Abstract
This paper introduces and assesses a new technique to minimize the memory footprints of Digital Signal Processing (DSP) applications specified with Synchronous Dataflow (SDF) graphs and implemented on shared-memory Multiprocessor Systems-on-Chips (MPSoCs). In addition to the SDF specification, which captures data dependencies between coarse-grained tasks called actors, the proposed technique relies on two optional inputs abstracting the internal data dependencies of actors: annotations of the ports of SDF actors, and script-based specifications of merging opportunities between input and output buffers of actors. An automated optimization process is used to exploit these buffer merging opportunities and to minimize the memory footprints of applications. Experimental results on a computer vision application show a reduction of the memory footprint by 34% compared to state-of-the-art minimization techniques.
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