2024/07/30 by Jonathan Lin, Lin, Jonathan, Frank Barrows +3 · 2 citations
Computer Science · #Adaptation and Self-Organizing Systems (nlin.AO) #Classical Analysis and ODEs (math.CA) #Distributed #Dynamical Systems (math.DS) #FOS: Computer and information sciences #FOS: Mathematics #FOS: Physical sciences #Mesoscale and Nanoscale Physics (cond-mat.mes-hall) #Neural Networks and Applications #Parallel #and Cluster Computing (cs.DC)
paper · pdf · doi:10.48550/arxiv.2407.20539
openalex publication_date 2024/07/30 · openalex created_date 2025/10/10 · openalex updated_date 2026/07/28
The advent of memristive devices offers a promising avenue for efficient and scalable analog computing, particularly for linear algebra operations essential in various scientific and engineering applications. This paper investigates the potential of memristive crossbars in implementing matrix inversion algorithms. We explore both static and dynamic approaches, emphasizing the advantages of analog and in-memory computing for matrix operations beyond multiplication. Our results demonstrate that memristive arrays can significantly reduce computational complexity and power consumption compared to traditional digital methods for certain matrix tasks. Furthermore, we address the challenges of device variability, precision, and scalability, providing insights into the practical implementation of these algorithms.