Quantitative Prediction of 3-D Multiple Parameters of Tectonic Fractures in Tight Sandstone Reservoirs Based on Geomechanical Method. - Jianwei Feng; Junsheng Dai; Jinming Lu; Xizhe Li (IEEE Access, 2018)

A Low-Latency and Low-Complexity Hardware Architecture for CTC Beam Search Decoding. - Siyuan Lu 002; Jinming Lu; Jun Lin 001; Zhongfeng Wang 001; Li Du (SiPS, 2019)

Training Deep Neural Networks Using Posit Number System. - Jinming Lu; Siyuan Lu 002; Zhisheng Wang; Chao Fang; Jun Lin 001; Zhongfeng Wang 001; Li Du (SoCC, 2019)

A Hardware-Oriented and Memory-Efficient Method for CTC Decoding. - Siyuan Lu 002; Jinming Lu; Jun Lin 001; Zhongfeng Wang 001 (IEEE Access, 2019)

E-LSTM: An Efficient Hardware Architecture for Long Short-Term Memory. - Meiqi Wang; Zhisheng Wang; Jinming Lu; Jun Lin 001; Zhongfeng Wang 001 (IEEE J. Emerg. Sel. Topics Circuits Syst., 2019)

LBFP: Logarithmic Block Floating Point Arithmetic for Deep Neural Networks. - Chao Ni 005; Jinming Lu; Jun Lin 001; Zhongfeng Wang 001 (APCCAS, 2020)

A Reconfigurable DNN Training Accelerator on FPGA. - Jinming Lu; Jun Lin 001; Zhongfeng Wang 001 (SiPS, 2020)

An FPGA-Based Reconfigurable Accelerator for Low-Bit DNN Training. - Haikuo Shao; Jinming Lu; Jun Lin 001; Zhongfeng Wang 001 (ISVLSI, 2021)

A Reconfigurable Accelerator for Generative Adversarial Network Training Based on FPGA. - Tongtong Yin; Wendong Mao; Jinming Lu; Zhongfeng Wang 001 (ISVLSI, 2021)

Evaluations on Deep Neural Networks Training Using Posit Number System. - Jinming Lu; Chao Fang; Mingyang Xu; Jun Lin 001; Zhongfeng Wang 001 (IEEE Trans. Computers, 2021)

An Efficient CNN Training Accelerator Leveraging Transposable Block Sparsity. - Mingyang Xu; Jinming Lu; Zhongfeng Wang 001; Jun Lin 001 (AICAS, 2022)

An Efficient Hardware Architecture for DNN Training by Exploiting Triple Sparsity. - Jian Huang; Jinming Lu; Zhongfeng Wang 001 (ISCAS, 2022)

THETA: A High-Efficiency Training Accelerator for DNNs With Triple-Side Sparsity Exploration. - Jinming Lu; Jian Huang; Zhongfeng Wang 001 (IEEE Trans. Very Large Scale Integr. Syst., 2022)

Data Analysis for Machine Sound Detection: Challenges, Methods, and Future Trends. - Mingming Zhang; Liu Jie; Jinming Lu (CISP-BMEI, 2023)

An FPGA-Based Reconfigurable CNN Training Accelerator Using Decomposable Winograd. - Hui Wang 083; Jinming Lu; Jun Lin 001; Zhongfeng Wang 001 (ISVLSI, 2023)

Cathode Shape Design for Steady-State Electrochemical Machining. - Jinming Lu; Ewald A. Werner (Algorithms, 2023)

ETA: An Efficient Training Accelerator for DNNs Based on Hardware-Algorithm Co-Optimization. - Jinming Lu; Chao Ni 005; Zhongfeng Wang 001 (IEEE Trans. Neural Networks Learn. Syst., 2023)

An Efficient Training Accelerator for Transformers With Hardware-Algorithm Co-Optimization. - Haikuo Shao; Jinming Lu; Meiqi Wang; Zhongfeng Wang 001 (IEEE Trans. Very Large Scale Integr. Syst., 2023)

WinTA: An Efficient Reconfigurable CNN Training Accelerator With Decomposition Winograd. - Jinming Lu; Hui Wang 083; Jun Lin 001; Zhongfeng Wang 001 (IEEE Trans. Circuits Syst. I Regul. Pap., 2024)

CDM-QTA: Quantized Training Acceleration for Efficient LoRA Fine-Tuning of Diffusion Model. - Jinming Lu; Minghao She; Wendong Mao; Zhongfeng Wang 001 (ISCAS, 2025)

Ultra Memory-Efficient On-FPGA Training of Transformers via Tensor-Compressed Optimization. - Jiayi Tian; Jinming Lu; Hai Li 008; Xiangwei Wang; Cong Hao; Ian A. Young; Zheng Zhang 005 (CoRR, 2025)

FETTA: Flexible and Efficient Hardware Accelerator for Tensorized Neural Network Training. - Jinming Lu; Jiayi Tian; Hai Li 008; Ian A. Young; Zheng Zhang 005 (CoRR, 2025)

FLAT-LLM: Fine-grained Low-rank Activation Space Transformation for Large Language Model Compression. - Jiayi Tian; Ryan Solgi; Jinming Lu; Yifan Yang; Hai Li 008; Zheng Zhang 005 (CoRR, 2025)