Subgraph matching is a significant problem in several fields, including like social network analysis, chemical compound search, and fraud detection. While current solutions using CPU, graphics processing units (GPUs), and data center field-programmable gate arrays (FPGAs) deliver high performance, they consume significant power, making them unsuitable for resource-constrained edge environments. This article introduces a novel low-power FPGA accelerator that enables efficient subgraph matching using a small FPGA-based accelerator through three main innovations: a flexible two-level hash table architecture that minimizes memory accesses, a caching mechanism that reduces on-chip memory requirements, and a dynamic first-in first-out (FIFO) queue that efficiently buffers partial results. Experimental results on five real-world graphs show that the accelerator reduces energy consumption by an average of 75× compared to state-of-the-art CPU solutions, and 107× compared to GPU solutions, demonstrating that complex graph operations can be performed efficiently using even a small accelerator implemented on a reconfigurable platform.

Low-Power Subgraph Isomorphism at the Edge Using FPGAs / Bosio, Roberto; Brignone, Giovanni; Urso, Teodoro; Lazarescu, Mihai T.; Lavagno, Luciano; Pasini, Paolo. - In: IEEE ACCESS. - ISSN 2169-3536. - 13:(2025), pp. 67127-67135. [10.1109/ACCESS.2025.3560405]

Low-Power Subgraph Isomorphism at the Edge Using FPGAs

Roberto Bosio;Giovanni Brignone;Teodoro Urso;Mihai T. Lazarescu;Luciano Lavagno;Paolo Pasini
2025

Abstract

Subgraph matching is a significant problem in several fields, including like social network analysis, chemical compound search, and fraud detection. While current solutions using CPU, graphics processing units (GPUs), and data center field-programmable gate arrays (FPGAs) deliver high performance, they consume significant power, making them unsuitable for resource-constrained edge environments. This article introduces a novel low-power FPGA accelerator that enables efficient subgraph matching using a small FPGA-based accelerator through three main innovations: a flexible two-level hash table architecture that minimizes memory accesses, a caching mechanism that reduces on-chip memory requirements, and a dynamic first-in first-out (FIFO) queue that efficiently buffers partial results. Experimental results on five real-world graphs show that the accelerator reduces energy consumption by an average of 75× compared to state-of-the-art CPU solutions, and 107× compared to GPU solutions, demonstrating that complex graph operations can be performed efficiently using even a small accelerator implemented on a reconfigurable platform.
2025
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Utilizza questo identificativo per citare o creare un link a questo documento: https://hdl.handle.net/11583/2999761