IM3A: Boosting Deep Neural Network Efficiency via In-Memory Addressing-Assisted Acceleration. - Fangxin Liu; Wenbo Zhao 005; Zongwu Wang; Tao Yang 031; Li Jiang 002 (ACM Great Lakes Symposium on VLSI, 2021)

Bit-Transformer: Transforming Bit-level Sparsity into Higher Preformance in ReRAM-based Accelerator. - Fangxin Liu; Wenbo Zhao 005; Zhezhi He; Zongwu Wang; Yilong Zhao; Yongbiao Chen; Li Jiang 002 (ICCAD, 2021)

SME: ReRAM-based Sparse-Multiplication-Engine to Squeeze-Out Bit Sparsity of Neural Network. - Fangxin Liu; Wenbo Zhao 005; Zhezhi He; Zongwu Wang; Yilong Zhao; Tao Yang 031; Jingnai Feng; Xiaoyao Liang; Li Jiang 002 (ICCD, 2021)

Improving Neural Network Efficiency via Post-training Quantization with Adaptive Floating-Point. - Fangxin Liu; Wenbo Zhao 005; Zhezhi He; Yanzhi Wang; Zongwu Wang; Changzhi Dai; Xiaoyao Liang; Li Jiang 002 (ICCV, 2021)

SME: ReRAM-based Sparse-Multiplication-Engine to Squeeze-Out Bit Sparsity of Neural Network. - Fangxin Liu; Wenbo Zhao 005; Yilong Zhao; Zongwu Wang; Tao Yang 031; Zhezhi He; Naifeng Jing; Xiaoyao Liang; Li Jiang 002 (CoRR, 2021)

SpikeConverter: An Efficient Conversion Framework Zipping the Gap between Artificial Neural Networks and Spiking Neural Networks. - Fangxin Liu; Wenbo Zhao 005; Yongbiao Chen; Zongwu Wang; Li Jiang 002 (AAAI, 2022)

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 002 (ASP-DAC, 2022)

PIM-DH: ReRAM-based processing-in-memory architecture for deep hashing acceleration. - Fangxin Liu; Wenbo Zhao 005; Yongbiao Chen; Zongwu Wang; Zhezhi He; Rui Yang; Qidong Tang; Tao Yang 031; Cheng Zhuo; Li Jiang 002 (DAC, 2022)

EBSP: evolving bit sparsity patterns for hardware-friendly inference of quantized deep neural networks. - Fangxin Liu; Wenbo Zhao 005; Zongwu Wang; Yongbiao Chen; Zhezhi He; Naifeng Jing; Xiaoyao Liang; Li Jiang 002 (DAC, 2022)

SATO: spiking neural network acceleration via temporal-oriented dataflow and architecture. - Fangxin Liu; Wenbo Zhao 005; Zongwu Wang; Yongbiao Chen; Tao Yang 031; Zhezhi He; Xiaokang Yang 001; Li Jiang 002 (DAC, 2022)

Self-Terminating Write of Multi-Level Cell ReRAM for Efficient Neuromorphic Computing. - Zongwu Wang; Zhezhi He; Rui Yang; Shiquan Fan; Jie Lin 004; Fangxin Liu; Yueyang Jia; Chenxi Yuan; Qidong Tang; Li Jiang 002 (DATE, 2022)

DTQAtten: Leveraging Dynamic Token-based Quantization for Efficient Attention Architecture. - Tao Yang 031; Dongyue Li; Zhuoran Song; Yilong Zhao; Fangxin Liu; Zongwu Wang; Zhezhi He; Li Jiang 002 (DATE, 2022)

DynSNN: A Dynamic Approach to Reduce Redundancy in Spiking Neural Networks. - Fangxin Liu; Wenbo Zhao 005; Yongbiao Chen; Zongwu Wang; Fei Dai 008 (ICASSP, 2022)

Randomize and Match: Exploiting Irregular Sparsity for Energy Efficient Processing in SNNs. - Fangxin Liu; Zongwu Wang; Wenbo Zhao 005; Yongbiao Chen; Tao Yang 031; Xiaokang Yang 001; Li Jiang 002 (ICCD, 2022)

Cross-layer Designs against Non-ideal Effects in ReRAM-based Processing-in-Memory System. - Chen Nie; Zongwu Wang; Qidong Tang; Chenyang Lv; Li Jiang 002; Zhezhi He (ISQED, 2022)

IVQ: In-Memory Acceleration of DNN Inference Exploiting Varied Quantization. - Fangxin Liu; Wenbo Zhao 005; Zongwu Wang; Yilong Zhao; Tao Yang 031; Yiran Chen 001; Li Jiang 002 (IEEE Trans. Comput. Aided Des. Integr. Circuits Syst., 2022)

SIMSnn: A Weight-Agnostic ReRAM-based Search-In-Memory Engine for SNN Acceleration. - Fangxin Liu; Wenbo Zhao 005; Zongwu Wang; Xiaokang Yang 001; Li Jiang 002 (DATE, 2023)

SoBS-X: Squeeze-Out Bit Sparsity for ReRAM-Crossbar-Based Neural Network Accelerator. - Fangxin Liu; Zongwu Wang; Yongbiao Chen; Zhezhi He; Tao Yang 031; Xiaoyao Liang; Li Jiang 002 (IEEE Trans. Comput. Aided Des. Integr. Circuits Syst., 2023)

TSTC: Enabling Efficient Training via Structured Sparse Tensor Compilation. - Shiyuan Huang; Fangxin Liu; Tian Li; Zongwu Wang; Haomin Li 002; Li Jiang 002 (ASPDAC, 2024)

PAAP-HD: PIM-Assisted Approximation for Efficient Hyper-Dimensional Computing. - Fangxin Liu; Haomin Li 002; Ning Yang; Yichi Chen; Zongwu Wang; Tao Yang 031; Li Jiang 002 (ASPDAC, 2024)

TEAS: Exploiting Spiking Activity for Temporal-wise Adaptive Spiking Neural Networks. - Fangxin Liu; Haomin Li 002; Ning Yang; Zongwu Wang; Tao Yang 031; Li Jiang 002 (ASPDAC, 2024)

INSPIRE: Accelerating Deep Neural Networks via Hardware-friendly Index-Pair Encoding. - Fangxin Liu; Ning Yang; Zhiyan Song; Zongwu Wang; Haomin Li 002; Shiyuan Huang; Zhuoran Song; Songwen Pei; Li Jiang 002 (DAC, 2024)

EOS: An Energy-Oriented Attack Framework for Spiking Neural Networks. - Ning Yang; Fangxin Liu; Zongwu Wang; Haomin Li 002; Zhuoran Song; Songwen Pei; Li Jiang 002 (DAC, 2024)

SPARK: Scalable and Precision-Aware Acceleration of Neural Networks via Efficient Encoding. - Fangxin Liu; Ning Yang; Haomin Li 002; Zongwu Wang; Zhuoran Song; Songwen Pei; Li Jiang 002 (HPCA, 2024)

HOLES: Boosting Large Language Models Efficiency with Hardware-Friendly Lossless Encoding. - Fangxin Liu; Ning Yang; Zhiyan Song; Zongwu Wang; Li Jiang 002 (ICCD, 2024)

PS4: A Low Power SNN Accelerator with Spike Speculative Scheme. - Zongwu Wang; Fangxin Liu; Xin Tang; Li Jiang 002 (ICCD, 2024)

T-BUS: Taming Bipartite Unstructured Sparsity for Energy-Efficient DNN Acceleration. - Ning Yang; Fangxin Liu; Zongwu Wang; Zhiyan Song; Tao Yang 031; Li Jiang 002 (ICCD, 2024)

Ninja: A Hardware Assisted System for Accelerating Nested Address Translation. - Longyu Zhao; Zongwu Wang; Fangxin Liu; Li Jiang 002 (ICCD, 2024)

UM-PIM: DRAM-based PIM with Uniform & Shared Memory Space. - Yilong Zhao; Mingyu Gao 001; Fangxin Liu; Yiwei Hu; Zongwu Wang; Han Lin; Jin Li 002; He Xian; Hanlin Dong; Tao Yang 031; Naifeng Jing; Xiaoyao Liang; et al. (ISCA, 2024)

LowPASS: A Low power PIM-based accelerator with Speculative Scheme for SNNs. - Fangxin Liu; Shiyuan Huang; Longyu Zhao; Li Jiang 002; Zongwu Wang (ISLPED, 2024)

COMPASS: SRAM-Based Computing-in-Memory SNN Accelerator with Adaptive Spike Speculation. - Zongwu Wang; Fangxin Liu; Ning Yang; Shiyuan Huang; Haomin Li 002; Li Jiang 002 (MICRO, 2024)

TokenRing: An Efficient Parallelism Framework for Infinite-Context LLMs via Bidirectional Communication. - Zongwu Wang; Fangxin Liu; Mingshuai Li; Li Jiang 002 (CoRR, 2024)

ERA-BS: Boosting the Efficiency of ReRAM-Based PIM Accelerator With Fine-Grained Bit-Level Sparsity. - Fangxin Liu; Wenbo Zhao 005; Zongwu Wang; Yongbiao Chen; Xiaoyao Liang; Li Jiang 002 (IEEE Trans. Computers, 2024)

NeuronQuant: Accurate and Efficient Post-Training Quantization for Spiking Neural Networks. - Haomin Li 002; Fangxin Liu; Zewen Sun; Zongwu Wang; Shiyuan Huang; Ning Yang; Li Jiang 002 (ASP-DAC, 2025)

Exploiting Differential-Based Data Encoding for Enhanced Query Efficiency. - Fangxin Liu; Zongwu Wang; Peng Xu; Shiyuan Huang; Li Jiang 002 (ASP-DAC, 2025)

TAIL: Exploiting Temporal Asynchronous Execution for Efficient Spiking Neural Networks with Inter-Layer Parallelism. - Haomin Li 002; Fangxin Liu; Zongwu Wang; Dongxu Lyu; Shiyuan Huang; Ning Yang; Qi Sun; Zhuoran Song; Li Jiang 002 (DATE, 2025)

HyperDyn: Dynamic Dimensional Masking for Efficient Hyper-Dimensional Computing. - Fangxin Liu; Haomin Li 002; Zongwu Wang; Dongxu Lyu; Li Jiang 002 (DATE, 2025)

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 002 (DATE, 2025)

EVASION: Efficient KV CAche CompreSsion vIa PrOduct QuaNtization. - Zongwu Wang; Fangxin Liu; Peng Xu; Qingxiao Sun; Junping Zhao; Li Jiang 002 (DATE, 2025)

CROSS: Compiler-Driven Optimization of Sparse DNNs Using Sparse/Dense Computation Kernels. - Fangxin Liu; Shiyuan Huang; Ning Yang; Zongwu Wang; Haomin Li 002; Li Jiang 002 (HPCA, 2025)

FATE: Boosting the Performance of Hyper-Dimensional Computing Intelligence with Flexible Numerical DAta TypE. - Haomin Li 002; Fangxin Liu; Yichi Chen; Zongwu Wang; Shiyuan Huang; Ning Yang; Dongxu Lyu; Li Jiang 002 (ISCA, 2025)

STCO: Enhancing Training Efficiency via Structured Sparse Tensor Compilation Optimization. - Shiyuan Huang; Fangxin Liu; Tian Li; Zongwu Wang; Ning Yang; Haomin Li 002; Li Jiang 002 (ACM Trans. Design Autom. Electr. Syst., 2025)

SpMMPlu-Pro: An Enhanced Compiler Plug-In for Efficient SpMM and Sparsity Propagation Algorithm. - Shiyuan Huang; Fangxin Liu; Tao Yang 031; Zongwu Wang; Ning Yang; Li Jiang 002 (IEEE Trans. Comput. Aided Des. Integr. Circuits Syst., 2025)

ALLMod: Exploring Area-Efficiency of LUT-based Large Number Modular Reduction via Hybrid Workloads. - Fangxin Liu; Haomin Li 002; Zongwu Wang; Bo Zhang; Mingzhe Zhang; Shoumeng Yan; Li Jiang 002; Haibing Guan (CoRR, 2025)

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 002; Haibing Guan (CoRR, 2025)

FlexQuant: A Flexible and Efficient Dynamic Precision Switching Framework for LLM Quantization. - Fangxin Liu; Zongwu Wang; JinHong Xia; Junping Zhao; Jian Liu; Haibing Guan; Li Jiang 002 (CoRR, 2025)

ASDR: Exploiting Adaptive Sampling and Data Reuse for CIM-based Instant Neural Rendering. - Fangxin Liu; Haomin Li 002; Bowen Zhu; Zongwu Wang; Zhuoran Song; Habing Guan; Li Jiang 002 (CoRR, 2025)