{"id":160,"date":"2022-07-16T23:42:12","date_gmt":"2022-07-16T15:42:12","guid":{"rendered":"https:\/\/www.corticalchip.com\/?p=160"},"modified":"2025-07-03T10:52:59","modified_gmt":"2025-07-03T02:52:59","slug":"%e7%8e%8b%e8%85%be%e9%9c%84%e4%b8%aa%e4%ba%ba%e4%bb%8b%e7%bb%8d","status":"publish","type":"post","link":"https:\/\/www.corticalchip.com\/index.php\/2022\/07\/16\/%e7%8e%8b%e8%85%be%e9%9c%84%e4%b8%aa%e4%ba%ba%e4%bb%8b%e7%bb%8d\/","title":{"rendered":"\u738b\u817e\u9704\u4e2a\u4eba\u7b80\u4ecb"},"content":{"rendered":"\n

                                                                                                              \"\"
                                                                                          \u4e2a\u4eba\u90ae\u7bb1\uff1a20221201012@stu.cqu.edu.cn<\/p>\n\n\n\n

\u6559\u80b2\u80cc\u666f<\/strong>
      \u738b\u817e\u9704\uff0c2019\u5e746\u6708\u548c2022\u5e746\u6708\u6bd5\u4e1a\u4e8e\u91cd\u5e86\u5927\u5b66\u5fae\u7535\u5b50\u4e0e\u901a\u4fe1\u5de5\u7a0b\u5b66\u9662\uff0c\u5206\u522b\u83b7\u5f97\u5de5\u5b66\u5b66\u58eb\u548c\u7855\u58eb\u5b66\u4f4d\uff0c\u4e8e2022\u5e749\u6708\u5728\u91cd\u5e86\u5927\u5b66\u5fae\u7535\u5b50\u4e0e\u901a\u4fe1\u5de5\u7a0b\u5b66\u9662\u653b\u8bfb\u535a\u58eb\u5b66\u4f4d\uff0c2024\u5e746\u6708\u5df2\u8fbe\u5230\u535a\u58eb\u6bd5\u4e1a\u6761\u4ef6\u3002\u5f53\u524d\u4e3b\u8981\u7814\u7a76\u5174\u8da3\u65b9\u5411\u4e3a\u795e\u7ecf\u5f62\u6001\u7c7b\u8111\u7b97\u6cd5\u548c\u6570\u5b57\u82af\u7247\u8bbe\u8ba1\u3002\u5171\u53c2\u4e0e5\u9879\u56fd\u5bb6\u7ea7\u548c\u7701\u90e8\u7ea7\u79d1\u7814\u8bfe\u9898\uff0c\u5176\u4e2d\u4e3b\u78142\u9879\u3002\u5df2\u53d1\u8868\u5b66\u672f\u8bba\u658717\u7bc7\uff0c\u516c\u5f00\u53d1\u660e\u4e13\u522913\u9879\u3002\u5e26\u961f\u83b7\u5f97\u7b2c\u56db\u5c4a\u4e2d\u56fd\u7814\u7a76\u751f\u521b\u201c\u82af\u201d\u5927\u8d5b\u5168\u56fd\u603b\u51b3\u8d5b\u4e8c\u7b49\u5956\u3002<\/p>\n\n\n\n

\u53d1\u8868\u8bba\u6587<\/strong><\/p>\n\n\n\n

    \n
  1. A Visual-Cortex-Mimetic Tiny Neuromorphic Vision Processor Based on Reconfigurable Cortical Neuron Unit<\/a><\/mark>
    \u00a0 \u00a0\u00a0Mingju Chen, Junxian He, Haibing Wang, Tengxiao Wang<\/strong>, Haoran Gao, Liyuan Liu, Ying Wang, Cong Shi*. IEEE Transactions <\/strong><\/em>On Circuits and Systems II: Express Briefs<\/em><\/strong><\/strong>, <\/strong>2025, 72(7), 943-947.\uff08SCI\uff0c1\u533a\uff09<\/strong>

    <\/li>\n\n\n\n
  2. An Edge Neuromorphic Processor With High-Accuracy On-Chip Aggregate-Label Learning<\/a>
        Guanyu Chen, Minju Chen, Tengxiao Wang<\/strong>, Haibing Wang, Xiang Fu, Yingcheng Liu, Liyuan Liu, Cong Shi*. IEEE Transactions <\/strong><\/em>On Circuits and Systems II: Express Briefs<\/em><\/strong>, <\/strong>2025, 72(3), 509-513.\uff08SCI\uff0c2\u533a\uff09<\/strong>

    <\/li>\n\n\n\n
  3. MorphBungee-Lite: An Edge Neuromorphic Architecture With Balanced Cross-Core Workloads Based on Layer-Wise Event-Batch Learning\/Inference<\/a><\/mark>
        Zhengqing Zhong, Haibing Wang, Mingju Chen, Yingcheng Lin, Min Tian, Tengxiao Wang<\/strong>, Liyuan Liu, <\/strong>Cong Shi*. IEEE Transactions On Circuits and Systems Part II: Express Briefs<\/em><\/strong>, 2025, 72(1), 293 – 297\uff08SCI, 2\u533a<\/strong>\uff09

    <\/li>\n\n\n\n
  4. A Visual Cortex-Inspired Edge Neuromorphic Hardware Architecture With On-Chip Multi-Layer STDP Learning<\/a><\/mark>
        Junxian He, Min Tian, Ying Jiang, Haibing Wang, Tengxiao Wang<\/strong>, Xichuan Zhou, Liyuan Liu, Nanjian Wu, Ying Wang, Cong Shi*. Computers and Electrical Engineering<\/em><\/strong>, 2024, 120:109806\uff08SCI, 1\u533a\uff09<\/strong>

    <\/li>\n\n\n\n
  5. MorphBungee: A 65-nm 7.2-mm2<\/sup> 27-\u03bcJ\/image Digital Edge Neuromorphic Chip with On-Chip 802-frame\/s Multi-Layer Spiking Neural Network Learning<\/a><\/mark>
        Tengxiao Wang<\/strong>, Min Tian, Haibing Wang, Zhengqing Zhong, Junxian He, Fang Tang, Xichuan Zhou, Yingcheng Lin, Shuang-Ming Yu, Liyuan Liu, Cong Shi*. IEEE Transactions on Biomedical Circuits and Systems<\/strong><\/em>, <\/strong>2025, 19(1), 209-225. \uff08SCI\uff0c1\u533a\uff09<\/strong>

    <\/li>\n\n\n\n
  6. MorphBungee: A 65nm 7.2mm2<\/sup> 27\u03bcJ\/image Digital Edge Neuromorphic Chip with On-Chip 802 frame\/s Multi-layer Spiking Neural Network Learning<\/a><\/mark>
        Tengxiao Wang<\/strong>, Min Tian, Zhengqing Zhong, Haibing Wang, Junxian He, Fang Tang, Xichuan Zhou, Shuangming Yu, Nanjian Wu, Liyuan Liu, Cong Shi*<\/sup>, 2023 IEEE Asian Solid-State Circuits Conference (ASSCC)<\/em><\/strong>, 2023, 1-3.

    <\/li>\n\n\n\n
  7. Live Demonstration: Face Recognition at The Edge Using Fast On-Chip Deep Learning Neuromorphic Chip<\/a>
        Zhengqing Zhong, Tengxiao Wang<\/strong>, Haibing Wang, Zhihua Zhou, Junxian He, Fang Tang, Xichuan Zhou, Shuangming Yu, Liyuan Liu, Nanjian Wu, Min Tian, Cong Shi*. <\/strong>2023 IEEE 5th International Conference on Artificial Intelligence Circuits and Systems (AICAS)<\/strong><\/em>, 2023, 1-2.

    <\/li>\n\n\n\n
  8. High-Accuracy Deep ANN-to-SNN Conversion Using Quantization-Aware Training Framework and Calcium-Gated Bipolar Leaky Integrate & Fire Neuron<\/a><\/mark>
         Haoran Gao, Junxian He, Haibing Wang, Tengxiao Wang<\/strong>, Zhengqing Zhong, Jianyi Yu, Ying Wang, Min Tian, Cong Shi*. Frontiers in Neuroscience<\/em><\/strong>: 2023, 17, 1141701.\uff08SCI\uff0c2\u533a\uff09<\/strong>

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  9. An Edge Neuromorphic Hardware with Fast On-Chip Error-Triggered Learning on Compressive Sensed Spikes<\/a>
    <\/mark>     Cong Shi, Jingya Zhang, Tengxiao Wang<\/strong>, Zhengqing Zhong, Junxian He, Haoran Gao, Jianyi Yu, Ping Li, Min Tian*. IEEE Transactions on Circuits and Systems II: Express Briefs<\/em><\/strong>: 2023,<\/mark> 70(7), 2665-2669.\uff08SCI\uff0c2\u533a\uff09<\/strong><\/mark>

    <\/li>\n\n\n\n
  10. A Configurable On-Chip Spike Encoding Network Based on Dual-Mode Integrate & Fire Neurons<\/a><\/mark>
         Zhengqing Zhong, Yunpeng Tuo*, Haibing Wang, Tengxiao Wang<\/strong>, Junxian He, Min Tian, Cong Shi. IEEE 6th Information Technology, Networking, Electronic and Automation Control Conference(ITNEC)<\/em><\/strong>: 2023, 1445-1449.

    <\/li>\n\n\n\n
  11. \u7269\u7aef\u795e\u7ecf\u5f62\u6001\u7c7b\u8111\u82af\u7247\u8bbe\u8ba1\u7efc\u8ff0<\/a>
         \u949f\u6b63\u9752, \u738b\u817e\u9704<\/strong>, \u5218\u529b\u6e90, \u5434\u5357\u5065, \u7530\u654f, \u77f3\u5306*. \u5fae\u7eb3\u7535\u5b50\u4e0e\u667a\u80fd\u5236\u9020<\/em><\/strong>: 2022, 4(3), 19-30.

    <\/li>\n\n\n\n
  12. TEDOP: a Tiny Event-Driven Neural Network Hardware Core Enabling On-Chip Spike-Driven Synaptic Plasticity<\/a><\/mark>
         Cong Shi, Sihao Chen, Haibing Wang, Zhengqing Zhong, Ping Li, Junxian He, Tengxiao Wang<\/strong>, Jianyi Yu, Min Tian*. IEEE International Conference on Integrated Circuits, Technologies and Applications (ICTA)<\/em><\/strong>: 2022, 62-63.

    <\/li>\n\n\n\n
  13. MorphBungee: An Edge Neuromorphic Chip for High-Accuracy On-Chip Learning of Multiple-Layer Spiking Neural Networks<\/a><\/mark>
        Tengxiao Wang<\/strong>, Haibing Wang, Junxian He, Zhengqing Zhong, Fang Tang, Xichuan Zhou, Shuang-Ming Yu, Liyuan Liu, Nanjian Wu, Min Tian, Cong Shi*. IEEE Biomedical Circuits and Systems Conference (BioCAS)<\/em><\/strong>: 2022, 255-259.

    <\/li>\n\n\n\n
  14. TripleBrain: A Compact Neuromorphic Hardware Core with Fast On-Chip Self-Organizing and Reinforcement Spike-Timing Dependent Plasticity<\/a>
        Haibing Wang, Zhen He, Tengxiao Wang<\/strong>, Junxian He, Xichuan Zhou, Ying Wang, Liyuan Liu, Nanjian Wu, Min Tian, Cong Shi. IEEE Transactions on Biomedical Circuits and Systems<\/em><\/strong>. 2022, 16(4), 636-650.\uff08SCI\uff0c2\u533a\uff09<\/strong>

    <\/li>\n\n\n\n
  15. A Low-cost FPGA Implementation of Spiking Extreme Learning Machine With On-chip Reward-Modulated STDP Learning<\/a>
        Zhen He, Cong Shi*, Tengxiao Wang<\/strong>, Ying Wang, Min Tian, Xichuan Zhou, Ping Li, Liyuan Liu, Nanjian Wu, Gang Luo. IEEE Transactions on Circuits and Systems II: Express Briefs<\/em><\/strong>: 2022, 69(3), 1657-1661.\uff08SCI\uff0c2\u533a\uff09<\/strong>

    <\/li>\n\n\n\n
  16. TripleBrain: An Edge Neuromorphic Architecture for High-accuracy Single-layer Spiking Neural Network with On-chip Self-organizing and Reinforcement Learning<\/a>
         Haibing Wang, Zhen He, Jinsong Rao, Tengxiao Wang<\/strong>, Junxian He, Min Tian, Xichuan Zhou, Liyuan Liu, Nanjian Wu, Cong Shi*. IEEE International Conference on Integrated Circuits, Technologies and Applications (ICTA)<\/em><\/strong>: 2021, 88-89.

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  17. DeepTempo: a Hardware-Friendly Direct Feedback Alignment Multi-Layer Tempotron Learning Rule for Deep Spiking Neural Networks<\/a>
         Cong Shi*, Tengxiao Wang<\/strong>, Junxian He, Jianghao Zhang, Liyuan Liu, Nanjian Wu. IEEE Transactions on Circuits and Systems II: Express Briefs<\/em><\/strong>: 2021, 68(5), 1581-1585. \uff08SCI\uff0c2\u533a\uff0cinvited paper\uff09<\/strong>

    <\/li>\n\n\n\n
  18. CompSNN: A Lightweight Spiking Neural Network Based on Spatiotemporally Compressive Spike Features<\/a>
         Tengxiao Wang<\/strong>, Cong Shi*, Xichuan Zhou, Yingcheng Lin, Junxian He, Ping Gan, Ping Li, Ying Wang, Liyuan Liu, Nanjian Wu, Gang Luo. Neurocomputing<\/em><\/strong>: 2021, 425(15), 96-106. \uff08SCI\uff0c2\u533a\uff09<\/strong>

    <\/li>\n\n\n\n
  19. A Heterogeneous Spiking Neural Network for Computationally Efficient Face Recognition<\/a>
         Xichuan Zhou, Zhenghua Zhou, Zhengqing Zhong, Jianyi Yu, Tengxiao Wang<\/strong>, Min Tian, Ying Jiang, Cong Shi*. IEEE International Symposium on Circuits & Systems (ISCAS)<\/strong><\/em>: 2021, 1-5.

    <\/li>\n\n\n\n
  20. A High-speed Low-cost VLSI System Capable of On-chip Online Learning for Dynamic Vision Sensor Data Classification<\/a>
         Wei He, Jinguo Huang, Tengxiao Wang<\/strong>, Yingcheng Lin, Junxian He, Xichuan Zhou, Ping Li, Ying Wang, Nanjian Wu, Cong Shi*. Sensors<\/strong>: 2020, 20(17), 4715. \uff08SCI\uff0c3\u533a\uff09<\/strong><\/li>\n<\/ol>\n\n\n\n

    <\/p>\n\n\n\n

    \u53d1\u660e\u4e13\u5229<\/strong><\/p>\n\n\n\n

      \n
    1. \u7f51\u7edc\u7ed3\u6784\u53ef\u914d\u7f6e\u7684\u7c7b\u8111\u82af\u7247\u67b6\u6784.
      \u77f3\u5306*, \u738b\u817e\u9704<\/strong>, \u7530\u654f, \u738b\u6d77\u51b0, \u949f\u6b63\u9752, \u4f55\u4fca\u8d24, \u848b\u9896. \u4e2d\u56fd\uff0c\u516c\u5f00\u53f7\uff1aCN117273100A\uff0c2023-12-22

      <\/li>\n\n\n\n
    2. \u4e8b\u4ef6\u9a71\u52a8\u7c7b\u578b\u82af\u7247\u4e2d\u7a81\u89e6\u6743\u91cd\u66f4\u65b0\u65b9\u6cd5\u3001\u82af\u7247\u3001\u7535\u5b50\u8bbe\u5907.
      \u77f3\u5306*, \u738b\u6d77\u51b0, \u7530\u654f, \u4f55\u4fca\u8d24, \u738b\u817e\u9704<\/strong>, \u55bb\u5251\u4f9d, \u9ad8\u704f\u7136, \u5f20\u9756\u96c5, \u9648\u4e50\u6bc5, \u9648\u601d\u8c6a, \u5eb9\u4e91\u9e4f. \u4e2d\u56fd\uff0c\u516c\u5f00\u53f7\uff1aCN116629331A\uff0c2023-08-22

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    3. \u57fa\u4e8eCa-LIF\u795e\u7ecf\u5143\u6a21\u578b\u7684Spike-BP\u7247\u4e0a\u5b66\u4e60\u65b9\u6cd5\u3001\u7cfb\u7edf\u53ca\u5904\u7406\u5668.
      \u77f3\u5306*, \u9ad8\u704f\u7136, \u7530\u654f, \u4f55\u4fca\u8d24, \u738b\u817e\u9704<\/strong>, \u55bb\u5251\u4f9d, \u738b\u6d77\u51b0, \u9648\u601d\u8c6a, \u5f20\u9756\u96c5, \u5eb9\u4e91\u9e4f, \u9648\u4e50\u6bc5. \u4e2d\u56fd\uff0c\u516c\u5f00\u53f7\uff1aCN116629344A\uff0c2023-08-22

      <\/li>\n\n\n\n
    4. \u57fa\u4e8e\u5168\u8109\u51b2HMAX\u6a21\u578b\u7684\u591a\u5c42\u5377\u79ef\u7c7b\u8111\u82af\u7247.
      \u77f3\u5306*, \u4f55\u4fca\u8d24, \u738b\u6d77\u51b0, \u738b\u817e\u9704<\/strong>, \u5f20\u9756\u96c5, \u9ad8\u704f\u7136. \u4e2d\u56fd\uff0c\u516c\u5f00\u53f7\uff1aCN116562350A\uff0c2023-08-08

      <\/li>\n\n\n\n
    5. \u4e00\u79cd\u57fa\u4e8e\u6db2\u4f53\u72b6\u6001\u673a\u7684\u8109\u51b2\u795e\u7ecf\u7f51\u7edc\u67b6\u6784.
      \u77f3\u5306*, \u9648\u4e50\u6bc5, \u7530\u654f, \u4f55\u4fca\u8d24, \u738b\u817e\u9704<\/strong>, \u738b\u6d77\u51b0, \u55bb\u5251\u4f9d, \u9ad8\u704f\u7136. \u4e2d\u56fd\uff0c\u516c\u5f00\u53f7\uff1aCN116562354A\uff0c2023-08-08

      <\/li>\n\n\n\n
    6. \u6df1\u5c42\u8109\u51b2\u795e\u7ecf\u7f51\u7edc\u6a21\u578b\u53ca\u6df1\u5c42SNN\u7247\u4e0a\u5b9e\u65f6\u5b66\u4e60\u5904\u7406\u5668.
      \u77f3\u5306*\uff0c\u5f20\u9756\u96c5, \u7530\u654f, \u738b\u817e\u9704<\/strong>, \u4f55\u4fca\u8d24, \u55bb\u5251\u4f9d, \u9ad8\u704f\u7136, \u738b\u6d77\u51b0, \u9648\u4e50\u6bc5, \u9648\u601d\u8c6a, \u5eb9\u4e91\u9e4f. \u4e2d\u56fd\uff0c\u516c\u5f00\u53f7\uff1aCN116562344A\uff0c2023-08-08

      <\/li>\n\n\n\n
    7. \u57fa\u4e8e\u53cc\u6a21\u5f0f\u79ef\u5206\u70b9\u706b\u795e\u7ecf\u5143\u7684\u7247\u4e0a\u8109\u51b2\u7f16\u7801\u5668.
      \u77f3\u5306*, \u5eb9\u4e91\u9e4f, \u949f\u6b63\u9752, \u7530\u654f, \u738b\u6d77\u51b0, \u738b\u817e\u9704<\/strong>, \u4f55\u4fca\u8d24, \u9648\u4e50\u6bc5, \u5f20\u9756\u96c5, \u738b\u4e3d, \u9648\u601d\u8c6a, \u9ad8\u704f\u7136. \u4e2d\u56fd\uff0c\u516c\u5f00\u53f7\uff1aCN116050487A\uff0c2023-05-02

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    8. \u8f7b\u91cf\u7ea7\u7247\u4e0a\u5b66\u4e60FPGA\u786c\u4ef6\u67b6\u6784\u53ca\u5176\u8bbe\u8ba1\u65b9\u6cd5.
      \u77f3\u5306*, \u5f20\u9756\u96c5, \u7530\u654f, \u738b\u817e\u9704<\/strong>, \u738b\u6d77\u51b0, \u4f55\u4fca\u8d24, \u5362\u9756, \u9ad8\u704f\u7136. \u4e2d\u56fd\uff0c\u516c\u5f00\u53f7\uff1aCN115115039A\uff0c2022-09-27<\/mark>

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    9. \u4e00\u79cd\u4f4e\u5149\u7167\u5c0f\u50cf\u7d20CFA\u91c7\u6837\u4e0e\u8fb9\u7f18\u8ba1\u7b97\u8bbe\u5907\u9002\u7528\u7684\u53bb\u9a6c\u8d5b\u514b\u65b9\u6cd5.
      \u77f3\u5306*, \u4efb\u9759, \u674e\u777f, \u738b\u6d77\u51b0, \u9ad8\u704f\u7136, \u4f55\u4fca\u8d24, \u738b\u817e\u9704<\/strong>, \u738b\u4e3d. \u4e2d\u56fd\uff0c\u516c\u5f00\u53f7\uff1aCN115082315A\uff0c2022-09-20<\/mark>

      <\/li>\n\n\n\n
    10. \u57fa\u4e8e\u7b80\u5316SDSP\u7b97\u6cd5\u7684\u8f7b\u91cf\u7ea7\u7247\u4e0a\u5b66\u4e60\u65b9\u6cd5\u3001\u7cfb\u7edf\u53ca\u5904\u7406\u5668.
      \u9648\u601d\u8c6a, \u77f3\u5306*, \u7530\u654f, \u4f55\u4fca\u8d24, \u738b\u817e\u9704<\/strong>, \u738b\u6d77\u51b0, \u9ad8\u704f\u7136, \u5eb9\u4e91\u9e4f. \u4e2d\u56fd\uff0c\u516c\u5f00\u53f7\uff1aCN115018058A\uff0c2022-09-06<\/mark>

      <\/li>\n\n\n\n
    11. \u57fa\u4e8e\u5fc6\u963b\u5668\u7684\u53ef\u7247\u4e0a\u5f3a\u5316\u5b66\u4e60\u8109\u51b2GAN\u6a21\u578b\u53ca\u8bbe\u8ba1\u65b9\u6cd5. 
      \u77f3\u5306*, \u5362\u9756, \u7530\u654f, \u738b\u6d77\u51b0, \u55bb\u5251\u4f9d, \u4f55\u4fca\u8d24, \u738b\u817e\u9704<\/strong>, \u9ad8\u704f\u7136. \u4e2d\u56fd\uff0c\u516c\u5f00\u53f7\uff1aCN114943329A\uff0c2022-08-2<\/mark>5

      <\/li>\n\n\n\n
    12. \u57fa\u4e8e\u8109\u51b2\u795e\u7ecf\u7f51\u7edc\u7684\u8f7b\u91cf\u7ea7\u7247\u4e0a\u5b66\u4e60\u65b9\u6cd5\u3001\u7cfb\u7edf\u53ca\u5904\u7406\u5668.
      \u738b\u6d77\u51b0, \u77f3\u5306*, \u7530\u654f, \u738b\u817e\u9704<\/strong>, \u4f55\u4fca\u8d24, \u4f55\u796f. \u4e2d\u56fd\uff0c\u516c\u5f00\u53f7\uff1aCN114091663A\uff0c2022-2-25<\/mark>

      <\/li>\n\n\n\n
    13. \u57fa\u4e8e\u8109\u51b2\u795e\u7ecf\u7f51\u7edc\u7684\u5b9e\u65f6\u6df1\u5ea6\u5b66\u4e60\u65b9\u6cd5\u3001\u7cfb\u7edf\u53ca\u5904\u7406\u5668.
      \u738b\u817e\u9704<\/strong>, \u77f3\u5306*, \u7530\u654f, \u4f55\u4fca\u8d24, \u738b\u6d77\u51b0, \u55bb\u5251\u4f9d. \u4e2d\u56fd\uff0c\u516c\u5f00\u53f7\uff1aCN114065922A\uff0c2022-2-18<\/mark><\/li>\n<\/ol>\n","protected":false},"excerpt":{"rendered":"

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