%0 Journal Article
%J IEEE Embedded Systems Letters
%D 2017
%T Structure-aware Linear Solver for Realtime Convex Optimization for Embedded Systems
%A Ichitaro Yamazaki
%A Saeid Nooshabadi
%A Stanimire Tomov
%A Jack Dongarra
%K Karush Kuhn Tucker (KKT)
%K Realtime embedded convex optimization solver
%X With the increasing sophistication in the use of optimization algorithms such as deep learning on embedded systems, the convex optimization solvers on embedded systems have found widespread use. This letter presents a novel linear solver technique to reduce the run-time of convex optimization solver by using the property that some parameters are fixed during the solution iterations of a solve instance. Our experimental results show that the run-time can be reduced by two orders of magnitude.
%B IEEE Embedded Systems Letters
%V 9
%P 61–64
%8 2017-05
%G eng
%U http://ieeexplore.ieee.org/document/7917357/
%N 3
%R 10.1109/LES.2017.2700401
%0 Generic
%D 2016
%T High Performance Realtime Convex Solver for Embedded Systems
%A Ichitaro Yamazaki
%A Saeid Nooshabadi
%A Stanimire Tomov
%A Jack Dongarra
%K KKT
%K Realtime embedded convex optimization solver
%X Convex optimization solvers for embedded systems find widespread use. This letter presents a novel technique to reduce the run-time of decomposition of KKT matrix for the convex optimization solver for an embedded system, by two orders of magnitude. We use the property that although the KKT matrix changes, some of its block sub-matrices are fixed during the solution iterations and the associated solving instances.
%B University of Tennessee Computer Science Technical Report
%8 2016-10
%G eng