[1] Ying Wang, Huawei Li, Xiaowei Li,"A Case of On-chip memory Sub-system Design for Low-Power Machine Learning Accelerators,” to appear in IEEE Transactions on Computer-Aided Design of Integrated Circuits and Systems, 2017.

[2] Ying Wang, Jiachao Deng, Yuntan Fang, Huawei Li, Xiaowei Li, “Resilience-Aware Frequency Tuning for Neural-Network based Approximate Computing Chips,” IEEE Transactions on Very Large Scaled Integration Systems (TVLSI), 2015.

[3] Ying Wang, Cheng Wang, Yinhe Han, Huawei Li, Xiaowei Li,"Retention-Aware DRAM Assembly and Repair for Future FGR Memories,” IEEE Transactions on Computer-Aided Design of Integrated Circuits and Systems (TCAD)，2017.

[4] Ying Wang , Huawei Li, Dawen Xu, Xiaowei Li, “Real-Time meets Approximate Computing: An Elastic Deep Learning Accelerator Design with Adaptive Trade-off between QoS and QoR,” in IEEE/ACM Proceedings of Design, Automation Conference, 2017.

[5] Long Cheng, Ying Wang , Yulong Pei, and Dick Epema, “A Coflow-based Co-optimization Framework for High-performance Data Analytics,” in IEEE/ACM Proceedings of 46th International Conference on Parallel Processing, 2017.

[6]Xiandong Zhao, Ying Wang,Xuyi Cai, Cheng Liu, Lei Zhang,“Linear Symmetric Quantization of Neural Networks for Low-precision Integer Hardware,”5th International Conference on Learning Representations (ICLR), 2019.

专利：

[1]应用于二值权重卷积网络的处理系统及方法

[2]一种用于神经网络处理器的方法

[3]一种用于深度神经网络的压缩装置

[4]一种非易失性计算装置及其工作方法

[5]用于神经网络处理器的浮点乘法器及浮点数乘法