An Efficient Approximate Sum of Absolute Differences Hardware for FPGAs

Source : Verilog HDL

Proposed Abstract:

Sum of Absolute Differences (SAD) is mainly applied in block-matching tasks such as motion estimation for video compression, stereo matching for depth/disparity calculation, template matching in image/object detection, image registration (including medical imaging), and lightweight optical-flow/tracking systems, because it is simple, fast, and hardware-friendly. The Traditional accurate SAD hardware provides exact results but consumes high power and requires large area, while existing approximate designs reduce cost but often suffer from high errors and poor FPGA-specific optimization. To overcome these limitations, this work proposes an improved SAD hardware architecture that replaces the conventional full adder with a lightweight XOR–MUX structure. This change reduces delay, minimizes area, and increases speed by removing redundant logic and optimizing FPGA resource utilization. The novelty of the design lies in combining approximation with FPGA-aware optimization, achieving bounded error, reduced power consumption, and higher operating frequency. The proposed system is implemented in Verilog HDL and tested on a Xilinx FPGA, showing improvements in LUT usage, clock frequency, and power efficiency, making it suitable for real-time video and image processing applications.

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