Revolutionizing Biologically-inspired AI Hardware Accelerators: Unveiling the Potential of Metal Self-Directed Channel (M-SDC) Memristors in Neuromorphic Computing

Moore's Law, guiding transistor scaling for five decades, faces challenges with Dennard's scaling law weakening since 2004. Escalating lithography costs hinder further transistor node reduction, impacting performance gains. AI-driven demand strains data centers' energy consumption, questioning future computing sustainability. Researchers explore alternatives, including dark silicon, 3D stacking, superconducting structures, spintronics, and carbon nanotubes. Neuromorphic computing, designed to overcome the von Neumann bottleneck, adopts bio-inspired architecture. Memristors, the fourth electronic component, offer potential despite challenges. Metal Self-Directed Channel (M-SDC) Memristors, gaining prominence, undergo extensive research. Our study delves into M-SDC Memristors, revealing diverse conductance states and enhanced programmability crucial for large-scale neural networks. Our research illuminates M-SDC Memristors' reliability in in-memory architectures, providing insights for sustainable computing advancements. Neuromorphic computing, especially with Memristors, emerges as a promising solution, contributing essential knowledge for navigating challenges and advancing biologically-inspired AI hardware accelerators.