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Multiresolution Analysis (MRA) is a mathematical method that is based onworking on a problem at different scales. One of its applications is medicalimaging where processing at multiple scales, based on the concept of Gaussianand Laplacian image pyramids, is a well-known technique. It is often applied toreduce noise while preserving image detail on different levels of granularitywithout modifying the filter kernel. In scientific computing, multigrid methodsare a popular choice, as they are asymptotically optimal solvers for ellipticPartial Differential Equations (PDEs). As such algorithms have a very highcomputational complexity that would overwhelm CPUs in the presence of real-timeconstraints, application-specific processors come into consideration forimplementation. Despite of huge advancements in leveraging productivity in therespective fields, designers are still required to have detailed knowledgeabout coding techniques and the targeted architecture to achieve efficientsolutions. Recently, the HIPAcc framework was proposed as a means for automaticcode generation of image processing algorithms, based on a Domain-SpecificLanguage (DSL). From the same code base, it is possible to generate code forefficient implementations on several accelerator technologies includingdifferent types of Graphics Processing Units (GPUs) as well as reconfigurablelogic (FPGAs). In this work, we demonstrate the ability of HIPAcc to generatecode for the implementation of multiresolution applications on FPGAs andembedded GPUs.

Code Generation for High-Level Synthesis of Multiresolution Applications on FPGAs