Deep learning is expected to play a central role in 6G systems, by enabling data-driven physical layer designs that can adapt to practical wireless impairments. This is particularly important for millimetre-wave (mmWave) multiple-input multiple-output (MIMO) systems, which are strongly affected by residual channel distortions, interference, and hardware non-idealities that are difficult to capture with analytical models. In this paper, we propose a deep learning-based soft demapper that performs block-wise processing of received symbols instead of conventional symbol-wise detection. The demapper employs a one-dimensional convolutional neural network to learn temporal structure induced by practical propagation and hardware effects, while producing soft bit outputs compatible with soft-decision decoding stages. The proposed approach is trained and evaluated for a 4$\times$4 wideband mmWave MIMO system at 60~GHz, using an over-the-air dataset collected across multiple days and settings. Experimental results demonstrate consistent improvements over a conventional soft-decision demapper, including up to a 2.6$\times$ reduction in bit error rate for 16QAM and up to a 17.6$\times$ reduction for 64QAM under real over-the-air mmWave MIMO conditions. The proposed demapper is further implemented on a Zynq RFSoC platform using a reconfigurable convolution engine and fixed-point arithmetic, achieving an approximately 34.3$\times$ speedup compared to a processor-based implementation, and demonstrating that learning-based demapping can be deployed efficiently on resource-constrained edge hardware.

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