  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.
