  Neuro-symbolic artificial intelligence is a novel area of AI research which
seeks to combine traditional rules-based AI approaches with modern deep
learning techniques. Neuro-symbolic models have already demonstrated the
capability to outperform state-of-the-art deep learning models in domains such
as image and video reasoning. They have also been shown to obtain high accuracy
with significantly less training data than traditional models. Due to the
recency of the field's emergence and relative sparsity of published results,
the performance characteristics of these models are not well understood. In
this paper, we describe and analyze the performance characteristics of three
recent neuro-symbolic models. We find that symbolic models have less potential
parallelism than traditional neural models due to complex control flow and
low-operational-intensity operations, such as scalar multiplication and tensor
addition. However, the neural aspect of computation dominates the symbolic part
in cases where they are clearly separable. We also find that data movement
poses a potential bottleneck, as it does in many ML workloads.
