120,465 research outputs found
Low Latency Edge Classification GNN for Particle Trajectory Tracking on FPGAs
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
Learning Graphs from Linear Measurements: Fundamental Trade-offs and Applications
We consider a specific graph learning task: reconstructing a symmetric matrix that represents an underlying graph using linear measurements. We present a sparsity characterization for distributions of random graphs (that are allowed to contain high-degree nodes), based on which we study fundamental trade-offs between the number of measurements, the complexity of the graph class, and the probability of error. We first derive a necessary condition on the number of measurements. Then, by considering a three-stage recovery scheme, we give a sufficient condition for recovery. Furthermore, assuming the measurements are Gaussian IID, we prove upper and lower bounds on the (worst-case) sample complexity for both noisy and noiseless recovery. In the special cases of the uniform distribution on trees with n nodes and the Erdős-Rényi (n,p) class, the fundamental trade-offs are tight up to multiplicative factors with noiseless measurements. In addition, for practical applications, we design and implement a polynomial-time (in n ) algorithm based on the three-stage recovery scheme. Experiments show that the heuristic algorithm outperforms basis pursuit on star graphs. We apply the heuristic algorithm to learn admittance matrices in electric grids. Simulations for several canonical graph classes and IEEE power system test cases demonstrate the effectiveness and robustness of the proposed algorithm for parameter reconstruction
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