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

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 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.