A Machine Learning based Approximate Computing Approach on Data Flow Graphs: Work-in-Progress

Abstract

We report our ongoing work towards a machine learning based runtime approximate computing (AC) approach that can be applied on the data flow graph representation of any software program. This approach can utilize runtime inputs together with prior information of the software to identify and approximate the noncritical portion of a computation with low runtime overhead. Some preliminary experimental results show that compared with previous runtime AC approaches, our approach can significantly reduce the time overhead with little loss on the energy efficiency and computation accuracy.