Multiresolution Analysis (MRA) is a mathematical method that is based on working on a problem at different scales. One of its applications is medical imaging where processing at multiple scales, based on the concept of Gaussian and Laplacian image pyramids, is a well-known technique. It is often applied to reduce noise while preserving image detail on different levels of granularity without modifying the filter kernel. In scientific computing, multigrid methods are a popular choice, as they are asymptotically optimal solvers for elliptic Partial Differential Equations (PDEs). As such algorithms have a very high computational complexity that would overwhelm CPUs in the presence of real-time constraints, application-specific processors come into consideration for implementation. Despite of huge advancements in leveraging productivity in the respective fields, designers are still required to have detailed knowledge about coding techniques and the targeted architecture to achieve efficient solutions. Recently, the HIPAcc framework was proposed as a means for automatic code generation of image processing algorithms, based on a Domain-Specific Language (DSL). From the same code base, it is possible to generate code for efficient implementations on several accelerator technologies including different types of Graphics Processing Units (GPUs) as well as reconfigurable logic (FPGAs). In this work, we demonstrate the ability of HIPAcc to generate code for the implementation of multiresolution applications on FPGAs and embedded GPUs.

这篇论文阐述了利用基于高斯和拉普拉斯图像金字塔概念的多尺度处理来减少噪声，同时在不改变滤波器核的情况下保留不同粒度的图像细节的数学方法Multiresolution Analysis以及应用于医学成像方面。 该论文探讨了如何利用自动代码生成框架HIPAcc在多种加速器技术上生成用于FPGAs和嵌入式GPU的多分辨率应用程序的代码。

高层次综合多分辨率应用在FPGA的代码生成