Fully analog iteration for solving matrix equations with in-memory computing.

ABSTRACT: Memristive in-memory computing has demonstrated potential for solving matrix equations in scientific computing. However, the inherent inaccuracies of analog mechanisms create challenges in achieving high-precision solutions while maintaining low-energy consumption. This study introduces a memristive matrix equation solver that considerably accelerates solutions by performing mathematical iterations directly within an analog domain. Our approach facilitates rapid approximate solutions with a scalable circuit topology and expedites the high-precision refinement process by substantially reducing the digital-to-analog conversion overhead. We experimentally validated this methodology using a heterogeneous computing system. We performed simulations of multiple scientific problems on these circui

SUBMITTER: Li J 

PROVIDER: S-EPMC11817932 | biostudies-literature | 2025 Feb

REPOSITORIES: biostudies-literature