XC7K480T-2FFG1156C_XC7Z100-L2FFV900I Introduction

In order to better adapt to the new world of intelligent interconnection, Xilinx continues to take "flexible platform" as the core of its products, seizes new industrial opportunities, and formulates three major development strategies to support wider market applications. Victor Peng pointed out that the first strategy is "data center first." In the data center space, it is important to realize that Xilinx can support not only compute acceleration and data center applications, but also value-creating storage and networking.

. The deal could be finalized as soon as next week, people familiar with the matter said. Advanced Micro Devices (AMD) is in advanced talks to buy chipmaker Xilinx in a deal that could be worth more than $30 billion.

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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.

Softnautics chose Xilinx technology to implement this solution because it integrates both the Vitis™ AI stack and powerful hardware capabilities. Today, Xilinx's rich and powerful platform supports 70% of new developments, leading the way in FPGA-based system design.

Softnautics selected the Xilinx Ultrascale+ platform because it offers the best in application processing and FPGA acceleration. Compared to the previous platform, the system-level performance per power has been improved by 4 times. It supports Xilinx Vitis AI, which provides extensive capabilities for building AI inference using accelerated libraries. In addition, it provides excellent high-level synthesis (HLS) capabilities.

Xilinx, mostly known as microchips called Field Programmable Gate Arrays (FPGAs), is the leading company in this field. This makes them highly valuable in rapid prototyping and rapidly emerging technologies. In the FPGA space, Intel is another major player, having established itself in the space with its 2015 acquisition of Altera. Unlike standard chips, they can be reprogrammed after production.

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OpenCV does not have a data structure for vectors, but when we want to represent vectors, we need to represent them with matrix data. However, CvMat is more abstract, and its element data types are not limited to basic data types, but can be any predefined data types, such as RGB or other multi-channel data. In openCV, the CvMat and IplImage types are more focused on "images", especially with a certain degree of optimization for image operations in them.

Therefore, in the design of OpenCV with VivadoHLS, it is necessary to modify the input and output HLS synthesizable video design interface to the Video stream interface, that is, use the video interface provided by HLS to synthesize the function to realize AXI4 video stream to VivadoHLS in hls ::Mat<> type conversion. The VivadoHLS video processing library uses the hls::Mat<> data type, which is used to model the processing of video pixel streams, and is essentially equivalent to the hls::steam<> stream type, rather than stored in external memory in OpenCV. matrix matrix type.