Flexible multistandard FEC processor design with ASIP methodology. - Zhenzhi Wu; Dake Liu (ASAP, 2014)

FPGA implementation of a multi-algorithm parallel FEC for SDR platforms. - Zhenzhi Wu; Dake Liu; Zheng Yang; Qingying Wang; Wei Zhou (FPL, 2014)

Matrix reordering techniques for memory conflict reduction for pipelined QC-LDPC decoder. - Zhenzhi Wu; Dake Liu; Yanjun Zhang (ICCC, 2014)

Memory sharing techniques for multi-standard high-throughput FEC decoder. - Zhenzhi Wu; Dake Liu (ICSAMOS, 2014)

A conflict-free access method for parallel turbo decoder. - Chen Gong 003; Xiaoyang Li; Zhenzhi Wu; Dake Liu (WCSP, 2014)

Benefit and cost of cross sliding window scheduling for low latency 5G Turbo decoding. - Dake Liu; Chaoyuan Zuo; Zhenzhi Wu (ICCC, 2015)

A contention-free memory subsystem for 5G Turbo decoder with flexible degree of parallelism. - Zhenzhi Wu; Dake Liu; Xiaoyang Li; Chen Gong 003 (SiPS, 2015)

Loop acceleration and instruction repeat support for application specific instruction-set processors. - Zhenzhi Wu; Dake Liu; Xiaoyang Li (SoCC, 2015)

High-Throughput Trellis Processor for Multistandard FEC Decoding. - Zhenzhi Wu; Dake Liu (IEEE Trans. Very Large Scale Integr. Syst., 2015)

High-Accuracy Compressed Sensing Decoder Based on Adaptive (ℓ0, ℓ1) Complex Approximate Message Passing: Cross-layer Design. - Le Zheng; Zhenzhi Wu; Mingoo Seok; Xiaodong Wang 001; Quanhua Liu 002 (IEEE Trans. Circuits Syst. I Regul. Pap., 2016)

Gated XNOR Networks: Deep Neural Networks with Ternary Weights and Activations under a Unified Discretization Framework. - Lei Deng 003; Peng Jiao; Jing Pei; Zhenzhi Wu; Guoqi Li (CoRR, 2017)

Computational Complexity Analysis of FEC Decoding on SDR Platforms. - Zhenzhi Wu; Chen Gong 003; Dake Liu (J. Signal Process. Syst., 2017)

GXNOR-Net: Training deep neural networks with ternary weights and activations without full-precision memory under a unified discretization framework. - Lei Deng 003; Peng Jiao; Jing Pei; Zhenzhi Wu; Guoqi Li (Neural Networks, 2018)

Safe-Net: Solid and Abstract Feature Extraction Network for Pedestrian Attribute Recognition. - Daiheng Gao; Zhenzhi Wu; Weihao Zhang (ICIP, 2019)

ARLIF: A Flexible and Efficient Recurrent Neuronal Model for Sequential Tasks. - Daiheng Gao; Zhenzhi Wu; Yujie Wu 002; Guoqi Li; Jing Pei (HBAI@IJCAI, 2020)

Accurate and Efficient LIF-Nets for 3D Detection and Recognition. - Yueting Shi; Hai Li; Hehui Zhang; Zhenzhi Wu; Shiwei Ren (IEEE Access, 2020)

Tianjic: A Unified and Scalable Chip Bridging Spike-Based and Continuous Neural Computation. - Lei Deng 003; Guanrui Wang; Guoqi Li; Shuangchen Li; Ling Liang; Maohua Zhu; Yujie Wu 002; Zheyu Yang; Zhe Zou; Jing Pei; Zhenzhi Wu; Xing Hu 001; et al. (IEEE J. Solid State Circuits, 2020)

Hybrid neural state machine for neural network. - Lei Tian 004; Zhenzhi Wu; Shuang Wu; Luping Shi (Sci. China Inf. Sci., 2021)

Learnable Heterogeneous Convolution: Learning both topology and strength. - Rongzhen Zhao; Zhenzhi Wu; Qikun Zhang (Neural Networks, 2021)

Convolution of Convolution: Let Kernels Spatially Collaborate. - Rongzhen Zhao; Jian Li; Zhenzhi Wu (CVPR, 2022)

Modeling learnable electrical synapse for high precision spatio-temporal recognition. - Zhenzhi Wu; Zhihong Zhang; Huanhuan Gao; Jun Qin; Rongzhen Zhao; Guangshe Zhao; Guoqi Li (Neural Networks, 2022)

LIAF-Net: Leaky Integrate and Analog Fire Network for Lightweight and Efficient Spatiotemporal Information Processing. - Zhenzhi Wu; Hehui Zhang; Yihan Lin; Guoqi Li; Meng Wang; Ye Tang (IEEE Trans. Neural Networks Learn. Syst., 2022)