An Efficient FIR Filter Design using DA based Speculative Residue and Reverse Computation RNS system

Abstract

Finite impulse response (FIR) filter is prominently used in many digital signal processing (DSP) systems for various applications. In this paper, we present high-performance RNS based finite impulse response (FIR) filters design for ECG signal classification. In general, the residue number system (RNS) gives significant metrics over FIR implementation with its inherent parallelism and data partitioning mechanism. But increased bit width cause considerable performance trade-off due to its residue computation and the reverse conversion. In this paper optimized Residue Number System (RNS) arithmetic is proposed which includes distributed arithmetic based residue computation during RNS multiplication followed by speculative delay optimized reverse computation to mitigate the FIR filter trade-off characteristics with filter length. The proposed RNS design utilizes built-in block RAMs available in FPGA devices to accomplish the reverse conversion process. A distinctive feature of our FIR filter implementation with core optimized RNS is to minimize hardware complexity overhead with the improved operating speed. Initially fetal ECG signal detection is carried out to validate the functionality of FIR filter core and FPGA hardware synthesis is carried out for various input word size and FIR length. From the experimental, it is proved that the trade-off exists in conventional RNS FIR over filter length is narrow down along with considerable complexity reduction with our proposed optimized RNS system