Quality Tunable Approximate Adder for Low Energy Image Processing Applications In this paper, we present energyefficient Tunable Approximate Adders (TAAs) based on powergating. We propose two TAA designs: TAA1 and TAA2. TAAs are runtime configurable and are bittunable to support varying degrees of approximation with maximum bounded error. TAA designs have been implemented using UMC 65nm technology. On average, a singlebit TAA1 and TAA2 consume 15% and 47% lesser energy as compared to a singlebit exact mirror adder respectively. TAA1 is relatively lesser erroneous but consumes more energy as compared to TAA2. We also applied TAA1 and TAA2 on image processing applications: image addition, mean filtering and Laplacian filtering on multiple images. We have studied the tradeoff between energy consumption and output quality by varying the number of approximate bits for the aforementioned applications. For TAA1 at 4bit approximation, the average PSNR and average SSIM were 32.79 and 0.875 respectively with an energy saving of 6.67%. For TAA2 at 4bit approximation, the average PSNR and average SSIM were 28.37 and 0.88 respectively with an energy saving of 28.9%.

A New Approximate Multiplier Design for Digital Signal Processing In this paper, a new approximate multiplier is proposed, which can decrease the multiplication complexity with the improved area and power performance by using OR and AND gates. To evaluate the efficiency of the proposed approximate multiplier, design parameters are compared with exact multiplier and recently proposed approximate designs. Experiment results reveal that the power consumption of the proposed approximate multiplier is only 32% of that in the exact multiplier. The proposed approximate multipliers are further evaluated in image sharpening and edge detection applications. The peak signal to noise ratio (PSNR) and structural similarity (SSIM) of our proposed approximate multipliers show that it can be used in image processing.