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.

The SCORE compute model uses fixed-size, virtual compute and memory pages connected by stream links to capture the definition ofa computation abstracted from the detailed size ofthe physical hardware. When the number of physical compute pages is smaller than the number of virtual compute pages in the abstract computation graph, the design is time-multiplexed onto the available physical hardware. A key component ofthis strategy is an automatic scheduler that selects the temporal sequencing ofvirtual resources onto the physical device. We describe a quasistatic scheduling strategy that retains the full semantic power of the dynamic SCORE flow graph while taking advantage ofstatic scheduling techniques at program load time to hoist most ofthe computational work out of the inner scheduling loops. This strategy reduces online scheduling work per reconfiguration epoch by an order of magnitude. In addition, a more global perspective available from offline-scheduling improves schedule quality, resulting in a net reduction oftotal execution time by 46–81%. 1.