Stage-oriented, Mixed Design Methodology for Image Processing Using VHDL and Python

Marcin Chojnacki, P. Sekalski
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Abstract

The data analysis could be a very time consuming process during the hardware design on FPGA platforms. The verification process of designed modules is important at each step of a design process. In some cases standard methods for data analysis are insufficient, especially when the data representation are taken into assessment methods. In image processing systems based on FPGA there are various methods to support engineers in development of desired architecture. Some of them are based on scoping hardware signals in running device. It is also possible to scope signals in a simulation environment. In addition there are also high-level abstraction layer of data analysis methods based on Matlab, Python and similar tools. The unique image processing architecture developed by authors could not be upgraded with support of existing co-design methods. This is why stage-oriented, mixed design methodology was performed to support FPGA hardware development for faster prototyping and debugging with Vivado simulator tool and Python language. Presented approach was used to improve image processing design operating with ultra high resolution images (from 5 Mpix up to 70 Mpix).
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基于VHDL和Python的面向阶段、混合设计的图像处理方法
在FPGA平台上进行硬件设计时,数据分析是一个非常耗时的过程。在设计过程的每个步骤中,设计模块的验证过程都很重要。在某些情况下,数据分析的标准方法是不够的,特别是当将数据表示纳入评估方法时。在基于FPGA的图像处理系统中,有各种方法来支持工程师开发所需的体系结构。其中一些是基于运行设备中的硬件信号范围。在模拟环境中也可以确定信号的范围。此外,还有基于Matlab、Python等工具的数据分析方法的高级抽象层。作者开发的独特的图像处理架构无法在现有协同设计方法的支持下进行升级。这就是为什么执行面向阶段的混合设计方法来支持FPGA硬件开发,以便更快地使用Vivado模拟器工具和Python语言进行原型设计和调试。该方法被用于改进超高分辨率图像(从500万像素到70万像素)的图像处理设计。
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