More Like this

1. Accelerated Charged Particle Tracking with Graph Neural Networks on FPGAs

   Heinz, Aneesh; Razavimaleki, Vasall; Duarte, Javier; DeZoort, Gage; Ojalvo, Isobel; Thais, Savannah; Atkinson, Markus; Neubauer, Mark; Gray, Lindsey; Jindariani, Sergo; et al (November 2020, ArXivorg)

   null (Ed.)

   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. 

   more » « less
2. Low Latency Edge Classification GNN for Particle Trajectory Tracking on FPGAs

   https://doi.org/10.1109/FPL60245.2023.00050  

   Huang, Shi-Yu; Yang, Yun-Chen; Su, Yu-Ru; Lai, Bo-Cheng; Duarte, Javier; Hauck, Scott; Hsu, Shih-Chieh; Hu, Jin-Xuan; Neubauer, Mark S. (September 2023, IEEE)

   In-time particle trajectory reconstruction in the Large Hadron Collider is challenging due to the high collision rate and numerous particle hits. Using GNN (Graph Neural Network) on FPGA has enabled superior accuracy with flexible trajectory classification. However, existing GNN architectures have inefficient resource usage and insufficient parallelism for edge classification. This paper introduces a resource-efficient GNN architecture on FPGAs for low latency particle tracking. The modular architecture facilitates design scalability to support large graphs. Leveraging the geometric properties of hit detectors further reduces graph complexity and resource usage. Our results on Xilinx UltraScale+ VU9P demonstrate 1625x and 1574x performance improvement over CPU and GPU respectively. 

   more » « less
3. Nanosecond anomaly detection with decision trees and real-time application to exotic Higgs decays

   https://doi.org/10.1038/s41467-024-47704-8  

   Roche, S. T.; Bayer, Q.; Carlson, B. T.; Ouligian, W. C.; Serhiayenka, P.; Stelzer, J.; Hong, T. M. (April 2024, Nature Communications)

   Abstract We present an interpretable implementation of the autoencoding algorithm, used as an anomaly detector, built with a forest of deep decision trees on FPGA, field programmable gate arrays. Scenarios at the Large Hadron Collider at CERN are considered, for which the autoencoder is trained using known physical processes of the Standard Model. The design is then deployed in real-time trigger systems for anomaly detection of unknown physical processes, such as the detection of rare exotic decays of the Higgs boson. The inference is made with a latency value of 30 ns at percent-level resource usage using the Xilinx Virtex UltraScale+ VU9P FPGA. Our method offers anomaly detection at low latency values for edge AI users with resource constraints. 

   more » « less
4. Ultra-low latency recurrent neural network inference on FPGAs for physics applications with hls4ml

   https://doi.org/10.1088/2632-2153/acc0d7  

   Khoda, Elham E; Rankin, Dylan; Teixeira de Lima, Rafael; Harris, Philip; Hauck, Scott; Hsu, Shih-Chieh; Kagan, Michael; Loncar, Vladimir; Paikara, Chaitanya; Rao, Richa; et al (April 2023, Machine Learning: Science and Technology)

   Abstract Recurrent neural networks have been shown to be effective architectures for many tasks in high energy physics, and thus have been widely adopted. Their use in low-latency environments has, however, been limited as a result of the difficulties of implementing recurrent architectures on field-programmable gate arrays (FPGAs). In this paper we present an implementation of two types of recurrent neural network layers—long short-term memory and gated recurrent unit—within the hls4ml framework. We demonstrate that our implementation is capable of producing effective designs for both small and large models, and can be customized to meet specific design requirements for inference latencies and FPGA resources. We show the performance and synthesized designs for multiple neural networks, many of which are trained specifically for jet identification tasks at the CERN Large Hadron Collider. 

   more » « less
5. Graph Neural Networks for Charged Particle Tracking on FPGAs

   https://doi.org/10.3389/fdata.2022.828666  

   Elabd, Abdelrahman; Razavimaleki, Vesal; Huang, Shi-Yu; Duarte, Javier; Atkinson, Markus; DeZoort, Gage; Elmer, Peter; Hauck, Scott; Hu, Jin-Xuan; Hsu, Shih-Chieh; et al (March 2022, Frontiers in Big Data)

   The determination of charged particle trajectories in collisions at the CERN Large Hadron Collider (LHC) is an important but challenging problem, especially in the high interaction density conditions expected during the future high-luminosity phase of the LHC (HL-LHC). Graph neural networks (GNNs) are a type of geometric deep learning algorithm that has successfully been applied to this task by embedding tracker data as a graph—nodes represent hits, while edges represent possible track segments—and classifying the edges as true or fake track segments. However, their study in hardware- or software-based trigger applications has been limited due to their large computational cost. In this paper, we introduce an automated translation workflow, integrated into a broader tool called hls4ml , for converting GNNs into firmware for field-programmable gate arrays (FPGAs). We use this translation tool to implement GNNs for charged particle tracking, trained using the TrackML challenge dataset, on FPGAs with designs targeting different graph sizes, task complexites, and latency/throughput requirements. This work could enable the inclusion of charged particle tracking GNNs at the trigger level for HL-LHC experiments. 

   more » « less