An FPGA-Based Accelerator for Sequentially Training Graph Embedding Models

A sequentially-trainable graph embedding model is proposed by combining the node2vec algorithm with the online sequential extreme learning machine (OS-ELM) training method. The proposed model is implemented on a resource-limited FPGA device to enable efficient on-device training for dynamic graph structures.