An FPGA-Based Accelerator for Graph Embedding using Sequential Training Algorithm

A graph embedding is an emerging approach that can represent a graph structure with a fixed-length low-dimensional vector. node2vec is a well-known algorithm to obtain such a graph embedding by sampling neighboring nodes on a given graph with a random walk technique. However, the original node2vec a...

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Bibliographic Details
Published in:2024 IEEE International Parallel and Distributed Processing Symposium Workshops (IPDPSW) pp. 148 - 154
Main Authors: Sunaga, Kazuki, Sugiura, Keisuke, Matsutani, Hiroki
Format: Conference Proceeding
Language:English
Published: IEEE 27.05.2024
Subjects:
Accuracy
Conferences
Distributed processing
FPGA
Graph embedding
Heuristic algorithms
Internet of Things
node2vec
OS-ELM
Sequential training
Training
Vectors
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Summary:A graph embedding is an emerging approach that can represent a graph structure with a fixed-length low-dimensional vector. node2vec is a well-known algorithm to obtain such a graph embedding by sampling neighboring nodes on a given graph with a random walk technique. However, the original node2vec algorithm typically relies on a batch training of graph structures; thus, it is not suited for applications in which the graph structure changes after the deployment. In this paper, we focus on node2vec applications for IoT (Internet of Things) environments. To handle the changes of graph structures after the IoT devices have been deployed in edge environments, in this paper we propose to combine an online sequential training algorithm with node2vec. The proposed sequentially-trainable model is implemented on an FPGA (Field-Programmable Gate Array) device to demonstrate the benefits of our approach. The proposed FPGA implementation achieves up to 205.25 times speedup compared to the original model on ARM Cortex-A53 CPU. Evaluation results using dynamic graphs show that although the accuracy is decreased in the original model, the proposed sequential model can obtain better graph embedding that achieves a higher accuracy even when the graph structure is changed.
DOI:10.1109/IPDPSW63119.2024.00040