This paper presents the design and implementation of a new neural-machine-interface (NMI) for control of artificial legs. The requirements of high accuracy, real-time processing, low power consumption, and mobility of the NMI place great challenges on the computation engine of the system. By utilizing the architectural features of a mobile embedded CPU, we are able to implement our decision-making algorithm, based on neuromuscular phase-dependant support vector machines (SVM), with exceptional accuracy and processing speed. To demonstrate the superiority of our NMI, real-time experiments were performed on an able bodied subject with a 20 ms window increment. The 20 ms testing yielded accuracies of 99.94% while executing our algorithm efficiently with less than 11% processor loads.