Architectural simulators are widely used in computer architecture to explore design spaces and validate ideas before implementation. Recent works such as Scale-Sim extend this approach to convolutional neural network (CNN) accelerators, particularly systolic array-based designs. However, most target ASIC and overlook the unique resource characteristics of FPGA which is now a popular platform for CNN acceleration. To address this gap, we present MoSim, a modular, cycle-accurate simulator tailored for FPGA-based systolic CNN accelerators. MoSim models FPGA-specific resources and reports detailed performance and resource statistics to facilitate design space exploration. Evaluations on representative CNN workloads demonstrate that the simulator effectively captures FPGA-specific effects and guides practical design decisions.