Tailless flapping-wing drones mimic the flight mechanics of insects and offer unique advantages in agility and maneuverability compared to rotor-based drones. Yet, their limited onboard computational resources and non-linear flight dynamics complicate active attitude control. Neural network-based controllers have shown promising control performance for this task but they exceed a flapping-wing drone's onboard computational budget. To this end, the Sparse Identification of Nonlinear Dynamics (SINDy) algorithm offers a promising solution by distilling a neural network-based controller into a simplified mathematical expression. This expression is better suited for mapping onto an FPGA which provides the power efficiency essential for an energy-constrained drone. The current work presents an automated workflow to translate the simplified controller into an HDL description, maximizing DSP block usage for power and resource efficiency. Demonstration of this workflow on the pendulum simulation as a proof-of-concept has shown its efficacy. The included optimization techniques have resulted, on average, in a 40% reduction of DSP block usage, without compromising controller performance. This scalable, platform-agnostic workflow streamlines the design of a controller's hardware implementation and allows its future application to a flapping-wing drone.