Biased Compressor Based Approximate Multiplier Design Using Genetic Algorithm

Multiplication is the cornerstone in a vast array of applications. The employment of approximate multipliers offers a significant advantage in improving system performance. In this work, we conduct an in-depth and comprehensive analysis of various compressors, analyze their error distributions and propose a set of compressors with biased errors. By configuring heterogeneous compressors via a genetic algorithm and optimizing the input order of compressors, we propose several multipliers. Experimental results show that these multipliers have lower power, delay and area consumption compared with the state-of-the-art approximate multipliers. Notably, the AM_4 multiplier, without reducing the accuracy in image recognition and with an image blending PSNR exceeding 50, has reduced the area overhead by 27.3%, power consumption by 49.2%, and delay by 15.2% compared to the precise multiplier.