摘要
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.
基金
supported by the National Natural Science Foundation of China(Nos.11875146 and 11505074)
National Key Research and Development Program of China(No.2016YFE0100900).
