Scalable parallel architecture for singular value decomposition of large matrices

Abstract

Singular Value Decomposition (SVD) is a key linear algebraic operation in many scientific and engineering applications, many of them involving high dimensionality datasets and real-time response. In this paper we describe a scalable parallel processing architecture for accelerating the SVD of large m × n matrices. Based on a linear array of simple processing-units (PUs), the proposed architecture follows a double data-flow paradigm (FIFO memories and a shared-bus) for optimizing the time spent in data transferences. The PUs, which perform elemental column-pair evaluations and rotations, have been designed for an efficient utilization of available FPGA resources and to achieve maximum algorithm speed-ups. The architecture is fully scalable from a two-PU scheme to an arrangement with as many as n/2 PUs. This allows for a trade-off between occupied area and processing acceleration in the final implementation, and permits the SVD processor to be implemented both on low-cost and high-end FPGAs. The system has been prototyped on Spartan-6 and Kintex-7 devices for performance comparison.