With the advent of the next generation of video compression standards, the industry has shifted from basic video processing to more complex integrated processing solutions, which has allowed the system's requirements to exceed the video performance of independent DSPs. FPGAs offer DSP performance above 20GMACs for less than $ 30, which fills this gap for cost-sensitive military, automotive, medical, consumer, industrial, and security applications. Only FPGAs can provide logic, embedded processing, OS support and drivers for the complete end-to-end video solution.
The factor that hinders developers from using FPGAs for video applications is not their lack of understanding of FPGA performance advantages, but the lack of experience using their design flow, especially for developers of traditional DSP programs who are used to programming in C.
Developers can use the flexibility of FPGAs to configure hardware architectures optimized for specific applications to take advantage of the device's performance. This flexibility adds freedom to the development process and also promotes its complexity.
XtremeDSP Video Starter Kit (VSK) provides a complete and easy-to-use design environment. This development kit includes application examples and fully supports the standard tool flow, which helps speed up the design process and still enable differentiation of the final product.
Use the basic platform to develop video applications
The embedded system called the basic platform provides a framework from which you can use VSK to develop video applications. The basic platform is an embedded system created using Xilinx Platform Studio's Base System Builder (BSB), which includes a MicroBlaze embedded processor.
This framework can provide a starting point for new designs and can also be used as a convenient port to port existing applications developed on processor-based systems. On the MicroBlaze processor, you can easily recompile any C code for the external processor; once the high-performance video chain is connected, it can be ported from the software to the FPGA architecture.
To assist with this migration, VSK includes a custom peripheral IP library that can be easily added to the base system using Platform Studio, and can also be connected to a video interface, manage data frames, and perform memory access and basic video deal with. These custom peripherals include:
DVI input
DVI output
camera
Video Frame Buffer Controller (VFBC)
Video processing pipeline
This VFBC is ideal for video applications that require hardware control of two-dimensional data to achieve real-time operation.
Quickly start the development process with VSK reference design
VSK provides three reference designs to quickly start the development process of video applications running on FPGAs. Each reference design is built on a basic platform and uses custom peripherals from VSK's IP library. Table 1 lists each reference design and the video processing and connection functions it displays. These reference designs are intended to provide a starting point that can be further developed on this basis.
Create video applications with model-based design
To accelerate video applications on FPGAs, you need to migrate performance-critical operations from software running on the processor to hardware. VSK supports a variety of hardware design processes, including the process of using VHDL / Verilog to play a solid hardware design background, and also includes the use of more abstract modeling environments (including C, MATLAB, and Simulink) that require little or no hardware. Design experience flow.
The MathWorks Simulink is a model-based design environment that can be used to develop algorithm models for video systems. The MathWorks provides Simulink with an optional set of video and imaging modules, including a rich set of video building blocks that can be used to conveniently process streaming video and display results at each step in the model.
You can first use floating-point data types and high-level video and imaging modules to establish abstract patterns for the video processing algorithm itself, and then optimize the algorithm in a way that the designer believes can balance complexity, system cost, and performance.
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