DRAGON: Dynamic Recurrent Accelerator for Graph Online Convolution

Despite the extraordinary applicative potentiality that dynamic graph inference may entail, its practical-physical implementation has been a topic seldom explored in literature. Although graph inference through neural networks has received plenty of algorithmic innovation, its transfer to the physical world has not found similar development. This is understandable since the most preeminent Euclidean acceleration techniques from CNN have little implication in the non-Euclidean nature of relational graphs. Instead of coping with the challenges arising from forcing naturally sparse structures into more inflexible stochastic arrangements, in DRAGON, we embrace this characteristic in order to promote acceleration. Inspired by high-performance computing approaches like Parallel Multi-moth Flame Optimization for Link Prediction (PMFO-LP), we propose and implement a novel efficient architecture, capable of producing similar speed-up and performance than baseline but at a fraction of its hardware requirements and power consumption. We leverage the hidden parallelistic capacity of our previously developed static graph convolutional processor ACE-GCN and expanded it with RNN structures, allowing the deployment of a multi-processing network referenced around a common pool of proximity-based centroids. Experimental results demonstrate outstanding acceleration. In comparison with the fastest CPU-based software implementation available in the literature, DRAGON has achieved roughly 191× speed-up. Under the largest configuration and dataset, DRAGON was also able to overtake a more power-hungry PMFO-LP by almost 1.59× in speed, and at around 89.59% in power efficiency. More importantly than raw acceleration, we demonstrate the unique functional qualities of our approach as a flexible and fault-tolerant solution that makes it an interesting alternative for an anthology of applicative scenarios.