A Configurable Pruning Gaussian Image Filter for EnergyEfficient Edge Detection This paper presents a new configurable pruning Gaussian image filter CMOS architecture to address energy efficiency requirements regarding edge detection applications. Lowenergy consumption is key for Internet of Things (IoT) devices. Many emerging IoT applications rely on cameras to extract video or image features by running powerhungry computer vision algorithms. The Gaussian image filter is one of the most compute intensive tasks for preprocessing edge detection techniques which are widely adopted in the computer vision domain. Therefore, our proposed 2D Gaussian filter architecture enables: i) a low power and low area overhead runtime configuration scheme based on clock gating technique to prune the Gaussian filter (GF) window size, and ii) runtime capability to balance the tradeoff between edge detection quality and energy efficiency. Our proposed configurable architecture is synthesized and mapped onto 45 nm technology for an ASIC implementation. Results show that for 6 different runtime profiles our proposed configurable architecture provides power dissipation reduction of up to 64% with multiple levels of edge detection quality, which is assessed by considering the performance conformance metric.

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%.