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

• DOI: 10.1007/s41365-020-00756-z
• Publication Date: 2020-04-27T02:02:28Z
• Authors: Jun-Ling Chen; Peng-Cheng Ai; Dong Wang; Hui Wang; Ni Fang; De-Li Xu; Qi Gong; Yuan-Kang Yang
• Keywords: 0103 physical sciences; 01 natural sciences

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 off-line data processing. For processing the data in real time and reducing the off-line 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 at a working frequency of 25 MHz.