Real-Time Signal Processing of Massive Sensor Arrays via a Parallel Fast Converging SVD Algorithm: Latency, Throughput, and Resource Analysis

This paper introduces a parallel fast converging Jacobi-like singular value decomposition (SVD) algorithm applicable to real-time signal processing of massive sensor arrays. The proposed algorithm highly increases the SVD convergence rate for larger matrices when compared with traditional Jacobi-based methods. A highly modular system design is proposed, which retains the parallel nature of the Jacobi methods key to real-time implementation intended for field programmable gated arrays (FPGAs). The proof of design was provided via an implementation on Virtex-6 FPGA, and the improvement in performance was verified via simulations. The proposed design was compared with the traditional design in terms of FPGA resource consumption, maximum achievable frequency, and latency throughput tradeoff.