A pruning technique for B & B based design exploration of approximate computing variants

Approximate Computing is revealing a new design paradigm which trades algorithms precision off for enhancing performance parameters, commonly energy consumption and computation time. Applications which are characterized by the inherent resiliency property tolerate some quality loss, w. r. t. the optimal result. The approximation is accomplished by combining substitutions of fully-precise block operations with inaccurate ones. However, exploring every possible approximate variant of an algorithm would be extremely costly due to countless configurations. IIDEAA, a design exploration tool for approximate computing algorithms, introduced a branch and bound exploration approach to make it affordable. In this paper, we enhance the IIDEAA B & B exploration approach by introducing a pruning technique, which significantly reduces the design solution space to explore. We demonstrate the effectiveness of approach by comparing the execution of approximating campaigns over some algorithms employing proposed pruning rules.