Recent　research　has　shown　that　in　massive　multiple-input　multiple-output　(MIMO)　detection,　the　model-driven　machine　learning　(ML)　detection　algorithm　with　online　training　method　can　adapt　to　channel　variations　in　real　application　scenarios　and　has　high　detection　performance.　However,　the　on-device　training　hardware　of　the　ML-enhanced　detector　has　not　yet　been　addressed　in　the　current　literature.　In　this　article,　the　architecture　for　the　targeted　hardware　is　designed　through　optimization　on　both　the　algorithms　and　the　hardware.　We　first　introduce　a　magnitude-based　pruning　strategy　and　then　propose　efficient　MMNet　(EMMNet)-type　algorithms.　In　the　improved　algorithms,　several　algorithmic　transformations　or　approximations　are　incorporated　to　reduce　computational　complexity.　For　instance,　the　exponential　operation　is　replaced　by　a　linear　fitting　function,　and　division　is　converted　into　hardware-efficient　shift　and　subtraction　operations.　Moreover,　to　improve　energy　efficiency,　the　EMMNet-type　algorithms　are　quantized　with　fixed-point　(FXP)　data　formats　and　adopt　a　hardware-friendly　stochastic　gradient　descent　(SGD)-momentum　optimizer.　Based　on　the　proposed　algorithms,　a　low-complexity　and　high-throughput　training　architecture　with　reusable　units　is　developed,　which　can　support　modulations　from　QPSK　to　QAM64.　Compared　with　the　MMNet,　the　presented　EMMNet　detector　exhibits　a　48%　reduction　in　the　number　of　multiplications　without　sacrificing　detection　performance,　demonstrating　remarkable　hardware　efficiency.