  In-context learning (ICL), a property demonstrated by transformer-based
sequence models, refers to the automatic inference of an input-output mapping
based on examples of the mapping provided as context. ICL requires no explicit
learning, i.e., no explicit updates of model weights, directly mapping context
and new input to the new output. Prior work has proved the usefulness of ICL
for detection in MIMO channels. In this setting, the context is given by pilot
symbols, and ICL automatically adapts a detector, or equalizer, to apply to
newly received signals. However, the implementation tested in prior art was
based on conventional artificial neural networks (ANNs), which may prove too
energy-demanding to be run on mobile devices. This paper evaluates a
neuromorphic implementation of the transformer for ICL-based MIMO detection.
This approach replaces ANNs with spiking neural networks (SNNs), and implements
the attention mechanism via stochastic computing, requiring no multiplications,
but only logical AND operations and counting. When using conventional digital
CMOS hardware, the proposed implementation is shown to preserve accuracy, with
a reduction in power consumption ranging from $5.4\times$ to $26.8\times$,
depending on the model sizes, as compared to ANN-based implementations.
