Performance and Power-Efficiency Improvements on Graph Embedding using Sequential Training Algorithm and FPGA

Recently, graph structures have been utilized in IoT (Internet of Things) environments such as network anomaly detection, smart transportation, and smart grids. A graph embedding is a representation of a graph as a fixed-length, low-dimensional vector, which can concisely represent the characteristics of the graph. node2vec is one of the well-known algorithms for obtaining such a graph embedding by sampling neighboring nodes on a given graph using a random walk technique. However, the original node2vec algorithm relies on a conventional batch training using backpropagation algorithm. In other words, we have to retain the training data to retrain the model, which makes it unsuitable for real-world applications where the graph structure changes after the deployment. To address the changes of graph structures after the IoT devices are deployed in edge environments, this paper proposes a combination of an online sequential training algorithm and node2vec. The proposed model is implemented on an FPGA (Field-Programmable Gate Array) device for efficient sequential training. The proposed FPGA implementation achieves up to a 205.25 times speed improvement compared to the original model on an ARM Cortex-A53 CPU. We also evaluate the performance of the proposed model in the sequential training task from various perspectives. For example, evaluation results on dynamic graphs show that while the accuracy decreases in the original model, the proposed sequential model can obtain better graph embedding that achieves a higher accuracy even when the graph structure changes. In addition, the proposed FPGA implementation is evaluated in terms of the power consumption, and the results show that it significantly improves the power efficiency compared to the CPU and embedded GPU implementations.