  This paper studies numerically how the signal detector arrangement influences
the performance of reservoir computing using spin waves excited in a
ferrimagnetic garnet film. This investigation is essentially important since
the input information is not only conveyed but also transformed by the spin
waves into high-dimensional information space when the waves propagate in the
film in a spatially distributed manner. This spatiotemporal dynamics realizes a
rich reservoir-computational functionality. First, we simulate spin waves in a
rectangular garnet film with two input electrodes to obtain spatial
distributions of the reservoir states in response to input signals, which are
represented as spin vectors and used for a machine-learning waveform
classification task. The detected reservoir states are combined through readout
connection weights to generate a final output. We visualize the spatial
distribution of the weights after training to discuss the number and positions
of the output electrodes by arranging them at grid points, equiangularly
circular points or at random. We evaluate the classification accuracy by
changing the number of the output electrodes, and find that a high accuracy
($>$ 90\%) is achieved with only several tens of output electrodes regardless
of grid, circular or random arrangement. These results suggest that the spin
waves possess sufficiently complex and rich dynamics for this type of tasks.
Then we investigate in which area useful information is distributed more by
arranging the electrodes locally on the chip. Finally, we show that this device
has generalization ability for input wave-signal frequency in a certain
frequency range. These results will lead to practical design of spin-wave
reservoir devices for low-power intelligent computing in the near future.
