ImprLM: An Improved Logarithmic Multiplier Design Approach via Iterative Linear-Compensation and Modified Dynamic Segment

In this paper, we present ImprLM, an improved logarithmic multiplier design approach via iterative linear-compensation and dynamic segment. Firstly, we have optimized the computational flow of the Mitchell-based logarithmic algorithm to devise a more concise and compact logarithmic multiplier. Subsequently, we introduce an innovative iterative linear error compensation technique, which significantly improves the precision of the logarithmic multiplier compared with the cutting-edge Look-Up Table (LUT) based error compensation strategies. For the purpose of achieving additional logic savings, we introduce modified dynamic segment methods for intermediate variables during multiplication. The synthesis results demonstrate that ImprLM can achieve up to a 22.5% reduction in the power-delay product, while simultaneously enhancing accuracy by 26.8% compared to the state-of-the-art Mitchell-based logarithmic multipliers. Furthermore, we incorporated ImprLM into image processing and the results reveal that ImprLM has a negligible effect on the output quality, while maintaining a low resource consumption.