Nuclear Science and Techniques

《核技术》(英文版) ISSN 1001-8042 CN 31-1559/TL     2019 Impact factor 1.556

Nuclear Science and Techniques ›› 2020, Vol. 31 ›› Issue (5): 46 doi: 10.1007/s41365-020-00756-z

• NUCLEAR ELECTRONICS AND INSTRUMENTATION • Previous Articles     Next Articles

FPGA implementation of neural network accelerator for pulse information extraction in high energy physics

Jun-Ling Chen, Peng-Cheng Ai, Dong Wang, Hui Wang, Ni Fang, De-Li Xu, Qi Gong, Yuan-Kang Yang   

  1. Central China Normal University, Wuhan 430079, China
  • Received:2020-01-06 Revised:2020-03-22 Accepted:2020-03-23
  • Contact: Peng-Cheng Ai E-mail:pengcheng.ai@mails.ccnu.edu.cn
  • Supported by:
    This work was supported by the National Natural Science Foundation of China (Nos.11875146 and 11505074) and National Key Research and Development Program of China (No. 2016YFE0100900).
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Jun-Ling Chen, Peng-Cheng Ai, Dong Wang, Hui Wang, Ni Fang, De-Li Xu, Qi Gong, Yuan-Kang Yang. FPGA implementation of neural network accelerator for pulse information extraction in high energy physics.Nuclear Science and Techniques, 2020, 31(5): 46     doi: 10.1007/s41365-020-00756-z
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Abstract: Extracting the amplitude and time information from the shaped pulse is an important step in nuclear physics experiments. For this purpose, a neural network can be an alternative in offline data processing. For processing the data in real time and reducing the offline data storage required in a trigger event, we designed a customized neural network accelerator on a field programmable gate array platform to implement specific layers in a convolutional neural network. The latter is then used in the front-end electronics of the detector. With fully reconfigurable hardware, a tested neural network structure was used for accurate timing of shaped pulses common in front-end electronics. This design can handle up to four channels of pulse signals at once. The peak performance of each channel is 1.665 Giga Operations Per Second (GOPS) at a working frequency of 25 MHz.

Key words: Convolutional neural networks, Pulse shaping, Acceleration, Front-end electronics