Advancing Machine Learning with Memristor-Based Nanodevices: Unlocking Energy-Efficient and Scalable Architectures

The adoption of memristor-based nanodevices in machine learning systems is gaining momentum due to their fast, energy-efficient, and non-volatile switching characteristics. Memristors are distinctive in their ability to store resistance states based on past voltage and current, making them particularly effective for tasks like vector-matrix multiplication. This feature helps mitigate the von Neumann bottleneck, improving computational efficiency. One of the most compelling advantages of memristors is their capacity for in-memory computing, where data storage and processing occur in the same physical location. This eliminates the need for constant data transfer between memory and the central processing unit (CPU), a key limitation in traditional computing architectures. Additionally, memristors support analog computations, which can offer significant speed and power efficiency gains compared to digital approaches. These attributes are particularly beneficial in neuromorphic computing, where the brain's synaptic behavior is emulated to build more efficient, brain-inspired machine learning systems. As research continues, memristors are expected to play a pivotal role in advancing machine learning by enabling highly parallel, scalable, and energy-efficient architectures. However, challenges related to fabrication, device variability, and long-term stability still need to be addressed to fully unlock their potential. This review offers a comprehensive analysis of current research, practical applications, emerging challenges, and future prospects for this cutting-edge interdisciplinary field.