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Publikation

DIVIAC: Library of Input Data Aware Approximate Dividers with Partial Exact Minimization

Chandan Jha; Sallar Ahmadi-Pour; Sajjad Parvin; Rolf Drechsler
In: 39th International Conference On VLSI Design. International Conference on VLSI Design (VLSID-2026), January 3-7, Pune, India, 2026.

Zusammenfassung

Approximate divider designs have been extensively explored as they are widely used in image processing applications. The design of approximate dividers is predominantly accomplished through the use of functional approximation, where the Boolean functions of the subtractor sub-blocks in the dividers are approximated by replacing them with a different Boolean function. However, the prior works have explored only a few Boolean approximations and evaluated the error metrics using uniform distributions. This does not effectively explore the design space and leads to suboptimal approximate divider designs. In this work, we alleviate the limitations of prior works as follows: Firstly, we perform an extensive and systematic design space exploration to identify the Pareto-optimal approximate divider designs, where each sub-block has been reduced through exact minimization. Secondly, we do this for three input distributions, namely uniform, normal, and exponential distributions. Lastly, we also evaluate the design on the widely used image processing applications for approximate dividers, namely background removal and change detection. We aim to make the Pareto-optimal approximate divider designs available as open-source to stimulate further research.