Eliminating Iterations of Iterative Methods: Solving Large-Scale Sparse Linear System in O(1) with RRAM-based In-Memory Accelerator

Abstract

The sparse linear solver is an important component in lots of scientific computing applications. For large-scale sparse linear systems, general-purpose processors such as CPUs and GPUs are facing challenges of high time complexity and massive data movements between processors and main memories. This work utilizes the ability of in-situ analog computing of RRAMs and builds an RRAMbased accelerator for iterative linear solvers.We first propose a basic principle of mapping iterative solvers onto RRAM-based crossbar arrays. The proposed principle eliminates not only the iterations but also the convergence condition. Based on the principle, we propose a scalable architecture that can solve large-scale sparse matrices in O(1) time complexity. Compared with a massively parallel iterative solver on GPU, our accelerator shows 100× higher performance and 1000× energy reduction. If the solution obtained by our accelerator is used as the seed for a further refinement on GPU, about 35% of the solving time and energy consumption can be saved compared with a pure GPU solving process.