Memory-constrained Block Processing for DSP Software Optimization

Digital signal processing (DSP) applications involve processing long streams of input data. It is important to take into account this form of processing when implementing embedded software for DSP systems. Task-level vectorization, or block processing, is a useful dataflow graph transformation that can significantly improve execution performance by allowing subsequences of data items to be processed through individual task invocations. In this way, several benefits can be obtained, including reduced context switch overhead, increased memory locality, improved utilization of processor pipelines, and use of more efficient DSP oriented addressing modes. On the other hand, block processing generally results in increased memory requirements since it effectively increases the sizes of the input and output values associated with processing tasks. In this paper, we investigate the memory-performance trade-off associated with block processing. We develop novel block processing algorithms that c...

• Type: Journal
• Year: 2008
• Where: VLSISP
• Authors: Ming-Yung Ko, Chung-Ching Shen, Shuvra S. Bhattacharyya
• Keywords: Block Processing, Dataflow Graph Transformation, Individual Task Invocations, VLSISP 2008