Resistive Random Access Memory (ReRAM)-based neural network accelerators have potential to surpass their digital counterparts in computational efficiency and performance. However, design of these accelerators faces a number of challenges including imperfections of the ReRAM device and a large amount of calculations required to accurately simulate the former. We present XB-SIM ${}^{\mathrm{*}}$, a simulation framework for ReRAM-crossbar-based Convolutional Neural Network (CNN) accelerators. XB-SIM ${}^{\mathrm{*}}$ can be flexibly configured to simulate the accelerator’s structure and clock-driven behaviors at the architecture level. This framework also includes an ReRAM-aware Neural Network (NN) training algorithm and a CNN-oriented mapper to train an NN and map it onto the simulated design efficiently. Behavior of the simulator has been verified by the corresponding circuit simulation of a real chip. Furthermore, a batch processing mode of the massive calculations that are required to mimic the behavior of ReRAM-crossbar circuits is proposed to fully apply the computational concurrency of the mapping strategy. On CPU/GPGPU, this batch processing mode can improve the simulation speed by up to 5.02 $\mathrm{\times }$ or 34.29 $\mathrm{\times }$. Within this framework, comprehensive architectural exploration and end-to-end evaluation have been achieved, which provide some insights for systemic optimization.