Spatial distribution of information effective for logic function learning in spin-wave reservoir computing chip utilizing spatiotemporal physical dynamics

This paper investigates the spatial distribution of information effective for function learning in a spin-wave reservoir-computing garnet chip. We map the neural weights of a readout neuron virtually connected massively and densely to the reservoir chip. We find that the spatial weight distribution shows wavefront-like lines, suggesting the importance of concurrent and time-different interferences of the spin waves. We also estimate the size of reservoir output electrodes required for the proper information extraction. These results are significantly useful for designing spin reservoir chips in the near future energy efficient devices.