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

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)

Co-Designing Binarized Transformer and Hardware Accelerator for Efficient End-to-End Edge Deployment. - Yuhao Ji; Chao Fang; Shaobo Ma; Haikuo Shao; Zhongfeng Wang 001 (ICCAD, 2024)

A Flexible FPGA-Based Accelerator for Efficient Inference of Multi-Precision CNNs. - Xinyan Liu; Xiao Wu; Haikuo Shao; Zhongfeng Wang 001 (ISCAS, 2024)

An FPGA-Based Reconfigurable Accelerator for Convolution-Transformer Hybrid EfficientViT. - Haikuo Shao; Huihong Shi; Wendong Mao; Zhongfeng Wang 001 (ISCAS, 2024)

A Low Complexity Online Learning Approximate Message Passing Detector for Massive MIMO. - Baoling Hong; Haikuo Shao; Zhongfeng Wang 001 (IEEE Trans. Very Large Scale Integr. Syst., 2024)

Efficient Arbitrary Precision Acceleration for Large Language Models on GPU Tensor Cores. - Shaobo Ma; Chao Fang; Haikuo Shao; Zhongfeng Wang 001 (ASP-DAC, 2025)

An Efficient Training Architecture for Nonlinear Softmax Function in Transformers. - Haikuo Shao; Zhongfeng Wang 001 (ISCAS, 2025)

Trio-ViT: Post-Training Quantization and Acceleration for Softmax-Free Efficient Vision Transformer. - Huihong Shi; Haikuo Shao; Wendong Mao; Zhongfeng Wang 001 (IEEE Trans. Circuits Syst. I Regul. Pap., 2025)

AccLLM: Accelerating Long-Context LLM Inference Via Algorithm-Hardware Co-Design. - Yanbiao Liang; Huihong Shi; Haikuo Shao; Zhongfeng Wang 001 (CoRR, 2025)

FastMamba: A High-Speed and Efficient Mamba Accelerator on FPGA with Accurate Quantization. - Aotao Wang; Haikuo Shao; Shaobo Ma; Zhongfeng Wang 001 (CoRR, 2025)

ASTRA: Reconfigurable Training Architecture Design for Nonlinear Softmax and Activation Functions in Transformers. - Haikuo Shao; Zhongfeng Wang 001 (IEEE Trans. Very Large Scale Integr. Syst., 2025)