Reconfigurable Acceleration of Graph Neural Networks for Jet Identification in Particle Physics

Abstract

This paper presents a novel reconfigurable architecture to accelerate Graph Neural Networks (GNNs) for JEDI-net, a jet identification algorithm in particle physics which achieves state-of-the-art accuracy. The challenge is to deploy JEDI-net for online selection targeting the Large Hadron Collider (LHC) experiments with low latency. This paper proposes custom strength reduction for matrix multiplication operations customised for the GNN-based JEDI-net, which avoids the costly multiplication of the adjacency matrix with the input feature matrix. It exploits sparsity patterns and binary adjacency matrices to increase hardware efficiency while reducing latency. The throughput is further enhanced by a coarse-grained pipeline enabled by adopting column-major order data layout. Evaluation results show that our FPGA implementation is 11 times faster and consumes 12 times lower power than a GPU implementation. Moreover, the throughput of our FPGA design is sufficiently high to enable deployment of JEDI-net in a sub-microsecond, real-time collider trigger system, enabling it to benefit from improved accuracy.