Neuro-symbolic artificial intelligence has garnered considerable attention amid increasing industry demands for high-performance neural networks that are interpretable and adaptable to previously unknown problem domains with minimal reconfiguration. However, implementing neuro-symbolic hardware is challenging due to the complexity in symbolic knowledge representation and calculation. We experimentally demonstrated a memristor-based neuro-fuzzy hardware based on TiN/TaO_x/HfO_x/TiN chips that is superior to its silicon-based counterpart in terms of throughput and energy efficiency by using array topological structure for knowledge representation and physical laws for computing. Intrinsic memristor variability is fully exploited to increase robustness in knowledge representation. A hybrid in situ training strategy is proposed for error minimizing in training. The hardware adapts easier to a previously unknown environment, achieving ~6.6 times faster convergence and ~6 times lower error than deep learning. The hardware energy efficiency is over two orders of magnitude greater than field-programmable gate arrays. This research greatly extends the capability of memristor-based neuromorphic computing systems in artificial intelligence.

中文翻译：

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在忆阻器交叉阵列中实现的随机神经模糊系统

随着行业对高性能神经网络的需求不断增加，神经符号人工智能已经引起了相当大的关注，这些神经网络可以解释并适应以前未知的问题领域，并且只需最少的重新配置。然而，由于符号知识表示和计算的复杂性，实现神经符号硬件具有挑战性。我们通过实验证明了一种基于 TiN/TaO _x /HfO _x /TiN 芯片的基于忆阻器的神经模糊硬件，通过使用阵列拓扑结构进行知识表示和物理定律，该硬件在吞吐量和能源效率方面优于基于硅的同类硬件用于计算。充分利用忆阻器固有的可变性来提高知识表示的鲁棒性。提出了一种混合原位训练策略以最小化训练中的错误。该硬件更容易适应以前未知的环境，与深度学习相比，收敛速度加快约 6.6 倍，误差降低约 6 倍。硬件能效比现场可编程门阵列高两个数量级以上。这项研究极大地扩展了基于忆阻器的神经形态计算系统在人工智能中的能力。