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

CIMulator: A Comprehensive Simulation Platform for Computing-In-Memory Circuit Macros with Low Bit-Width and Real Memory Materials