A complexity effective communication model for behavioral modeling of signal processing applications

Abstract

In this paper, we argue that the address space of memory regions that participate in inter task communication is over-specified by the traditional communication models used in behavioral modeling, resulting in sub-optimal implementations. We propose shared messaging communication model and the associated channels for efficient inter task communication of high bandwidth data streams in behavioral models of signal processing applications. In shared messaging model, tasks communicate data through special memory regions whose address space is unspecified by the model without introducing non determinism. Address space to these regions can be assigned during mapping of application to specific architecture, by exploring feasible alternatives. We present experimental results to show that this flexibility reduces the complexity (e.g., communication latency, memory usage) of implementations significantly (up to an order of magnitude).