Error-Efficient Approximate Multiplier Design using Rounding Based Approach for Image Smoothing Application

We propose a novel, error-efficient approximate multiplier (EEAM), which is based on a rounding-based approach (RBA). Multiplication is performed using rounding, shift, and add operations. We round the input operands to the nearest power of two using RBA. The modified inputs are processed by an arithmetic block (AB), which consists of addition, subtraction, and shifter blocks. The proposed approximate multiplier has input operands whose widths range from 8-bit to 32-bits. We simulated the proposed multiplier by using Vivado and MATLAB. The proposed multiplier is also synthesized using the Cadence RTL compiler, and compared to prior approximate multiplier proposals, EEAM’s delay and energy consumption are about of 22% and 57% better than the best known approximate multipliers. We also show that the proposed approximate multiplier’s worst-case error, mean error distance, mean relative error distance, and normalized error distance are about 3%, 44%, 45%, and 13% improvement over existing approximate multipliers. Finally, we used the proposed approximate multiplier in an image smoothing filter., For this application, we observed that our multiplier provides higher PSNR and SSIM than any prior approximate multiplier.