Machine Learning on Heterogeneous, Edge, and Quantum Hardware for Particle Physics (ML-HEQUPP)

TL;DR

This paper discusses the integration of machine learning with heterogeneous, edge, and quantum hardware to address data processing challenges in future particle physics experiments, emphasizing emerging technologies and research priorities.

Contribution

It provides a community-driven vision outlining research opportunities in hardware-based ML systems tailored for particle physics applications.

Findings

Identifies key hardware technologies for ML in particle physics

Highlights the importance of low-power, low-latency edge computing

Proposes a research roadmap for hardware-accelerated ML systems

Abstract

The next generation of particle physics experiments will face a new era of challenges in data acquisition, due to unprecedented data rates and volumes along with extreme environments and operational constraints. Harnessing this data for scientific discovery demands real-time inference and decision-making, intelligent data reduction, and efficient processing architectures beyond current capabilities. Crucial to the success of this experimental paradigm are several emerging technologies, such as artificial intelligence and machine learning (AI/ML), silicon microelectronics, and the advent of quantum algorithms and processing. Their intersection includes areas of research such as low-power and low-latency devices for edge computing, heterogeneous accelerator systems, reconfigurable hardware, novel codesign and synthesis strategies, readout for cryogenic or high-radiation environments, and analog computing. This white paper presents a community-driven vision to identify and prioritize research and development opportunities in hardware-based ML systems and corresponding physics applications, contributing towards a successful transition to the new data frontier of fundamental science.