC 2024: 842-847 - [c26]Shiyuan Huang, Fangxin Liu, Tian Li, Zongwu Wang, Haomin Li, Li Jiang:
TSTC: Enabling Efficient Training via Structured Sparse Tensor Compilation. ASPDAC 2024: 884-889 - [c25]Fangxin Liu, Ning Yang, Zhiyan Song, Zongwu Wang, Haomin Li, Shiyuan Huang, Zhuoran Song, Songwen Pei, Li Jiang:
INSPIRE: Accelerating Deep Neural Networks via Hardware-friendly Index-Pair Encoding. DAC 2024: 10:1-10:6 - [c24]Ning Yang, Fangxin Liu, Zongwu Wang, Haomin Li, Zhuoran Song, Songwen Pei, Li Jiang:
EOS: An Energy-Oriented Attack Framework for Spiking Neural Networks. DAC 2024: 58:1-58:6 - [c23]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. HPCA 2024: 1029-1042 - [c22]Ning Yang, Fangxin Liu, Zongwu Wang, Zhiyan Song, Tao Yang, Li Jiang:
T-BUS: Taming Bipartite Unstructured Sparsity for Energy-Efficient DNN Acceleration. ICCD 2024: 68-75 - [c21]Zongwu Wang, Fangxin Liu, Xin Tang, Li Jiang:
PS4: A Low Power SNN Accelerator with Spike Speculative Scheme. ICCD 2024: 76-83 - [c20]Fangxin Liu, Ning Yang, Zhiyan Song, Zongwu Wang, Li Jiang:
HOLES: Boosting Large Language Models Efficiency with Hardware-Friendly Lossless Encoding. ICCD 2024: 207-214 - [c19]Longyu Zhao, Zongwu Wang, Fangxin Liu, Li Jiang:
Ninja: A Hardware Assisted System for Accelerating Nested Address Translation. ICCD 2024: 426-433 - [c18]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:
UM-PIM: DRAM-based PIM with Uniform & Shared Memory Space. ISCA 2024: 644-659 - [c17]Fangxin Liu, Shiyuan Huang, Longyu Zhao, Li Jiang, Zongwu Wang:
LowPASS: A Low power PIM-based accelerator with Speculative Scheme for SNNs. ISLPED 2024: 1-6 - [c16]Zongwu Wang, Fangxin Liu, Ning Yang, Shiyuan Huang, Haomin Li, Li Jiang:
COMPASS: SRAM-Based Computing-in-Memory SNN Accelerator with Adaptive Spike Speculation. MICRO 2024: 1090-1106 - [i2]Zongwu Wang, Fangxin Liu, Mingshuai Li, Li Jiang:
TokenRing: An Efficient Parallelism Framework for Infinite-Context LLMs via Bidirectional Communication. CoRR abs/2412.20501 (2024) - 2023
- [j2]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. IEEE Trans. Comput. Aided Des. Integr. Circuits Syst. 42(1): 204-217 (2023) - [c15]Fangxin Liu, Wenbo Zhao, Zongwu Wang, Xiaokang Yang, Li Jiang:
SIMSnn: A Weight-Agnostic ReRAM-based Search-In-Memory Engine for SNN Acceleration. DATE 2023: 1-2 - 2022
- [j1]Fangxin Liu, Wenbo Zhao, Zongwu Wang, Yilong Zhao, Tao Yang, Yiran Chen, Li Jiang:
IVQ: In-Memory Acceleration of DNN Inference Exploiting Varied Quantization. IEEE Trans. Comput. Aided Des. Integr. Circuits Syst. 41(12): 5313-5326 (2022) - [c14]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. AAAI 2022: 1692-1701 - [c13]Qidong Tang, Zhezhi He, Fangxin Liu, Zongwu Wang, Yiyuan Zhou, Yinghuan Zhang, Li Jiang:
HAWIS: Hardware-Aware Automated WIdth Search for Accurate, Energy-Efficient and Robust Binary Neural Network on ReRAM Dot-Product Engine. ASP-DAC 2022: 226-231 - [c12]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. DAC 2022: 259-264 - [c11]Fangxin Liu, Wenbo Zhao, Yongbiao Chen, Zongwu Wang, Zhezhi He, Rui Yang, Qidong Tang, Tao Yang, Cheng Zhuo, Li Jiang:
PIM-DH: ReRAM-based processing-in-memory architecture for deep hashing acceleration. DAC 2022: 1087-1092 - [c10]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. DAC 2022: 1105-1110 - [c9]Tao Yang, Dongyue Li, Zhuoran Song, Yilong Zhao, Fangxin Liu, Zongwu Wang, Zhezhi He, Li Jiang:
DTQAtten: Leveraging Dynamic Token-based Quantization for Efficient Attention Architecture. DATE 2022: 700-705 - [c8]Zongwu Wang, Zhezhi He, Rui Yang, Shiquan Fan, Jie Lin, Fangxin Liu, Yueyang Jia, Chenxi Yuan, Qidong Tang, Li Jiang:
Self-Terminating Write of Multi-Level Cell ReRAM for Efficient Neuromorphic Computing. DATE 2022: 1251-1256 - [c7]Fangxin Liu, Wenbo Zhao, Yongbiao Chen, Zongwu Wang, Fei Dai:
DynSNN: A Dynamic Approach to Reduce Redundancy in Spiking Neural Networks. ICASSP 2022: 2130-2134 - [c6]Fangxin Liu, Zongwu Wang, Wenbo Zhao, Yongbiao Chen, Tao Yang, Xiaokang Yang, Li Jiang:
Randomize and Match: Exploiting Irregular Sparsity for Energy Efficient Processing in SNNs. ICCD 2022: 451-454 - [c5]Chen Nie, Zongwu Wang, Qidong Tang, Chenyang Lv, Li Jiang, Zhezhi He:
Cross-layer Designs against Non-ideal Effects in ReRAM-based Processing-in-Memory System. ISQED 2022: 1-6 - 2021
- [c4]Fangxin Liu, Wenbo Zhao, Zongwu Wang, Tao Yang, Li Jiang:
IM3A: Boosting Deep Neural Network Efficiency via In-Memory Addressing-Assisted Acceleration. ACM Great Lakes Symposium on VLSI 2021: 253-258 - [c3]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. ICCAD 2021: 1-9 - [c2]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. ICCD 2021: 417-424 - [c1]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. ICCV 2021: 5261-5270 - [i1]Fangxin Liu, Wenbo Zhao, Yilong Zhao, Zongwu Wang, Tao Yang, Zhezhi He, Naifeng Jing, Xiaoyao Liang, Li Jiang:
SME: ReRAM-based Sparse-Multiplication-Engine to Squeeze-Out Bit Sparsity of Neural Network. CoRR abs/2103.01705 (2021)
Coauthor Index
[j8] [j7] [j6] [c46] [c45] [c44] [c43] [c42] [c41] [c40] [c38] [c37] [c36] [c35] [c34] [c33] [c32] [c31] [c30] [c29] [i9] [i8] [i7] [i6] [i5] [i4] [i3] [j5] [j4] [j3] [c28] [c27] [c26] [c25] [c24] [c23] [c22] [c21] [c20] [c19] [c18] [c17] [c16] [i2] [j2] [c15] [j1] [c14] [c13] [c12] [c11] [c10] [c9] [c8] [c6] [c5] [c4] [c3] [c2] [c1] [i1]
[j8] [j7] [j6] [c46] [c45] [c44] [c43] [c42] [c41] [c40] [c39] [c38] [c37] [c36] [c35] [c34] [c33] [c32] [c31] [c30] [c29] [i9] [i8] [i7] [i6] [i5] [i4] [i3] [j5] [j4] [j3] [c28] [c27] [c26] [c25] [c24] [c23] [c22] [c21] [c20] [c19] [c18] [c17] [c16] [i2] [j2] [c15] [j1] [c14] [c13] [c12] [c11] [c10] [c9] [c8] [c7] [c6] [c4] [c3] [c2] [c1] [i1]
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