2024-01-至今，西安交通大学，人工智能学院，助理教授

2018-09-2023-12，西安交通大学，电子科学与技术，博士

2015-09-2018-07，西安交通大学，软件工程(集成电路方向)，硕士

2011-09-2015-07，哈尔滨工业大学，电子科学与技术，学士

1. Jianing Chen, Wenlong Ma, Yunchuan Li, Wei Ren, Baoting Li, Hang Wang, Xuchong Zhang, Hongbin Sun. ME-MoD: Algorithm-Hardware-Dataflow Co-Design for Memory Efficient Mixture-of-Depth-Based Vision Transformer Accelerator[J]. IEEE Transactions on Very Large Scale Integration (VLSI) Systems, vol. 34, no. 9, pp. 2832-2845, 2026.

2.  Peng Yu, Xuchong Zhang, Baoting Li, Hongbin Sun, et al. Learn, Practice, and Review: A Cognitive-Inspired Single-Stage Framework for Efficient Domain Adaptive Object Detection[J]. IEEE Transactions on Instrumentation and Measurement, 2026. Early Access, pp. 1-1. 

3.  Chenwei Jia, Baoting Li, Xuchong Zhang, Mingzhuo Wei, Bochen Lin, Hongbin Sun. Quant Experts: Token-aware Adaptive Error Reconstruction with Mixture of Experts for Large Vision-Language Models Quantization[C]. Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR), 2026, pp. 24716-24726.

4. Xudong Zheng, Baoting Li, Tai Yu, Hang Wang, Xuchong Zhang, Hongbin Sun. A Low-Error Approximate Logarithmic Multiplier with Symmetric LUT for Efficient DNN Training[C]. 2026 IEEE International Symposium on Circuits and Systems (ISCAS), 2026, pp. 1899-1903.

5. Baoting Li, Danqing Zhang, Pengfei Zhao, et al., DQ-STP: An Efficient Sparse On-device Training Processor based on Low-rank Decomposition and Quantization for DNN[J]. IEEE Transactions on Circuits and Systems - I Regular Papers (TCAS-I), 2024. Accepted. 

6. Danqing Zhang, Baoting Li, Hang Wang, et al., An Efficient Sparse-Aware Summation Optimization Strategy for DNN Accelerator[C]. IEEE International Symposium on Circuits and Systems (ISCAS), 2024. Accepted. 

7. 汪航, 李宝婷, 张旭翀等, 深度神经网络在线训练硬件加速器的数据量化综述[J]. 微电子学与计算机. 2023.04.

8. Baoting Li, Hang Wang, Fujie Luo, et al. ACBN: Approximate Calculated Batch Normalization for Efficient DNN On-device Training Processor[J]. IEEE Transactions on Very Large Scale Integration Systems (TVLSI), vol. 31, no. 6, pp. 738-748, June 2023.

9. Baoting Li, Hang Wang, Xuchong Zhang, et al. Dynamic Dataflow Scheduling and Computation Mapping Techniques for Efficient Depthwise Separable Convolution Acceleration [J]. IEEE Transactions on Circuits and Systems - I Regular Papers (TCAS-I), vol. 68, no. 8, pp. 3279-3292, Aug. 2021.

10. Shaofei Yang, Longjun Liu, Baoting Li, et al. Exploiting Variable Precision Computation Array for Scalable Neural Network Accelerators[C]. 2020 2nd IEEE International Conference on Artificial Intelligence Circuits and Systems (AICAS), 2020, pp. 315-319.

11. Baoting Li, Longjun Liu, Yanming Jin, et al. Designing Efficient Shortcut Architecture for Improving the Accuracy of Fully Quantized Neural Networks Accelerator[C]. 2020 25th Asia and South Pacific Design Automation Conference (ASP-DAC), 2020, pp. 289-294.

12. Baoting Li, Longjun Liu, Jiahua Liang, et al. Exploring Resource-Aware Deep Neural Network Accelerator and Architecture Design[C]. 2018 IEEE 23rd International Conference on Digital Signal Processing (DSP), 2018, pp. 1-5.