We present a novel method for controlling the complexity of real-time signal processing computational tasks, in order to make sure that a total quality metric for all the signal processing tasks is maximized. The method makes decisions about how much compute power is allocated to each task through past observations of the input and output data of each task. We present preliminary results from filtering applications that demonstrate the ability of the system to maximize the total quality of a large number of tasks under a real-time computational constraint.
['Joseph Yeh', 'John Wawrzynek']
Quality based compute-resource allocation in real-time signal processing