Abstract

Digital signal processing (DSP) systems are widely used in communication, medical, sonar, radar, equipment health monitoring and many other applications. Frequently, the signal processing system has to meet real-time requirements and provide very large throughput. For example, modern automatic target recognition systems operate with a processing throughput in excess of 10 Gflop per second. In real-time vibration analysis used for turbine engine testing [1], the aggregate sustained computation rate is also in the Gflop range. The high performance requires the use of computing platforms that include the combination of dedicated hardware processors, and general-purpose computers forming a hybrid, parallel/distributed configuration. Complexity, heterogeneity of the computing environment, and real-time operation make the software development for digital signal processing difficult and expensive. The design of DSP systems is based on the available a priori information about the signal source and the noise in the environment. Necessarily, the performance of the implemented system largely depends on the environmental conditions as well as additional factors, such as the computational resources available to the task. In conventional design approaches, these conditions are typically set in design time by introducing various constraints, simplifications, and assumptions. The critical issue in this methodology is what happens if the design time assumptions do not hold? Stabilization of the environment is impossible in

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