论文标题
通过FPGA上的图神经网络加速带电的粒子跟踪
Accelerated Charged Particle Tracking with Graph Neural Networks on FPGAs
论文作者
论文摘要
我们开发和研究基于图神经网络的带电粒子跟踪算法的FPGA实现。这两种互补的FPGA设计基于OpenCL，这是一个编写跨异构平台执行程序的框架，以及HLS4ML是基于高级合成的编译器HLS4ML，用于神经网络以固件转换。我们根据基准数据集评估和比较了实现的资源使用情况，延迟和跟踪性能。我们发现，基于CPU的执行是可能的，有可能使此类算法有效地用于将来的计算工作流程和基于FPGA的级别1触发器，在CERN大型强子对撞机上。
We develop and study FPGA implementations of algorithms for charged particle tracking based on graph neural networks. The two complementary FPGA designs are based on OpenCL, a framework for writing programs that execute across heterogeneous platforms, and hls4ml, a high-level-synthesis-based compiler for neural network to firmware conversion. We evaluate and compare the resource usage, latency, and tracking performance of our implementations based on a benchmark dataset. We find a considerable speedup over CPU-based execution is possible, potentially enabling such algorithms to be used effectively in future computing workflows and the FPGA-based Level-1 trigger at the CERN Large Hadron Collider.
