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Neuro-symbolic artificial intelligence is a novel area of AI research whichseeks to combine traditional rules-based AI approaches with modern deeplearning techniques. Neuro-symbolic models have already demonstrated thecapability to outperform state-of-the-art deep learning models in domains suchas image and video reasoning. They have also been shown to obtain high accuracywith significantly less training data than traditional models. Due to therecency of the field's emergence and relative sparsity of published results,the performance characteristics of these models are not well understood. Inthis paper, we describe and analyze the performance characteristics of threerecent neuro-symbolic models. We find that symbolic models have less potentialparallelism than traditional neural models due to complex control flow andlow-operational-intensity operations, such as scalar multiplication and tensoraddition. However, the neural aspect of computation dominates the symbolic partin cases where they are clearly separable. We also find that data movementposes a potential bottleneck, as it does in many ML workloads.

Neuro-Symbolic AI: An Emerging Class of AI Workloads and their Characterization