Memristor State Transition Dynamics for Energy-Efficient CiM-Driven LLMs in Industrial IoT

The rapid development of the Industrial Internet of Things (IIoT) marks a major paradigm shift in the application of advanced computing technologies across industries. At the forefront of this revolution is the advanced technology of Computing in Memory (CiM), which mainly focuses on combining digital and analog approaches to improve the computational efficiency of neuromorphic processors. The goal of this study is to explore the connection between memristor-based CiM techniques and large-scale language models (LLMs) in IIoT systems, with a particular focus on memristor state-transition models and their impact on developing energy-efficient, high-performance neuromorphic processors. Experimental results show that the mobility of the memristor and the frequency of the applied voltage are important parameters of state transitions. We focus on exploring memristor’s instant saturation properties and state transition dynamics, and we investigate their potential applications in CiM-based LLMs. We analyze both multistate and dual-state memristors for analog and digital CiM, focusing on their advantages in terms of computational accuracy and power consumption for intelligent IIoT.