Truncation and Rounding-Based Scalable Approximate Multiplier Design for Computer Imaging Applications

Abstract

Advanced technology used for arithmetic computing application, comprises greater number of approximate multipliers and approximate adders. Truncation and Rounding-based Scalable Approximate Multiplier (TRSAM) distinguish a variety of modes based on height (h) and truncation (t) as TRSAM (h, t) in the architecture. This TRSAM operation produces higher absolute error in Least Significant Bit (LSB) data shift unit. A new scalable approximate multiplier approach that uses truncation and rounding TRSAM (3, 7) is proposed to increase the multiplier accuracy. With the help of foremost one bit architecture, the proposed scalable approximate multiplier approach reduces the partial products. The proposed approximate TRSAM multiplier architecture gives better results in terms of area, delay, and power. The accuracy of 95.2% and the energy utilization of 24.6 nJ is observed in the proposed multiplier design. The proposed approach shows 0.11%, 0.23%, and 0.24% less Mean Absolute Relative Error (MARE) when compared with the existing approach for the input of 8-bit, 16-bit, and 32-bit respectively. It also shows 0.13%, 0.19%, and 0.2% less Variance of Absolute Relative Error (VARE) when compared with the existing approach for the input of 8-bit, 16-bit, and 32-bit respectively. The proposed approach is implemented with Field-Programmable Gate Array (FPGA) and shows the delay of 3.640, 6.481, 12.505, 22.572, and 36.893 ns for the input of 8-bit, 16-bit, 32-bit, 64-bit, and 128-bit respectively. The proposed approach is applied in digital filters design which shows the Peak-Signal-to-Noise Ratio (PSNR) of 25.05 dB and Structural Similarity Index Measure (SSIM) of 0.98 with 393 pJ energy consumptions when used in image application. The proposed approach is simulated with Xilinx and MATLAB and implemented with FPGA.