论文标题
使用2D卷积神经网络的实时推断在现场可编程栅极阵列上用于高速粒子成像探测器
Real-time Inference with 2D Convolutional Neural Networks on Field Programmable Gate Arrays for High-rate Particle Imaging Detectors
论文作者
论文摘要
我们提出了2D卷积神经网络（CNN）的自定义实现，作为在高分辨率和高速粒子成像探测器中实时数据选择的可行应用程序，利用高端野外可编程门阵列（FPGA）中的硬件加速度。为了满足FPGA资源约束，优化了两层CNN，可用于与Kerastuner的准确性和延迟，而Network \ textIt {量化}进一步用于最小化网络的计算资源利用率。我们使用“用于机器学习的高级合成”（\ textIt {hls4ml}）工具在Xilinx Ultrascale+ FPGA上测试CNN部署，这是未来深层地下中源实验（Dune）遥远检测器的前端读取系统的FPGA技术。我们评估网络的准确性和估计延迟和硬件资源使用情况，并评论在拟议的沙丘数据采集系统中应用CNN进行实时数据选择的可行性。
We present a custom implementation of a 2D Convolutional Neural Network (CNN) as a viable application for real-time data selection in high-resolution and high-rate particle imaging detectors, making use of hardware acceleration in high-end Field Programmable Gate Arrays (FPGAs). To meet FPGA resource constraints, a two-layer CNN is optimized for accuracy and latency with KerasTuner, and network \textit{quantization} is further used to minimize the computing resource utilization of the network. We use "High Level Synthesis for Machine Learning" (\textit{hls4ml}) tools to test CNN deployment on a Xilinx UltraScale+ FPGA, which is a proposed FPGA technology for the front-end readout system of the future Deep Underground Neutrino Experiment (DUNE) far detector. We evaluate network accuracy and estimate latency and hardware resource usage, and comment on the feasibility of applying CNNs for real-time data selection within the proposed DUNE data acquisition system.
