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Xilinx 28nm FPGAs and Zynq™-7000 All Programmable SoCs provide superior parallel processing capabilities, real-time performance, fast compute rates, and connectivity, unlike traditional MCUs and ASSPs that handle simple motor control and low clock rates in software diversity. In addition, Xilinx devices reduce cost through tighter programmable system integration, reduce risk and design cycles through real-time programmability, and can handle computationally intensive tasks such as PID controllers, Clark/Park conversion, and space vector PWM Features high-performance digital signal processing (DSP) to reduce latency. Xilinx All Programmable FPGAs and SoC-based solutions and platforms fully meet the demanding timing and performance requirements imposed by today's complex control algorithms such as Field Oriented Control (FOC).

OpenCV functions cannot be synthesized directly through HLS, because OpenCV functions generally involve dynamic memory allocation, floating point, and assumption that the image is stored or modified in external memory.

XQ2VP40-5FG676N_XCV100E-7BG560C looking for [Aerospace Military Industry]

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The VivadoHLS video library is used to replace many basic OpenCV functions. It has similar interfaces and algorithms to OpenCV. It is mainly aimed at image processing functions implemented in the FPGA architecture, and includes FPGA-specific optimizations, such as fixed-point operations instead of floating-point operations. (Not necessarily accurate to bits), on-chip line buffer (line buffer) and window buffer (window buffer). Figure 2.1 shows the system architecture for implementing video processing on a Xilinx Zynq AP SoC device.

Descartes High Efficiency Speech Recognition Engine (using LSTM) This is an end-to-end ASR (Automatic Speech Recognition) system provided by DeePhi that enables FPGA acceleration on AWS F1.

At the same time, Xilinx's Zynq All-programmable SoC is a good way to implement embedded computer vision applications, solving the limitations of low-power and high-power video processing on a single processor, Zynq high-performance programmable logic and embedded ARM cores , is an integrated solution for image processing with optimized performance and power consumption.

Flexible, upgradable Xilinx FPGAs allow us to rapidly respond to changing image sensor interfaces, add dedicated imaging processing capabilities, while also providing the parallel processing required for powerful, flexible, custom high-resolution image and video processing Function. Xilinx industrial imaging solutions enable rapid prototyping, simplify development and dramatically reduce time-to-market for high-resolution videoconferencing, video surveillance, and machine vision systems.

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Embedded ARM processors provide unique, critical control-plane processing capabilities to support emerging bare-metal server use cases. Standard full-featured NIC solution and driver with patented Onload™ application acceleration software reduces latency by up to 80% and improves efficiency for Transmission Control Protocol (TCP)-based server applications in cloud applications—maximum up to 400%. The U25 SmartNIC platform supports “bump-in-the-wire” seamless embedding of networking, storage, and compute offload and acceleration, which avoids unnecessary data transfers and CPU processing to maximize efficiency . Relying on Xilinx's industry-leading FPGA technology, the Alveo U25 SmartNIC platform can provide higher throughput and a more powerful adaptable engine than SoC-based NICs, enabling cloud architects to quickly create multiple types of functions and applications. Speed up. Basic NICs provide ultra-high throughput, small packet performance, and low latency. This also significantly reduces the burden on the CPU and frees up more resources to run more applications.

Finally, the functions in the rewritten OpenCV design are replaced with video functions of the corresponding functions provided by HLS, and synthesized using VivadoHLS, implemented in FPGA programmable logic or as a Zynq SoC hardware accelerator under the Xilinx development environment. Next, establish the OpenCV processing algorithm based on the video data stream chain, and rewrite the usual design of the previous OpenCV. This rewrite is to be the same as the HLS video library processing mechanism, which is convenient for the function replacement in the following steps. We use the example of fast corners to illustrate the process of implementing OpenCV with VivadoHLS. Of course, these synthesizable codes can also run on a processor or ARM. First, develop an OpenCV-based fast corner algorithm design and validate this algorithm using OpenCV-based test-inspired simulations.

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