XC7K480T-2FF1156C_XC7Z100-FFG900ACX Introduction

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.

Xilinx, which has a market value of about $26 billion, has gained about 9 percent this year, slightly ahead of the S&P 500's 7 percent gain. AMD's stock price has risen 89% this year, and its current market value exceeds $100 billion, thanks to the new crown epidemic working from home to increase market demand for PCs, game consoles and other devices that use AMD chips. .

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However, in the third quarter, the demand for the semiconductor market has recovered significantly, and the cost expenditure has increased, and a new wave of mergers and acquisitions has emerged. In fact, 2020 was supposed to be a sluggish year for mergers and acquisitions in the semiconductor market, affected by the new crown epidemic and Sino-US relations. These two transactions have made the global semiconductor landscape go through a new round of mergers and acquisitions and reshuffles. If AMD reaches an acquisition agreement with Xilinx, the value of semiconductor M&A transactions in 2020 may also rise to $93.1 billion, making it the third largest merger and acquisition year in the history of the semiconductor industry. According to the report data released by IC Insights, a third-party analysis agency on September 29, the total value of global semiconductor mergers and acquisitions soared to US$63.1 billion in the first nine months of 2020, of which the two transactions of Nvidia-Arm and ADI-Maxim accounted for about 97% of total M&A in 2020. In the first quarter of this year, the value of semiconductor M&A transactions was $1.8 billion, and it only reached $165 million in the second quarter.

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.

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. AMD’s stock price has soared 89% this year, and its market value has now exceeded $100 billion to $101.568 billion.

Go language to FPGA platform builds custom, reprogrammable, low-latency accelerators using software-defined chips. GraphSim is a graph-based ArtSim SSSP algorithm. The resulting archive conforms to the RFC 1952 GZIP file format specification. It is a preconfigured, ready-to-run image for executing Dijkstra's shortest path search algorithm on Amazon's FGPA-accelerated F1. The GZIP accelerator provides hardware-accelerated gzip compression up to 25 times faster than CPU compression.

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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.

First, develop an OpenCV-based fast corner algorithm design and validate this algorithm using OpenCV-based test-inspired simulations. 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. 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. We use the example of fast corners to illustrate the process of implementing OpenCV with VivadoHLS.