Architectural simulation is a fundamental tool for modern computing system design. Computer architects can choose from a large set of software simulators that provide a robust and efficient platform for design exploration although, if the new architecture incorporates unconventional or novel features not supported by the existing simulators, the designers must develop their own. As reconfigurable hardware platforms grow more computationally capable we observe a move from software simulators towards these hardware platforms. The introduction of high-level HDLs, such as Blue spec System Verilog (BSV), offers improved productivity while still providing a tool flow capable of exploiting reconfigurable platforms. This paper focuses on understanding how to accelerate the simulation of the interconnection network of SpiNNaker [1], a massively-parallel computer for neural simulation. We analysed the modelling choices and trade-offs made during the implementation of the software (SW) model as well as those made when developing a new hardware model (HW) built on a Xilinx FPGA.