Abstract—Complex software programs are mostly characterized by phase behavior and runtime distributions. Due to the dynamism of the two characteristics, it is not efficient to make workload predictions during design-time. In our work, we present a novel online DVFS method that exploits both phase behavior and runtime distribution during runtime in combined Vdd/Vbb scaling. The presented method performs a bi-modal analysis of runtime distribution, and then a runtime distribution-aware workload prediction based on the analysis. In order to minimize the runtime overhead of the sophisticated workload prediction method, it performs table lookups to the pre-characterized data during runtime without compromising the quality of energy reduction. It also offers a new concept of program phase suitable for DVFS. Experiments show the effectiveness of the presented method in the case of H.264 decoder with two sets of long-term scenarios consisting of total 4655 frames. It offers 6.6% ∼ 33.5% r...