Physical reservoir computing using nonlinear heat conduction phenomena in alumina ceramic plates
Abstract
Physical reservoir computing (PRC) has attracted considerable attention as a low-training-cost framework for edge computing. In this study, we propose a PRC system that uses nonlinear heat conduction in an alumina ceramic plate. In the proposed system, the input information is applied as heat, and the transient temperature responses generated by heat conduction are used as reservoir states. The temperature dependences of the thermal conductivity and specific heat of alumina provide nonlinearity without relying on nonlinear sensor elements. The PRC performance was evaluated using temperature data obtained by finite-element analysis of an alumina disk. The results showed that the short-term memory task was improved by shortening the input step duration and increasing the input heat flux, whereas the parity-check task was enhanced by increasing the input heat flux. These results indicate that the input step duration primarily controls the memory, whereas the input heat flux controls the nonlinear transformation through temperature-dependent thermal diffusion. A demonstration experiment was performed using a fabricated alumina ceramic device with embedded resistors for heating and temperature sensing. The experimental results showed trends that were qualitatively consistent with the numerical simulations, demonstrating the feasibility of PRC using heat conduction in an actual solid-state thermal system. This study suggests that heat, which is generally treated as an unavoidable by-product of electronic devices, can be used as a computational resource for edge-computing applications.
