Real-Time Inference of Neural Networks on FPGAs for Motor Control Applications

Machine learning algorithms are increasingly used in industrial applications for a multitude of use-cases. However, using them in control tasks is a challenge due to real-time requirements and limited resources. In this paper, an implementation scheme for real-time inference of multilayer perceptron (MLP) neural networks on FPGAs is proposed. Design constraints for using MLPs in reinforcement learning agents for motor control applications are derived and accounted for in the implementation. Two MLP architectures are evaluated on an FPGA, and the timing and resource-usage data are reported. The real-time capability of the implementation for motor control applications is investigated for standard control frequencies. It is shown by experimental validation that real-time interference with an area-efficient implementation for motor control applications is achievable. Therefore, the proposed implementation scheme can be applied to deep reinforcement learning controllers with hard real-time requirements.