Modular Posit Multipliers: Enhancing Computational Efficiency in Image Processing

The Posit numbering system, an alternative to IEEE floating-point arithmetic, offers significant advantages in deep learning applications due to its efficient handling of non-uniform data distributions. This characteristic makes it particularly suitable for image processing tasks where traditional floatingpoint representations may be less effective. However, the flexible bit-width of Posit numbers introduces challenges in hardware design, particularly when implementing multipliers that must accommodate varying mantissa widths. This often results in high energy consumption, especially when only a small mantissa width is required. To address this issue, we propose a novel Posit multiplier architecture designed for energy efficiency. The core multiplier is built to support the maximum mantissa bit-width but is divided into smaller, modular units. During computation, only the necessary units are activated based on the regime bit-width, which determines the actual mantissa width required for the task. This approach not only reduces power consumption but also maintains high computational efficiency and precision, making it well-suited for image processing applications.