More and more specialized processors, known as Neural Processing Units (NPUs), have been or are being built for deep neural network inference. Design and optimization of this kind of processor are inseparable from the deep learning ecosystem and corresponding underlying software. This HW/SW co-design requirement poses challenges for designers. Therefore, in this work, we experiment with an agile development method to shorten the development cycles of NPUs. We utilize Chisel for hardware design and develop a custom Chisel backend for generating cycle-accurate simulators with C++/Python APIs. On top of the simulator, we built a Python software stack for software development, performance evaluation, and simulation-based verification. The proposed method is purely software and does not involve real hardware, thus allowing the integration of software agile development methods into digital designs. In the experiments, we show how it helps us identify inherent hardware limitations and how it shortens our development cycles.