While the development of artificial intelligence on cloud computer has made people's lives more convenient and affluent, the increase in electric power consumption and a load of network will be caused by the explosive growth in the amount of information flying through society in the future. Although this problem could be greatly improved if the information could be processed at an edge device where the information was acquired before sending the data to the cloud, this scheme has not been realized so far due to high computing cost resulting from the complexity of the network structure. Among various machine learning models, reservoir computing, in which only readout weights are trained, has the advantages of low computing cost and fast processing speed due to its simple and small network structure. In particular, development of physical reservoir that is a physical device satisfying distinctive properties required to reservoir (i.e., nonlinearity, short-term memory, and high dimensionality) is a promising approach to realize compact artificial intelligence integrated on the edge device (i.e., edge computer) and is energetically attempted with various physical devices.[1,2] However, some fatal issues remain on the road to said implementation as follows; low computational power, high electrical power consumption, and large device volume. Although it was theoretically found that spintronic physical reservoir utilizing nonlinear interfered spin wave can overcome these issues, the physical reservoir had not been experimentally demonstrated so far.[3]

  Very recently, we experimentally demonstrated a physical reservoir utilizing nonlinear interfered spin wave multi-detection for the first time [4,5] and achieved the best benchmark (the lowest processing error of 1.81 × 10^-2) in physical reservoirs at that time.[4] This excellent performance results from strong nonlinear dynamics characterized as chaos, which has not been observed in the theoretical demonstration. Here, we developed an innovative physical reservoir based on the physical reservoir using the chaotic interfered spin waves, namely iono-magnonic reservoir, toward further improvement of the computational performance. The iono-magnonic reservoir consists of spin wave propagating ferrimagnet Y₃Fe₅O₁₂ (YIG) and proton conducting polymer Nafion and combines the spin wave interference (spintronics) and an ion-gating driven by external electric field (ionics). Protons migrate from Nafion to YIG by gate voltage application, leading to spin wave frequency and amplitude variations through magnetization and magnetic anisotropy manipulations. The manipulated spin wave can be used reservoir computing, and the processing error is successfully reduced to 9.53 × 10^-3 from 1.81 × 10^-2.[6]

  In the presentation, we will report details of the iono-magnonic reservoir, including a mechanism of spin wave manipulation through ion-gating. The physical reservoir improved by physical dynamics manipulation will be keys to realize the edge computer.