Recently,　memory-augmented　neural　networks　(MANNs)　have　gained　significant　attention　as　a　critical　solution　for　few-shot　learning　(FSL).　These　networks　leverage　external　memory　to　store　prior　knowledge,　thereby　enhancing　classification　efficiency.　Spin-transfer　torque　magnetic　random　access　memory　(STT-MRAM)　is　particularly　suited　for　this　application　due　to　its　compact　cell　size,　excellent　data　retention,　and　scalability.　In　this　article,　we　introduce　a　STT-MRAM-based　near-memory　computing　(NMC)　macro　specifically　designed　for　MANNs.　Our　approach　incorporates　several　key　innovations　aimed　at　overcoming　challenges　in　hardware　implementation　while　improving　MANN　performance　as　follows:　1)　a　parallel　computing　architecture　within　the　NMC　to　expedite　L1　distance　computations;　2)　a　memory　invert　coding　(MIC)　and　self-termination　write　(STW)　scheme　that　reduce　write　operations　and　energy　consumption,　addressing　the　issues　of　frequent　writes　and　high　write　currents　during　the　training　phase　of　MANNs;　3)　a　dynamic　offset-compensation　sense　amplifier　(DOC-SA)　and　high-throughput　switch-capacitor　(HTSC)　readout　scheme　to　improve　read　accuracy　and　throughput,　tackling　low　read　margins　and　limited　readout　bandwidth;　4)　an　exploration　of　MANN　architectures　validates　the　reusability　of　the　NMC　macro.　The　optimized　matching-networks　(MCHnets)-based　structure　achieves　an　accuracy　exceeding　90%　in　five-way　and　eight-way　Omniglot　classification　tasks.　Fabricated　with　a　40-nm　CMOS　technology,　our　design　achieves　classification　accuracies　of　96.37%　for　eight-way-five-shot　tasks　and　93.72%　for　16-way-five-shot　tasks　on　the　Omniglot　dataset　utilizing　the　optimized　MCHnet,　showcasing　an　impressive　energy　efficiency　of　6.47　TOPS/W　at　the　basis　of　16-bit　L1　distance　computing　in　the　classification　tasks　of　MANN.