XQ Kintex UltraScale_XC6VLX550T-2FF1759I Introduction

Xilinx devices enable high power efficiency for all product portfolios, including Spartan-6 series and 7 series, UltraScale™ and UltraScale+™ FPGAs and SoCs, through select silicon processes and power architectures. With each product generation, Xilinx continues to enhance its power-saving features, including process improvements, architectural innovations, voltage scaling strategies, and advanced software optimization strategies. Power estimates, thermal models, full software support and demo boards are now publicly available for all product families. Below are details on specific product portfolio capabilities, silicon process advantages and benchmark comparisons.

On the other hand, AMD and Xilinx have been working closely together for a long time. A series of storage system-oriented IPs such as NVMe HA, NVMe TC and Embedded RDMA previously provided for AMD EPYC (Xiaolong) data center processors can help AMD build low latency The high-efficiency data path, thus realizing the efficient storage acceleration function of FPGA. In fact, a similar plot was staged as early as 2015, when Intel (Intel) acquired FPGA manufacturer Altera for $16.7 billion, and Altera also followed the trend for Intel's follow-up "CPU+xPU (GPU+FPGA+ASIC+ eASIC)” strategy provides the most solid foundation.

XQ Kintex UltraScale_XC6VLX550T-2FF1759I

XC6VLX75T-1FF784C

A total of 4 CPUs were released this time, namely Ryzen9 5950X, Ryzen9 5900X, Ryzen7 5800X and Ryzen5 5600X. Since AMD launched the Ryzen 4000 series notebook platform APU processors at CES in January this year, in order to facilitate consumers to identify and search, the Zen 3 architecture processor series was directly named the 5000 series.

The total dynamic power consumption of an FPGA is the combined power consumption from charging all capacitive nodes. Components of Power Consumption The power consumption of an FPGA consists of two components: dynamic power and static power. These capacitive nodes can be internal logic blocks, routing wires in an interconnect fabric, external package pins, or board-level traces driven by chip outputs. Dynamic power dissipation occurs when the signal charges the capacitive node.

AMD’s stock price has soared 89% this year, and its market value has now exceeded $100 billion to $101.568 billion. In response to AMD's acquisition of Xilinx, the Wall Street Journal analyzed that AMD may use its high stock valuation as a bargaining chip to promote the transaction or delist Xilinx at a high price.

Text is one of mankind's most intelligent and influential creations. The rich and precise high-level semantics contained in text can help us understand the world around us and be used to build autonomous solutions that can be deployed in real-world environments. Therefore, automatic text reading in natural environments, also known as scene text detection/recognition or Photo OCR (Optical Character Recognition), has become a research topic of increasing interest and importance in the field of computer vision.

XQ Kintex UltraScale_XC6VLX550T-2FF1759I

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XQ Kintex UltraScale_XC6VLX550T-2FF1759I

While the CvMat and IplImage types are more focused on "images", OpenCV is optimized for image operations (scaling, single-channel extraction, image thresholding, etc.) The common data containers related to image operations in OpenCV are Mat, CvMat and IplImage. These three types can represent and display images. However, the Mat type focuses on calculation and is highly mathematical.

Of course, these synthesizable codes can also run on a processor or ARM. 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. 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. First, develop an OpenCV-based fast corner algorithm design and validate this algorithm using OpenCV-based test-inspired simulations. We use the example of fast corners to illustrate the process of implementing OpenCV with VivadoHLS.