LL-GNN: Low Latency Graph Neural Networks on FPGAs for High Energy Physics

Abstract

This work presents a novel reconfigurable architecture for Low Latency Graph Neural Network (LL-GNN) designs for particle detectors, delivering unprecedented low latency performance. Incorporating FPGA-based GNNs into particle detectors presents a unique challenge since it requires sub-microsecond latency to deploy the networks for online event selection with a data rate of hundreds of terabytes per second in the Level-1 triggers at the CERN Large Hadron Collider experiments. This paper proposes a novel outer-product based matrix multiplication approach, which is enhanced by exploiting the structured adjacency matrix and a column-major data layout. Moreover, a fusion step is introduced to further reduce the end-to-end design latency by eliminating unnecessary boundaries. Furthermore, a 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 given latency constraints. To facilitate this, 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 9.0 times faster and achieves up to 13.1 times higher power efficiency than a GPU implementation. Compared to the 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. The proposed LL-GNN design advances the next generation of trigger systems by enabling sophisticated algorithms to process experimental data efficiently.

This paper has been accepted by ACM Transactions on Embedded Computing Systems (TECS)

References in corpus (5)

• Automatic heterogeneous quantization of deep neural networks for low-latency inference on the edge for particle detectors
• GraphACT: Accelerating GCN Training on CPU-FPGA Heterogeneous Platforms
• Distance-Weighted Graph Neural Networks on FPGAs for Real-Time Particle Reconstruction in High Energy Physics
• Graph Neural Networks for Charged Particle Tracking on FPGAs
• MetaML: Automating Customizable Cross-Stage Design-Flow for Deep Learning Acceleration

Cited by in corpus (6)

• Ultrafast jet classification on FPGAs for the HL-LHC
• da4ml: Distributed Arithmetic for Real-time Neural Networks on FPGAs
• Fast Jet Tagging with MLP-Mixers on FPGAs
• Efficient Mixed Precision Quantization in Graph Neural Networks
• JEDI-linear: Fast and Efficient Graph Neural Networks for Jet Tagging on FPGAs
• Real-time graph neural networks on FPGAs for the Belle II electromagnetic calorimeter