Parallelization of embedded software is often desirable for power/performance-related considerations for computation-intensive applications that frequently occur in the signal-processing domain. Although hardware support for parallel computation is increasingly available in embedded processing platforms, there is a distinct lack of effective software support. One of the most widely known efforts in support of parallel software is the message passing interface (MPI). However, MPI suffers from several drawbacks with regards to customization to specialized parallel processing contexts, and performance degradation for communication-intensive applications. In this paper, we propose a new interface, the signal passing interface (SPI), that is targeted toward signal processing applications and addresses the limitations of MPI for this important domain of embedded software by integrating relevant properties of MPI and coarse-grain dataflow modeling. SPI is much easier and more intuitive to use, and due to its careful specialization, more performance-efficient for the targeted application domain. We present our preliminary version of SPI, along with experiments using SPI on a practical face detection system that demonstrate the capabilities of SPI.
['Sankalita Saha', 'Shuvra S. Bhattacharyya', 'Wayne H. Wolf']
A Communication Interface for Multiprocessor Signal Processing Systems