Implementation of a Robust and Power-Efficient Nonlinear 64-QAM Demapper using In-Memory Computing

Analog in-memory computing reduces power consumption sacrificing computational accuracy. We implement multiplication-accumulation in resistive RAM accounting for non-idealities (variations, quantization, ADC noise). The floating-point performance is recovered while minimizing power consumption in offline 64-QAM experiments.