Data clustering for efficient approximate computing

Given the saturation of single-threaded performance improvements in General-Purpose Processor, novel architectural techniques are required to meet emerging demands. In this paper, we propose a generic acceleration framework for approximate algorithms that replaces function execution by table look-up accesses in dedicated memories. A strategy based on the K-Means Clustering algorithm is used to learn mappings from arbitrary function inputs to frequently occurring outputs at compile-time. At run-time, these learned values are fetched from dedicated look-up tables and the best result is selected using the Nearest-Centroid Classifier, which is implemented in hardware. The proposed approach improves over the state-of-the-art neural acceleration solution, with nearly 3X times better performance, \(18.72\%\) up to \(90.99\%\) energy reductions and \(17\%\) area savings under similar levels of quality, thus opening new opportunities for performance harvesting in approximate accelerators.