Abstract
Image processing is widely used in many applications, including medical imaging, industrial manufacturing and security systems. In these applications, the size of the image is often very large, the processing time should be very small and the real-time constraints should be met. Therefore, during the last decades, there has been an increasing demand to exploit parallelism in applications. It is possible to explore parallelism along three axes: data-level parallelism (DLP), instruction-level parallelism (ILP) and task-level parallelism (TLP).
This paper explores the limitations and bottlenecks of increasing support for parallelism along the DLP and ILP axes in isolation and in combination. To scrutinize the effect of DLP and ILP in our architecture (template), an area model based on the number of ALUs (ILP) and the number of processing elements (DLP) in the template is defined, as well as a performance model. Based on these models and the template, a set of kernels of image processing applications has been studied to find Pareto optimal architectures in terms of area and number of cycles via multi-objective optimization.
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Fatemi, H., Corporaal, H., Basten, T., Kleihorst, R., Jonker, P. (2005). Designing Area and Performance Constrained SIMD/VLIW Image Processing Architectures. In: Blanc-Talon, J., Philips, W., Popescu, D., Scheunders, P. (eds) Advanced Concepts for Intelligent Vision Systems. ACIVS 2005. Lecture Notes in Computer Science, vol 3708. Springer, Berlin, Heidelberg. https://doi.org/10.1007/11558484_87
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DOI: https://doi.org/10.1007/11558484_87
Publisher Name: Springer, Berlin, Heidelberg
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