  While physical reservoir computing (PRC) is a promising way to achieve low
power consumption neuromorphic computing, its computational performance is
still insufficient at a practical level. One promising approach to improving
PRC performance is deep reservoir computing (deep-RC), in which the component
reservoirs are multi-layered. However, all of the deep-RC schemes reported so
far have been effective only for simulation reservoirs and limited PRCs, and
there have been no reports of nanodevice implementations. Here, as the first
nanodevice implementation of Deep-RC, we report a demonstration of deep
physical reservoir computing using an ion gating reservoir (IGR), which is a
small and high-performance physical reservoir. While previously reported
Deep-RC scheme did not improve the performance of IGR, our Deep-IGR achieved a
normalized mean squared error of 0.0092 on a second-order nonlinear
autoregressive moving average task, with is the best performance of any
physical reservoir so far reported. More importantly, the device outperformed
full simulation reservoir computing. The dramatic performance improvement of
the IGR with our deep-RC architecture paves the way for high-performance,
large-scale, physical neural network devices.
