# Benchmarking energy consumption and latency for neuromorphic computing in condensed matter and particle physics

arXiv: https://arxiv.org/abs/2209.10481 · HTML: https://mlsanity.com/paper/2209.10481/

## Key facts

- Cited by 5 papers in the map (data through 14 Oct 2026).
- Builds on 6 of its 34 references that are in the map.
- First posted on arXiv 21 Sep 2022.

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

- [A Living Review of Machine Learning for Particle Physics](https://mlsanity.com/paper/2102.02770/) (2021)
- [Brain-inspired computing: We need a master plan](https://mlsanity.com/paper/2104.14517/) (2021)
- [Machine Learning in the Search for New Fundamental Physics](https://mlsanity.com/paper/2112.03769/) (2021)
- [The Dark Machines Anomaly Score Challenge: Benchmark Data and Model Independent Event Classification for the Large Hadron Collider](https://mlsanity.com/paper/2105.14027/) (2021)
- [Machine Learning for Condensed Matter Physics](https://mlsanity.com/paper/2005.14228/) (2020)
- [Spiking neuromorphic chip learns entangled quantum states](https://mlsanity.com/paper/2008.01039/) (2020)

## Built on by (most cited first, 5 of 5)

- [On-Sensor Data Filtering using Neuromorphic Computing for High Energy Physics Experiments](https://mlsanity.com/paper/2307.11242/) (2023)
- [HPCNeuroNet: A Neuromorphic Approach Merging SNN Temporal Dynamics with Transformer Attention for FPGA-based Particle Physics](https://mlsanity.com/paper/2412.17571/) (2024)
- [Over-the-Air Multi-Sensor Inference with Neural Networks Using Memristor-Based Analog Computing](https://mlsanity.com/paper/2501.10245/) (2025)
- [PETNet -- Coincident Particle Event Detection using Spiking Neural Networks](https://mlsanity.com/paper/2504.06730/) (2025)
- [The Neural Division of Labor: Biologically-Inspired Modular Architectures for Robust Neuromorphic Computing](https://mlsanity.com/paper/2608.08317/) (2026)

## Abstract (arXiv)

The massive use of artificial neural networks (ANNs), increasingly popular in many areas of scientific computing, rapidly increases the energy consumption of modern high-performance computing systems. An appealing and possibly more sustainable alternative is provided by novel neuromorphic paradigms, which directly implement ANNs in hardware. However, little is known about the actual benefits of running ANNs on neuromorphic hardware for use cases in scientific computing. Here we present a methodology for measuring the energy cost and compute time for inference tasks with ANNs on conventional hardware. In addition, we have designed an architecture for these tasks and estimate the same metrics based on a state-of-the-art analog in-memory computing (AIMC) platform, one of the key paradigms in neuromorphic computing. Both methodologies are compared for a use case in quantum many-body physics in two dimensional condensed matter systems and for anomaly detection at 40 MHz rates at the Large Hadron Collider in particle physics. We find that AIMC can achieve up to one order of magnitude shorter computation times than conventional hardware, at an energy cost that is up to three orders of magnitude smaller. This suggests great potential for faster and more sustainable scientific computing with neuromorphic hardware.