使用符号回归加速基于图的径迹任务
Accelerating Graph-based Tracking Tasks with Symbolic Regression
AI总结：
提出使用符号回归替代基于图的神经网络进行粒子径迹重建，保留图结构与消息传递，使其更易在FPGA上实现且在CPU上运行更快。
摘要：
由于探测器信号具有巨大的组合性，从径迹探测器的击中中重建粒子径迹是一项计算密集型任务。近期的努力已经证明，机器学习（ML）技术可以成功应用于径迹问题，扩展并改进了基于特征工程的传统方法。然而，复杂的模型在异构触发系统上实现起来可能具有挑战性，这些系统集成了如 FPGA 等架构。在 FPGA 上部署网络是可行的，但具有挑战性且受限于其资源。一种高效的替代方案可以采用符号回归（SR）。我们提出了一种新方法，使用 SR 来替代基于图的神经网络。用符号函数替换每个网络块，保留了数据的图结构并实现了消息传递。该技术非常适合异构硬件，因为它可以更容易地在 FPGA 上实现，并且与常规方法相比，在 CPU 上能提供更快的执行时间。虽然径迹问题是这项工作的目标，但它也为该方法提供了一个原理验证，该方法可应用于许多用例。
英文摘要：
The reconstruction of particle tracks from hits in tracking detectors is a computationally intensive task due to the large combinatorics of detector signals. Recent efforts have proven that ML techniques can be successfully applied to the tracking problem, extending and improving the conventional methods based on feature engineering. However, complex models can be challenging to implement on heterogeneous trigger systems, integrating architectures such as FPGAs. Deploying the network on an FPGA is feasible but challenging and limited by its resources. An efficient alternative can employ symbolic regression (SR). We propose a novel approach that uses SR to replace a graph-based neural network. Substituting each network block with a symbolic function preserves the graph structure of the data and enables message passing. The technique is perfectly suitable for heterogeneous hardware, as it can be implemented more easily on FPGAs and grants faster execution times on CPU with respect to conventional methods. While the tracking problem is the target for this work, it also provides a proof-of-principle for the method that can be applied to many use cases.
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