As deep neural networks require tremendous amount of computation and memory, analog computing with emerging memory devices is a promising alternative to digital computing for edge devices. However, because of the increasing simulation time for analog computing system, it has not been explored. To overcome this issue, analytically approximated simulators are developed, but these models are inaccurate and narrow down the options for peripheral circuits for multiply-accumulate operation (MAC). In this sense, we propose a methodology, SEMULATOR (SiMULATOR by Emulating the analog computing block) which uses a deep neural network to emulate the behavior of crossbar-based analog computing system. With the proposed neural architecture, we experimentally and theoretically shows that it emulates a MAC unit for neural computation. In addition, the simulation time is incomparably reduced when it compared to the circuit simulators such as SPICE.

中文翻译：

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SEMULATOR：使用回归神经网络模拟基于交叉开关阵列的模拟神经系统的动力学

由于深度神经网络需要大量的计算和内存，因此新兴内存设备的模拟计算是边缘设备数字计算的有前途的替代方法。但是，由于模拟计算系统的仿真时间增加，因此尚未进行探索。为了克服这个问题，开发了解析近似的模拟器，但是这些模型不准确，并且缩小了用于乘法累加运算（MAC）的外围电路的选择范围。在这种意义上，我们提出了一种方法，即SEMULATOR（通过模拟模拟计算模块来模拟的SiMULATOR），该方法使用深度神经网络来模拟基于交叉开关的模拟计算系统的行为。利用所提出的神经体系结构，我们从实验和理论上证明了它模拟了用于神经计算的MAC单元。此外，