Solving sparse linear systems with approximate inverse preconditioners on analog devices
Emerging Technologies
2021-07-16 v1
Abstract
Sparse linear system solvers are computationally expensive kernels that lie at the heart of numerous applications. This paper proposes a flexible preconditioning framework to substantially reduce the time and energy requirements of this task by utilizing a hybrid architecture that combines conventional digital microprocessors with analog crossbar array accelerators. Our analysis and experiments with a simulator for analog hardware demonstrate that an order of magnitude speedup is readily attainable without much impact on convergence, despite the noise in analog computations.
Cite
@article{arxiv.2107.06973,
title = {Solving sparse linear systems with approximate inverse preconditioners on analog devices},
author = {Vasileios Kalantzis and Anshul Gupta and Lior Horesh and Tomasz Nowicki and Mark S. Squillante and Chai Wah Wu},
journal= {arXiv preprint arXiv:2107.06973},
year = {2021}
}