RMLIM: A Runtime Machine Learning Based Identification Model for Approximate Computing on Data Flow Graphs

Approximate computing (AC) is an effective energy-efficient method for applications that have intrinsic error resilience. Early research efforts select noncritical portion of the computation, operations that have little impact on the accuracy of the results, for approximation. They ignore the runtime information and result in either under-approximation, which fails to reach the full potential of energy saving, or over-approximation, which causes unacceptable errors in the computation. A recently proposed runtime approach first estimates the noncritical portion for given input values and then performs accurate computation only on the critical portion. However, its complicated estimation process brings large runtime overhead and may not be suitable for real time embedded software. In this paper, we solve this problem by proposing a Runtime Machine Learning based Identification Model (RMLIM) to locate the noncritical portion in the data flow graph representation of any software program. RMLIM is trained offline by generated training data set and then applied at runtime for each input. This reduces the runtime complexity of identifying noncritical parts. Our experiments show that, compared with the existing runtime AC method, our machine learning based approach can maintain similar energy efficiency and computation accuracy, but reduces the execution time by 40 percent–61 percent.