Dynamic Memristors for Temporal Signal Processing

Abstract

Abstract The rapid advancement of neuromorphic computing demands innovative hardware solutions capable of efficiently mimicking the functionality of biological neural systems. In this context, dynamic memristors have emerged as promising candidates for realizing neuromorphic reservoir computing (RC) architectures. The dynamic memristors characterized by their ability to exhibit nonlinear conductance variations and transient memory behaviors offer unique advantages for constructing RC systems. Unlike recurrent neural networks (RNNs) that face challenges such as vanishing or exploding gradients during training, RC leverages a fixed‐size reservoir layer that acts as a nonlinear dynamic memory. Researchers can capitalize on their adaptable and efficient characteristics by integrating dynamic memristors into RC systems to enable rapid information processing with low learning costs. This perspective provides an overview of the recent developments in dynamic memristors and their applications in neuromorphic RC. It highlights their potential to revolutionize artificial intelligence hardware by offering faster learning speeds and enhanced energy efficiency. Furthermore, it discusses challenges and opportunities associated with integrating dynamic memristors into RC architectures, paving the way for developing next‐generation cognitive computing systems.