Hardware-efficient QR-decomposition using bivariate numeric function approximation

Abstract

Bivariate function approximation has proven its feasibility in terms of hardware-efficient arithmetic signal processing. However, its impact on high performance QR decomposition (QRD) has only been roughly studied so far. In this paper, a novel hardware architecture for Givens-Rotation-based QRD is proposed targeting hardware efficient signal processing. To this end, an ingenious triangular systolic array structure is considered. Complex-valued matrices are efficiently processed by means of a sophisticated bivariate numeric function approximation methodology. In order to get a comprehensive insight in the performance, exhaustive evaluation is carried out with a modern multi-antenna wireless communication system. In detail, the proposed QRD hardware architecture is used in a suitable channel pre-coding setup. For a meaningful proof-of-concept, our work is evaluated on several levels of the computing stack. In addition, our design is implemented and physically synthesized in a state-of-the-art 65-nm Taiwan Semiconductor Manufacturing Company technology and compared with other publications. The results indicate our approach to be a powerful solution for hardware-based QRD, especially in terms of energy and area requirements.