Research Topic

  • Hardware acceleration of neural network algorithms
    Deep learning is now widely used in a variety of domains, and brain-inspired neural computing like neural network (NN) is considered to be one of the most powerful applications of deep learning, which requires high computing capability and energy efficiency. My research focuses on using hardware accelerator, including FPGA, GPU, and emerging devices, to boost the speed and energy efficiency of NNs.

  • Published 34 papers and 2 patents. Cited by more than 800 in Google Scholar. H-index is 14, and H10-index is 18. Lixue Xia received the Bast Paper Nominations in DAC 2017 and ITC 2018.


Publications

    Journal Articles

  • [J7] Yi Cai, Yujun Lin, Lixue Xia, Xiaoming Chen, Song Han, Yu Wang, Huazhong Yang, Long Live TIME: Improving Lifetime and Security for NVM-based Training-In-Memory Systems , to appear in IEEE Transactions on Computer-Aided Design of Integrated Circuits and Systems (TCAD), 2020.
  • [J6] Yi Cai, Tianqi Tang, Lixue Xia, Boxun Li, Yu Wang, Huazhong Yang, Low Bit-width Convolutional Neural Network on RRAM , in IEEE Transactions on Computer-Aided Design of Integrated Circuits and Systems (TCAD), 2019.
  • [J5] Ming Cheng, Lixue Xia, Zhenhua Zhu, Yi Cai, Yuan Xie, Yu Wang, Huazhong Yang, TIME: A Training-in-memory Architecture for RRAM-based Deep Neural Networks, in IEEE Transactions on Computer-Aided Design of Integrated Circuits and Systems, vol.38, No.5, 2019.
  • [J4] Lixue Xia, Mengyun Liu, Xuefei Ning, Krishnendu Chakrabarty, Yu Wang, Fault-Tolerant Training Enabled by On-Line Fault Detection for RRAM-Based Neural Computing System, in IEEE Transactions on Computer-Aided Design of Integrated Circuits and Systems (TCAD), vol.38, No.9, 2019.
  • [J3] Lixue Xia, Boxun Li, Tianqi Tang, Peng Gu, Pai-yu Chen, Shimeng Yu, Yu Cao, Yu Wang, Yuan Xie, Huazhong Yang, MNSIM: Simulation Platform for Memristor-based Neuromorphic Computing System, in IEEE Transactions on Computer-Aided Design of Integrated Circuits and Systems (TCAD), vol.37, No.5, 2018, pp.1009-1022.
  • [J2] Lixue Xia, Wenqin Huangfu, Tianqi Tang, Xiling Yin, Krishnendu Chakrabarty, Yuan Xie, Yu Wang, Huazhong Yang, Stuck-at Fault Tolerance in RRAM Computing Systems , in IEEE Journal on Emerging and Selected Topics in Circuits and Systems (JETCAS), vol.8, No.1, 2018, pp.102-115.
  • [J1] Lixue Xia, Peng Gu, Boxun Li, Tianqi Tang, Xiling Yin, Wenqin Huangfu, Shimeng Yu, Yu Cao, Yu Wang, Huazhong Yang, Technological Exploration of RRAM Crossbar Array for Matrix-Vector Multiplication, in Journal of Computer Science and Technology (JCST), vol.31, No.1, 2016, pp.3-19.


    • Conference Papers

  • [C27] Jiansong Zhang, Lixue Xia, Zhao Jiang, Hao Liang, Jiaoyan Chen, Shouda Liu, Wei Lin, Yuan Xie, Ouroboros: An Inference Engine for Deep Learning Based TTS on Embedded Devices, in IEEE Hot Chips 31 Symposium (HCS), 2019.
  • [C26] Lixue Xia, Lansong Diao, Zhao Jiang, Hao Liang, Kai Chen, Li Ding, Shunli Dou, Zibin Su, Meng Sun, Jiansong Zhang, Wei Lin, PAI-FCNN: FPGA based inference system for complex CNN models, in IEEE International Conference on Application-specific Systems, Architectures and Processors (ASAP), 2019. (invited)
  • [C25] Zhenhua Zhu, Yujun Lin, Hanbo Sun, Guohao Dai, Lixue Xia, Yu Wang, Huazhong Yang, A Configurable Multi-Precision CNN Accelerator Based on Single Bit RRAM, in ACM/EDAC/IEEE Design Automation Conference (DAC),2019.
  • [C24] Mengyun Liu, Lixue Xia, Yu Wang, Krishnendu Chakrabarty, \Fault Tolerance in Neuromorphic Computing Systems", in Asia and South Pacific Design Automation Conference (ASP-DAC), 2019.
  • [C23] Yi Cai, Tianqi Tang, Lixue Xia, Ming Cheng, Zhenhua Zhu, Yu Wang, Huazhong Yang, Training Low Bitwidth Convolutional Neural Networks on RRAM, in Asia and South Pacific Design Automation Conference (ASP-DAC), 2018, pp.117-122.
  • [C22] Yi Cai, Yujun Lin, Lixue Xia, Xiaoming Chen, Song Han, Yu Wang, Huazhong Yang, Long Live TIME: Improving Lifetime for Training-In-Memory Engines by Structured Gradient Sparsification , in Design Automation Conference (DAC), 2018.
  • [C21] Keni Qiu, Weiwen Chen, Yuanchao Xu, Lixue Xia, Yu Wang, Zili Shao, A Low Power Design Enabled by a Peripheral Circuit Reuse Structure integrated with a Retimed Data Flow for RRAM Crossbar-based Convolutional Neural Network, in Design, Automation & Test in Europe Conference & Exhibition (DATE), 2018.
  • [C20] Jilan Lin, Lixue Xia, Zhenhua Zhu, Hanbo Sun, Yi Cai, Hui Gao, Ming Cheng, Xiaoming Chen, Yu Wang, Huazhong Yang, Rescuing Memristor-based Computing with Non-linear Resistance Levels , in Design, Automation & Test in Europe Conference & Exhibition (DATE), 2018, pp.407-412.
  • [C19] Mengyun Liu, Lixue Xia, Yu Wang, Krishnendu Chakrabarty, Design of Fault-Tolerant Neuromorphic Computing Systems , in European Test Symposium, 2018.
  • [C18] Zhenhua Zhu, Jilan Lin, Ming Cheng, Lixue Xia, Hanbo Sun, Xiaoming Chen, Yu Wang, Huazhong Yang, Mixed Size Crossbar based RRAM CNN Accelerator with Overlapped Mapping Method , in IEEE/ACM International Conference on Computer-Aided Design (ICCAD), 2018.
  • [C17] Mengyun Liu, Lixue Xia, Yu Wang, Krishnendu Chakrabarty, Fault Tolerance for RRAM-Based Matrix Operations , in International Test Conference (ITC), 2018. (Best Paper Candidate)
  • [C16] Yuanhui Ni, Keni Qiu, Weiwen Chen, Lixue Xia, Yu Wang, Low Power Driven and Multi-CLP aware Loop Tiling for RRAM Crossbar-based CNN , in ACM/SIGAPP Symposium On Applied Computing (SAC), 2018.
  • [C15] Wenqin Huangfu, Lixue Xia, Ming Cheng, Xilin Yin, Tianqi Tang, Boxun Li, Krishnendu Chakrabarty, Yuan Xie, Yu Wang, Huazhong Yang, Computation-Oriented Fault-Tolerance Schemes for RRAM Computing Systems , in Asia and South Pacific Design Automation Conference (ASP-DAC), 2017, pp.794-799.
  • [C14] Tianqi Tang, Lixue Xia, Boxun Li, Yu Wang, Huazhong Yang, Binary Convolutional Neural Network on RRAM, in Asia and South Pacific Design Automation Conference (ASP-DAC), 2017, pp.782-787.
  • [C13] Lixue Xia, Mengyun Liu, Xuefei Ning, Krishnendu Chakrabarty, Yu Wang, Fault-Tolerant Training with On-Line Fault Detection for RRAM-Based Neural Computing Systems, in Design Automation Conference (DAC), 2017. (Best Paper Candidate)
  • [C12] Ming Cheng, Lixue Xia, Zhenhua Zhu, Yi Cai, Yuan Xie, Yu Wang, Huazhong Yang, TIME:A Training-in-memory Architecture for Memristor-based Deep Neural Network, in Design Automation Conference (DAC), 2017, pp.26:1-26:6.
  • [C11] Fang Su, Wei-Hao Chen, Lixue Xia, Chieh-Pu Lo, Tianqi Tang, Zhibo Wang, Kuo-Hsiang Hsu, Ming Cheng, Jun-Yi Li, Yuan Xie, Yu Wang, Meng-Fan Chang, Huazhong Yang, Yongpan Liu, A 462GOPs/J RRAM-Based Nonvolatile Intelligent Processor for Energy Harvesting IoE System Featuring Nonvolatile Logics and Processing-In-Memory, in IEEE Symposium on VLSI Circuits (VLSIC), 2017.
  • [C10] Yu Wang, Lixue Xia, Ming Cheng, Tianqi Tang, Boxun Li, Huazhong Yang, RRAM Based Learning Acceleration, in Compliers, Architectures, and Sythesis of Embedded Systems (CASES), 2016, pp.1-2. (invited)
  • [C9] Lixue Xia, Tianqi Tang, Wenqin Huangfu, Ming Cheng, Xiling Yin, Boxun Li, Yu Wang, Huazhong Yang, Switched by Input: Power Efficient Structure for RRAM-based Convolutional Neural Network, in Design Automation Conference (DAC), 2016, pp.125:1-125:6.
  • [C8] Lixue Xia, Boxun Li, Tianqi Tang, Peng Gu, Xiling Yin, Wenqin Huangfu, Pai-Yu Chen, Shimeng Yu, Yu Cao, Yu Wang, Yuan Xie, Huazhong Yang, MNSIM: Simulation Platform for Memristor-based Neuromorphic Computing System, in Design, Automation & Test in Europe Conference & Exhibition (DATE), 2016, pp.469-474.
  • [C7] Xiaoming Chen, Lixue Xia, Yu Wang, Huazhong Yang, Sparsity-Oriented Sparse Solver Design for Circuit Simulation, in DATE, 2016, pp.1580-1585.
  • [C6] Yu Wang, Lixue Xia, Tianqi Tang, Boxun Li, Song Yao, Ming Cheng, Huazhong Yang, Low Power Convolutional Neural Networks on a Chip , in ISCAS, 2016, pp.129-132.
  • [C5] Lixue Xia, Rong Luo, Bin Zhao, Yu Wang, Huazhong Yang, An Accurate and Low Cost PM2.5 Estimation Method Based on Artificial Neural Network , in Asia and South Pacific Design Automation Conference (ASP-DAC), 2015, pp.190-195.
  • [C4] Boxun Li, Lixue Xia, Peng Gu, Yu Wang, Huazhong Yang, Merging the interface: Power, area and accuracy co-optimization for RRAM crossbar-based mixed-signal, in Design Automation Conference (DAC), 2015, pp.13:1-13:6.
  • [C3] Tianqi Tang, Lixue Xia, Boxun Li, Rong Luo, Yu Wang, Yiran Chen, Huangzhong Yang, Spiking Neural Network with RRAM : Can We Use it for Real-World Application?, in Design, Automation & Test in Europe Conference & Exhibition (DATE), 2015, pp.860-865.
  • [C2] Yu Wang, Tianqi Tang, Lixue Xia, Boxun Li, Peng Gu, Hai Li, Yuan Xie, Huazhong Yang, Energy Efficient RRAM Spiking Neural Network for Real Time Classification, in Great Lakes Symposium on VLSI (GLSVLSI), 2015, pp.189-194.
  • [C1] Shimeng Yu, Pai-Yu Chen, Yu Cao, Lixue Xia, Yu Wang, Huaqiang Wu, Scaling-up Resistive Synaptic Arrays for Neuro-inspired Architecture: Challenges and Prospect, in IEEE International Electron Devices Meeting (IEDM), 2015, pp.451-454.

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