  This paper presents a simulation platform, namely CIMulator, for quantifying
the efficacy of various synaptic devices in neuromorphic accelerators for
different neural network architectures. Nonvolatile memory devices, such as
resistive random-access memory, ferroelectric field-effect transistor, and
volatile static random-access memory devices, can be selected as synaptic
devices. A multilayer perceptron and convolutional neural networks (CNNs), such
as LeNet-5, VGG-16, and a custom CNN named C4W-1, are simulated to evaluate the
effects of these synaptic devices on the training and inference outcomes. The
dataset used in the simulations are MNIST, CIFAR-10, and a white blood cell
dataset. By applying batch normalization and appropriate optimizers in the
training phase, neuromorphic systems with very low-bit-width or binary weights
could achieve high pattern recognition rates that approach software-based CNN
accuracy. We also introduce spiking neural networks with RRAM-based synaptic
devices for the recognition of MNIST handwritten digits.
