Performance Analysis of Neuro-Symbolic AI Models

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Source arXiv

Machine Learning

Neural and Evolutionary Computing

Performance

Artificial Intelligence

Performance Analysis of Neuro-Symbolic AI Models

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

Neuro-symbolic artificial intelligence (neuro-symbolic models) is a novel area of artificial intelligence research which seeks to combine traditional rules-based 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.

In this paper, we describe and analyze the performance characteristics of three recent neuro-symbolic models.

We also find that data movement poses a potential bottleneck, as it does in many machine learning (ml) workloads.

Authors

Zachary Susskind, Bryce Arden, Lizy K. John, Patrick Stockton, Eugene B. John

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