CRRS: Custom Regression and Regularisation Solver for Large-Scale Linear Systems

This paper presents novel regression and regularisation techniques based on Field Programmable Gate Array (FPGA) technology for large-scale datasets for machine learning and other applications. We introduce a customisable design which allows end-users to select their regression and regularisation techniques from a library supporting relevant methods such as Multiple Linear Regression, Ridge Regression, Adaptive/Lasso Regression and Elastic Net Regularisation. We introduce the first Adaptive Elastic Net architecture for FPGAs. Tests on dense and sparse datasets of varying sizes show 158 times speedup and 114 times enhancement in energy efficiency when comparing an 8-FPGA system with the corresponding software C++ implementation on a 12-core CPU, for an Adaptive Elastic Net regularisation of a matrix with 11.56*10^9 coefficients.