Memristors are now becoming a prominent candidate to serve as the building blocks of non-von Neumann in-memory computing architectures. By mapping analog numerical matrices into memristor crossbar arrays, efficient multiply accumulate operations can be performed in a massively parallel fashion using the physics mechanisms of Ohm’s law and Kirchhoff’s law. In this brief review, we present the recent progress in two niche applications: neural network accelerators and numerical computing units, mainly focusing on the advances in hardware demonstrations. The former one is regarded as soft computing since it can tolerant some degree of the device and array imperfections. The acceleration of multiple layer perceptrons, convolutional neural networks, generative adversarial networks, and long short-term memory neural networks are described. The latter one is hard computing because the solving of numerical problems requires high-precision devices. Several breakthroughs in memristive equation solvers with improved computation accuracies are highlighted. Besides, other nonvolatile devices with the capability of analog computing are also briefly introduced. Finally, we conclude the review with discussions on the challenges and opportunities for future research toward realizing memristive analog computing machines.

忆阻器现在正成为非冯·诺依曼内存中计算体系结构的基石。通过将模拟数值矩阵映射到忆阻器交叉开关阵列中，可以使用欧姆定律和基尔霍夫定律的物理机制以大规模并行方式执行有效的乘法累加运算。在这篇简短的评论中，我们介绍了两个利基应用程序的最新进展：神经网络加速器和数值计算单元，主要侧重于硬件演示的进展。前一种方法被认为是软计算，因为它可以容忍某种程度的设备和阵列缺陷。描述了多层感知器，卷积神经网络，生成对抗网络和长短期记忆神经网络的加速。后一种是难计算的，因为解决数字问题需要高精度的设备。强调了忆阻方程求解器在计算精度上的一些突破。此外，还简要介绍了其他具有模拟计算功能的非易失性设备。最后，我们以讨论实现忆阻型模拟计算机的未来挑战和机遇为讨论的总结。