Specialized hardware accelerators for machine learning (ML) tasks help bring intelligent data processing to edge devices. To fully leverage their potential, efficient mapping of the software task onto the target hardware is required. One way to achieve this is through a joint design optimization of both hardware and software. This talk presents the parameterizable ML accelerator UltraTrail and its use in the hardware/software co-design framework HANNAH. We introduce the accelerators’ architecture and show how a hardware-aware neural architecture search can be utilized to automatically search for optimal hardware configurations. The advantages of this approach over a handcrafted solution are demonstrated on an audio use case. Finally, a generator-based approach is outlined that aims at further automating the design process of such hardware accelerators to increase performance and design efficiency.