Industry leaders in automotive radars are moving towards highly dense MIMO radars (i.e., 4D radars), as they provide robust detection at a high angular resolution. However, these systems come at the expense of highly parallel processing requirements, higher off- chip communication data rates, and higher power consumption as a result of denser arrays to process. To date, no work in the open literature addresses the low-power requirements in DSPs for such dense MIMO FMCW radars, their scalability, their highly parallel processing requirements, and on-chip Machine Learning (ML) in the context of those 4D (i.e., azimuth, elevation, Doppler, and range) radars, which are all crucial challenges to leverage their potential. We propose a 12-ADC multi-core DSP (RaDSP) in 22FDX GLOBALFOUNDRIES using Adaptive Body Biasing (ABB) at 0.6V to enable 5D (i.e., 4D plus the target class) MIMO radar processing with 25 processing elements (PEs) operating at a low clock frequency for a reduced average power consumption of only 52.6mW that is at least 90x lower than state-of-the-art commercial MIMO Radar DSPs.