A parallel hardware architecture for information-theoretic adaptive filtering

Information-theoretic cost functions such as minimization of the error entropy (MEE) can extract more structure from the error signal, yielding better results in many realistic problems. However, adaptive filters (AFs) using MEE methods are more computationally intensive when compared to conventional, mean-squared error (MSE) methods employed in the well-known, least mean squares (LMS) algorithm. This paper presents a novel, parallel hardware architecture for MEE adaptive filtering. The design has been implemented and evaluated in realtime on one of the servers of the Novo-G machine in the NSF CHREC Center at the University of Florida, believed to be the most powerful reconfigurable supercomputer in academia. By pipelining the design and parallelizing independent computations within the algorithm, our proposed hardware architecture successfully achieves a speedup of 5800 on one FPGA, 23200 on one quad-FPGA board, and 46400 on two quad-FPGA boards, as compared to the same algorithm running in software (optimized C program) on a single CPU core. Just as important, our results show that this reconfigurable design does not lose precision while converging to the optimum solution in the same number of steps as the software version. As a result, our approach makes it possible for AFs using the MEE cost function to adapt in real-time for signals that require a sampling rate in excess of 400 kHz and thus can target a much wider range of applications.