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

SEMULATOR: Emulating the Dynamics of Crossbar Array-based Analog Neural System with Regression Neural Networks