Design of high-performance, accurate, and approximate Dadda-tree multipliers for image processing applications

Approximate computing comes to the fore as an alternative paradigm to enhance efficiency in computing systems by trading off the system's accuracy for better performance. This paper seeks to leverage the principles of approximate computing to design efficient multiplier architectures for FPGA platforms. Specifically, this work presents FPGA implementations of one accurate and two approximate multiplier units based on the Dadda algorithm. The multipliers employ a novel partial product reduction technique that minimizes the utilized resources and the critical path delay, offering a more resource-efficient alternative than traditional multipliers. Our accurate and best-performing approximate 8 × 8 multiplier shows an improvement of 28% and 37% in PDAP over the Xilinx exact multiplier and the most performance-efficient existing approximate multiplier, respectively. Further evaluation based on the processing of images with different modalities shows a substantial improvement in PSNR over the existing approximate multipliers, especially in the healthcare domain, thereby highlighting the possible application of the proposed multipliers in error-resilient medical imaging tasks.