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.
@article{arxiv.2307.11242,
title = {On-Sensor Data Filtering using Neuromorphic Computing for High Energy Physics Experiments},
author = {Shruti R. Kulkarni and Aaron Young and Prasanna Date and Narasinga Rao Miniskar and Jeffrey S. Vetter and Farah Fahim and Benjamin Parpillon and Jennet Dickinson and Nhan Tran and Jieun Yoo and Corrinne Mills and Morris Swartz and Petar Maksimovic and Catherine D. Schuman and Alice Bean},
journal= {arXiv preprint arXiv:2307.11242},
year = {2023}
}