Developments in magnetic resonance imaging (MRI) in the last decades show a trend towards a growing number of array coils and an increasing use of a wide variety of sensors. Associated cabling and safety issues have been addressed by moving data acquisition closer to the coil. However, with the increasing number of radio-frequency (RF) channels and trend towards higher acquisition duty-cycles, the data amount is growing, which poses challenges for throughput and data handling. As it is becoming a limitation, early compression and preprocessing is becoming ever more important. Additionally, sensors deliver diverse data, which require distinct and often low-latency processing for run-time updates of scanner operation. To address these challenges, we propose the transition to reconfigurable hardware with an application tailored assembly of interfaces and real-time processing resources. We present an integrated solution based on a system-on-chip (SoC), which offers sufficient throughput and hardware-based parallel processing power for very challenging applications. It is equipped with fiber-optical modules serving as versatile interfaces for modular systems with in-field operation. We demonstrate the utility of the platform on the example of concurrent imaging and field sensing with hardware-based coil compression and trajectory extraction. The preprocessed data are then used in expanded encoding model based image reconstruction of single-shot and segmented spirals as used in time-series and anatomical imaging respectively.