Speaker
Description
Machine learning techniques have demonstrated great potential for efficiently addressing the increasing combinatorial complexity of track finding at high-luminosity collider experiments. To achieve high computational efficiency, neural network models often require compression, a necessity that becomes increasingly important for the large models used in GNN-based tracking pipelines. This is particularly true for FPGA implementations targeting low-latency hardware trigger applications, where on-chip compute and memory resources are limited. While common approaches include quantisation and pruning, this contribution investigates symbolic regression (SR) as a model compression tool. SR is a regression algorithm that searches the space of algebraic operators to find analytic functions that best fit an underlying dataset. The aim is to replace the neural network models involved in GNN tracking with a set of functions that require less computational resources.
In this contribution the open data ColliderML dataset is used to study this approach on networks used for track seeding within regions of the pixel system of the OpenDataDetector. The focus is on Metric Learning which is a machine learning based approach to construct a graph from hit data. Results of applying an SR based approximation scheme to a Metric Learning multilayer perceptron used for regional track seeding are presented. Furthermore, the computational resource usage on FPGA of the found analytic function sets is compared to that of other compression schemes.