# On-Sensor Data Filtering using Neuromorphic Computing for High Energy Physics Experiments

arXiv: https://arxiv.org/abs/2307.11242 · HTML: https://mlsanity.com/paper/2307.11242/

## Key facts

- Cited by 7 papers in the map (data through 14 Oct 2026).
- Builds on 6 of its 28 references that are in the map.
- First posted on arXiv 20 Jul 2023.

## Builds on (most cited first, 6 of 6)

- [Event-based Vision: A Survey](https://mlsanity.com/paper/1904.08405/) (2019)
- [A reconfigurable neural network ASIC for detector front-end data compression at the HL-LHC](https://mlsanity.com/paper/2105.01683/) (2021)
- [Ultra-low latency recurrent neural network inference on FPGAs for physics applications with hls4ml](https://mlsanity.com/paper/2207.00559/) (2022)
- [Efficient Spiking Neural Networks with Radix Encoding](https://mlsanity.com/paper/2105.06943/) (2021)
- [Neuromorphic Computing is Turing-Complete](https://mlsanity.com/paper/2104.13983/) (2021)
- [Benchmarking energy consumption and latency for neuromorphic computing in condensed matter and particle physics](https://mlsanity.com/paper/2209.10481/) (2022)

## Built on by (most cited first, 7 of 7)

- [Neural Network Methods for Radiation Detectors and Imaging](https://mlsanity.com/paper/2311.05726/) (2023)
- [NeuroCoreX: An Open-Source FPGA-Based Spiking Neural Network Emulator with On-Chip Learning](https://mlsanity.com/paper/2506.14138/) (2025)
- [Embedded FPGA Developments in 130nm and 28nm CMOS for Machine Learning in Particle Detector Readout](https://mlsanity.com/paper/2404.17701/) (2024)
- [Spiking Neural Network inference on FPGAs with hls4ml](https://mlsanity.com/paper/2606.10008/) (2026)
- [HPCNeuroNet: A Neuromorphic Approach Merging SNN Temporal Dynamics with Transformer Attention for FPGA-based Particle Physics](https://mlsanity.com/paper/2412.17571/) (2024)
- [PETNet -- Coincident Particle Event Detection using Spiking Neural Networks](https://mlsanity.com/paper/2504.06730/) (2025)
- [SuperNeuroMAT: An Efficient Matrix-based Simulator for Spiking Neural Networks](https://mlsanity.com/paper/2608.08479/) (2026)

## Abstract (arXiv)

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.