AUSN: Approximately Uniform Quantization by Adaptively Superimposing Non-uniform Distribution for Deep Neural Networks. - Fangxin Liu; Wenbo Zhao 005; Yanzhi Wang; Changzhi Dai; Li Jiang 002 (CoRR, 2020)

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)

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)

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)

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)

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)