论文标题
加速深层神经网络，用于实时数据选择，用于高分辨率成像粒子探测器
Accelerating Deep Neural Networks for Real-time Data Selection for High-resolution Imaging Particle Detectors
论文作者
论文摘要
本文介绍了在现场可编程阵列上卷积神经网络的自定义实现，优化和性能评估，以加速对大型的二维图像输入的深度神经网络推断。有针对性的应用是用于高分辨率粒子成像探测器的数据选择，特别是液体氩时间投影室检测器，例如未来深层地下中微子实验所采用的数据选择。我们基于深层神经网络的出色性能来激励这种特定的应用程序，以分类Dune LARTPC的模拟原始数据，并在远程，长期和有限访问的操作探测器条件下对功率有效的数据处理的需求。
This paper presents the custom implementation, optimization, and performance evaluation of convolutional neural networks on field programmable gate arrays, for the purposes of accelerating deep neural network inference on large, two-dimensional image inputs. The targeted application is that of data selection for high-resolution particle imaging detectors, and in particular liquid argon time projection chamber detectors, such as that employed by the future Deep Underground Neutrino Experiment. We motivate this particular application based on the excellent performance of deep neural networks on classifying simulated raw data from the DUNE LArTPC, combined with the need for power-efficient data processing in the case of remote, long-term, and limited-access operating detector conditions.
