The most used algorithm in active noise control (ANC) applications is the filtered-x least mean square (FxLMS). For large scale systems with multiple inputs and outputs the computational demand of the FxLMS is rising rapidly. Conventional solutions, running on digital signal processors (DSPs), have limitations in parallel computing. In this work a parameterizable multiple input-multiple output (MIMO) feedback FxLMS architecture for field-programmable gate array (FPGA) platforms is presented that can easily be optimized for high-performance or resource efficient computation. The implementation is validated in a real-time practical application using an active headrest with 2 x 2 channels. The noise reduction is evaluated over various configurations with increasing performance and the respective synthesis results are presented. Here the noise reduction does benefit from the additional computational performance gains. An additional configuration with 11 x 11 channels and filter lengths of 2049 at a sample rate of 40 kHz is shown as a benchmark on high-end FPGAs.