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.

翻译：下一代粒子物理实验将面临数据获取领域的新时代挑战，这源于前所未有的数据速率与数据量，以及极端环境与运行约束。要利用这些数据进行科学发现，需要超越当前能力的实时推理与决策、智能数据缩减以及高效的处理架构。这一实验范式成功的关键在于几项新兴技术，例如人工智能与机器学习（AI/ML）、硅微电子学，以及量子算法与处理的兴起。它们的交叉领域包括面向边缘计算的低功耗与低延迟设备、异构加速器系统、可重构硬件、新颖的协同设计与综合策略、面向低温或高辐射环境的读出技术，以及模拟计算等研究方向。本白皮书提出了一个社区驱动的愿景，旨在识别并优先考虑基于硬件的机器学习系统及相应物理应用中的研发机遇，为成功迈向基础科学的新数据前沿贡献力量。