Exploiting Static Conductance and Dynamic Switching of Memristors for Artificial Intelligence Applications

Memristors, as programmable resistive switching devices, offer two fundamental computational modalities for artificial intelligence: static conductance for parallel data processing and dynamic switching for temporal, logical, and stochastic operations. This Review systematically distinguishes these two modalities and evaluates their respective hardware implementations. In terms of our review scope, we first examine how static conductance modality is exploited in analog matrix computing, which encompasses matrix–vector multiplication and matrix equation solving, and discuss how these primitives enable efficient neural network inference and training. Second, we survey dynamic switching modality and its algorithmic applications, including stateful logic for digital in-memory acceleration, attractor networks for associative memory, reservoir computing and spatiotemporal signal processing using transient device dynamics, biologically inspired spike-timing-dependent plasticity, and stochastic computation. In addition, we discuss key challenges such as device variability, stochastic switching, interconnect parasitics, peripheral circuit overhead, and endurance limitations. We also highlight opportunities for future development, emphasizing algorithm–hardware co-design to leverage application-specific error tolerance and mitigate device non-idealities. Finally, we outline promising research directions aimed at realizing robust, scalable, and energy-efficient memristor-based AI systems.