# ACED: Hardware-Accelerated Graph Neural Networks for Real-Time Decision-Making in High Energy Particle Physics

> **NSF 01002526DB NSF RESEARCH & RELATED ACTIVIT** · Carnegie Mellon University (PA) · $499,934

## Abstract

Real-time artificial intelligence (AI) has become increasingly popular due to its ability to increase the accuracy of those tasks that need to be executed quickly, like the decision made by self-driving vehicles. In recent years, more complex use cases in different areas of science and beyond are emerging, where even higher accuracy is needed for sub-milliseconds tasks. In these conditions, more complex architectures need to be accelerated with dedicated hardware, by deploying them, for example, on specialized chips like field-programmable gate arrays (FPGAs). This award will develop a FPGA-accelerated graph neural networks (GNNs) to improve the real-time data filtering systems employed by high energy particle physics experiments. This work will not only promote the progress of science, but its impact can potentially transcend the field of high-energy particle physics, with potential applications in quantum computing, where it can improve the readout and control of qubits, or in autonomous navigation, where hardware-accelerated GNNs can improve the simultaneous localization and mapping of drones used for search and rescue.

The Large Hadron Collider (LHC) at CERN  will undergo a high-luminosity (HL) upgrade in 2030. It will deliver denser collisions, which will result in a dataset ten times larger, suitable for searches for rarer physics processes, as well as for higher precision measurement of particle properties. However, more particles per collision will be produced with

## Key facts

- **NSF award ID:** 2435808
- **Awardee organization:** Carnegie Mellon University (PA)
- **SAM.gov UEI:** U3NKNFLNQ613
- **PI:** Matteo Cremonesi
- **Primary program:** 01002526DB NSF RESEARCH & RELATED ACTIVIT
- **All programs:** —
- **Estimated total:** $499,934
- **Funds obligated:** $499,934
- **Transaction type:** Standard Grant
- **Period:** 07/01/2025 → 12/31/2026

## Primary source

NSF Award Search: https://www.nsf.gov/awardsearch/showAward?AWD_ID=2435808

## Citation

> US National Science Foundation, Award 2435808, ACED: Hardware-Accelerated Graph Neural Networks for Real-Time Decision-Making in High Energy Particle Physics. Retrieved via AI Analytics 2026-06-07 from https://api.ai-analytics.org/grant/nsf/2435808. Licensed CC0.

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