AI Insight
This white paper addresses the computational challenges facing next-generation particle physics experiments, which will generate unprecedented data rates requiring real-time processing and decision-making. The authors present a community-driven research agenda focusing on the integration of machine learning with emerging hardware technologies including edge computing devices, heterogeneous accelerators, reconfigurable systems, and quantum processors. The work identifies priority areas for research and development in hardware-based ML systems designed to operate in extreme environments such as cryogenic temperatures and high-radiation fields typical of particle physics experiments.
Why it matters
The proposed framework could enable particle physics experiments to handle exponentially growing data volumes that would otherwise overwhelm traditional computing infrastructure. The hardware solutions and AI/ML integration strategies developed for particle physics applications may have broader impacts in other fields requiring real-time processing in challenging environments, such as space exploration, medical imaging, and industrial monitoring systems.
Understand the Science
⚠️ Preprint – Noch nicht peer-reviewed
Dieser Artikel wurde noch nicht von unabhängigen Experten begutachtet. Die Ergebnisse sind vorläufig und sollten mit Vorsicht interpretiert werden.
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.
Source: Machine Learning on Heterogeneous, Edge, and Quantum Hardware for Particle Physics (ML-HEQUPP)