Graph Neural Networks for Charged Particle Tracking on FPGAs

Elabd, Abdelrahman; Razavimaleki, Vesal; Huang, Shi-Yu; Duarte, Javier; Atkinson, Markus; DeZoort, Gage; Elmer, Peter; Hauck, Scott; Hu, Jin-Xuan; Hsu, Shih-Chieh; Lai, Bo-Cheng; Neubauer, Mark; Ojalvo, Isobel; Thais, Savannah; Trahms, Matthew

doi:10.48550/arxiv.2112.02048

Cited by 2 publications

(2 citation statements)

References 43 publications

(54 reference statements)

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“…It learns to capture complex interactions that can be used to predict future states and abstract physical properties. The acceleration of interaction-network based GNNs has also been studied for charged particle tracking at the CERN LHC on FPGAs [26].…”

Section: Graph Neural Network and Interaction Networkmentioning

confidence: 99%

LL-GNN: Low Latency Graph Neural Networks on FPGAs for Particle Detectors

Que¹,

Loo²,

Fan³

et al. 2022

Preprint

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This work proposes a novel reconfigurable architecture for low latency Graph Neural Network (GNN) design specifically for particle detectors. Accelerating GNNs for particle detectors is challenging since it requires sub-microsecond latency to deploy the networks for online event selection in the Level-1 triggers at the CERN Large Hadron Collider experiments. This paper proposes a custom code transformation with strength reduction for the matrix multiplication operations in the interaction-network based GNNs with fully connected graphs, which avoids the costly multiplication of the adjacency matrix with the input feature matrix. It exploits sparsity patterns as well as binary adjacency matrices, and avoids irregular memory access, leading to a reduction in latency and improvement in hardware efficiency. In addition, we introduce an outer-product based matrix multiplication approach which is enhanced by the strength reduction for low latency design. Also, a fusion step is introduced to further reduce the design latency. Furthermore, an GNN-specific algorithm-hardware co-design approach is presented which not only finds a design with a much better latency but also finds a high accuracy design under a given latency constraint. Finally, a customizable template for this low latency GNN hardware architecture has been designed and open-sourced, which enables the generation of low-latency FPGA designs with efficient resource utilization using a high-level synthesis tool. Evaluation results show that our FPGA implementation is up to 24 times faster and consumes up to 45 times less power than a GPU implementation. Compared to our previous FPGA implementations, this work achieves 6.51 to 16.7 times lower latency. Moreover, the latency of our FPGA design is sufficiently low to enable deployment of GNNs in a sub-microsecond, real-time collider trigger system, enabling it to benefit from improved accuracy.

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Section: Graph Neural Network and Interaction Networkmentioning

confidence: 99%

LL-GNN: Low Latency Graph Neural Networks on FPGAs for Particle Detectors

Que¹,

Loo²,

Fan³

et al. 2022

Preprint

View full text Add to dashboard Cite

show abstract

“…More recently, driven in part by the need to increase accuracy in selecting high-dimensional and highly-detailed data from modern-day particle detectors, machine learning (ML) algorithms based on both supervised and unsupervised learning have been proposed and shown to be capable of effectively triggering on incoming physics data, proving to be a viable solution for the upcoming data challenges of future particle physics experiments (see, e.g. [3][4][5][6][7][8][9]). Implementing ML algorithms into dedicated hardware for triggering, such as GPUs, or FPGAs, can potentially guarantee fast execution of the algorithm while taking advantage of the algorithm's accuracy in selecting data of interest with maximal signal efficiency and signal purity.…”

Section: Introductionmentioning

confidence: 99%

Real-time Inference with 2D Convolutional Neural Networks on Field Programmable Gate Arrays for High-rate Particle Imaging Detectors

Jwa¹,

Guglielmo²,

Arnold³

et al. 2022

Preprint

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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 quantization is further used to minimize the computing resource utilization of the network. We use "High Level Synthesis for Machine Learning" (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.

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Graph Neural Networks for Charged Particle Tracking on FPGAs

Cited by 2 publications

References 43 publications

LL-GNN: Low Latency Graph Neural Networks on FPGAs for Particle Detectors

LL-GNN: Low Latency Graph Neural Networks on FPGAs for Particle Detectors

Real-time Inference with 2D Convolutional Neural Networks on Field Programmable Gate Arrays for High-rate Particle Imaging Detectors

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