Implementation of 3-D multiple linear regression in hardware using the xilinx spartan-3AN FPGA

In this paper, a linear regression algorithm implementation in hardware using the Field Programmable Gate Array (FPGA) is presented. A two-dimensional (2-D) simple linear regression aims to develop a linear equation of two variables based on observed data values in two dimensions. A more complex problem that this considered in this paper is the three-dimensional (3-D) multiple linear regression that approximates a linear equation of three variables based on a set of observed data points in three dimensions. The algorithm was initially modelled and verified using Python, NumPy and Matplotlib. The linear regression equation was then translated to hardware using a VHDL description of the algorithm targeting the Xilinx Spartan-3AN FPGA. In this paper, the design and simulation of the algorithm based on using the available hardware resources within the FPGA are introduced and discussed.

Palavras-chave

FPGA, Hardware, Linear regression, Machine learning, VHDL

Idioma

Inglês

Citação

Proceedings of the 16th International Conference on Electrical Engineering/Electronics, Computer, Telecommunications and Information Technology, ECTI-CON 2019, p. 171-174.