Implementation of Dynamic SISD-SIMD Integer Dividers

Digital division is a pivotal operation in a number of domains, such as scientific computation, cryptography, digital signal processing, and machine learning, and has been broadly researched in the past decades. However, the increasing demand for efficient digital systems has emphasized the design of digital dividers with more stringent area and power costs without excessively compromising their performance. One option to achieve this is to implement Single Instruction Multiple Data (SIMD) dividers capable of exploiting the abundance of data-level parallelism, which is naturally present in common tasks such as stream processing, matrix normalization, and vector scaling. In this paper, we propose the designs of four configurable integer dividers capable of sustaining both Single Instruction Single Data (SISD) and SIMD computations, targeting different levels of performance and efficiency. Compared to one of the state-of-the-art integer dividers, our most efficient SIMD divider shows a 54% smaller area and 30% lower power consumption. Furthermore, we propose a novel technique which can reduce n-bit divisions to smaller (n−m)-bit ones, yielding a peek speedup of 15% when implemented in one of our proposed dividers.