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.

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神经符号 AI：新兴的一类 AI 工作负载及其特征

神经符号人工智能是人工智能研究的一个新领域，旨在将传统的基于规则的人工智能方法与现代深度学习技术相结合。神经符号模型已经证明了在图像和视频推理等领域优于最先进的深度学习模型的能力。与传统模型相比，它们还被证明能够以更少的训练数据获得高精度。由于该领域的出现较新且已发表的结果相对稀少，这些模型的性能特征还没有得到很好的理解。在本文中，我们描述和分析了三个最近的神经符号模型的性能特征。我们发现，由于复杂的控制流和低操作强度的操作，例如标量乘法和张量加法，符号模型比传统神经模型具有更少的潜在并行性。然而，在它们明显可分离的情况下，计算的神经方面支配了符号部分。我们还发现数据移动带来了潜在的瓶颈，就像在许多 ML 工作负载中一样。