Exploring the Effectiveness of Sigma-Delta Modulators in Stochastic Computing-Based FIR Filtering

Abstract

A soft-filtering processing architecture based on Sigma-Delta Modulation and Stochastic Computing is proposed. It converts a high-resolution signal using a first order digital Sigma-Delta Modulator into a single-bit one and then exploits Stochastic Computing's encoding to perform area-efficient multiplications. The Sigma-Delta Modulator allows for the input signal to be oversampled at a much higher frequency rate, offering improved performance in terms of SNR, which is not possible with standard Stochastic Computing filter realizations. Spectral simulations results demonstrate the proper signal quantization and operation of the filter, including the filter's roll-off behavior. FPGA synthesis results of the proposed architecture, illustrate its area advantages in comparison to conventional binary filtering.