This work describes the investigation of neuromorphic computing-based spiking neural network (SNN) models used to filter data from sensor electronics in high energy physics experiments conducted at the High Luminosity Large Hadron Collider. We present our approach for developing a compact neuromorphic model that filters out the sensor data based on the particle's transverse momentum with the goal of reducing the amount of data being sent to the downstream electronics. The incoming charge waveforms are converted to streams of binary-valued events, which are then processed by the SNN. We present our insights on the various system design choices - from data encoding to optimal hyperparameters of the training algorithm - for an accurate and compact SNN optimized for hardware deployment. Our results show that an SNN trained with an evolutionary algorithm and an optimized set of hyperparameters obtains a signal efficiency of about 91% with nearly half as many parameters as a deep neural network.

翻译：本文研究了基于神经形态计算的脉冲神经网络模型，用于过滤高亮度大型强子对撞机高能物理实验中传感器电子设备的数据。我们提出了一种紧凑型神经形态模型的开发方法，该模型根据粒子的横向动量过滤传感器数据，旨在减少发送至下游电子设备的数据量。传入的电荷波形被转换为二进制值事件流，随后由脉冲神经网络处理。我们针对系统设计中的多种选择——从数据编码到训练算法的最优超参数——提出了见解，以构建适用于硬件部署的精确且紧凑的脉冲神经网络。结果表明，采用进化算法训练并配备优化超参数集的脉冲神经网络，在参数数量仅为深度神经网络一半的情况下，可实现约91%的信号效率。