For the efficient recognition and classification of numerous images, neuroinspired deep learning algorithms have demonstrated their substantial performance. Nevertheless, current deep learning algorithms that are performed on von Neumann machines face significant limitations due to their inherent inefficient energy consumption. Thus, alternative approaches (i.e., neuromorphic systems) are expected to provide more energy‐efficient computing units. However, the implementation of the neuromorphic system is still challenging due to the uncertain impacts of synaptic device specifications on system performance. Moreover, only few studies are reported how to implement feature extraction algorithms on the neuromorphic system. Here, a synaptic device network architecture with a feature extraction algorithm inspired by the convolutional neural network is demonstrated. Its pattern recognition efficacy is validated using a device‐to‐system level simulation. The network can classify handwritten digits at up to a 90% recognition rate despite using fewer synaptic devices than the architecture without feature extraction.

中文翻译：

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具有特征提取的突触设备网络架构，用于无监督图像分类

为了有效识别和分类大量图像，神经启发式深度学习算法已证明了其实质性的性能。然而，由于它们固有的低能效消耗，在冯·诺依曼机器上执行的当前深度学习算法面临着巨大的局限性。因此，替代方法（即神经形态系统）有望提供更节能的计算单元。但是，由于突触设备规格对系统性能的不确定影响，神经形态系统的实施仍然具有挑战性。此外，只有很少的研究报道如何在神经形态系统上实现特征提取算法。这里，演示了具有卷积神经网络启发的特征提取算法的突触设备网络体系结构。它的模式识别功效已通过设备到系统级别的仿真得到了验证。尽管使用的突触设备比没有特征提取的体系结构少，但该网络可以以高达90％的识别率对手写数字进行分类。