XC5VLX85-1FFG676C_XC5VSX240T-2FF1738C Introduction

Excellent performance and excellent specifications let consumers once again call out: AMD YES!. First of all, AMD officially announced the new Zen 3 CPU architecture and brought the latest generation of Ryzen 5000 series desktop processors. Today, there is a lot of breaking news about AMD.

In Victor Peng's view, the big bang of geometric multiples, AI applications from end-to-edge to cloud, and post-Moore's law computing, all of which cannot be satisfied by a single architecture, which will be the three major factors affecting silence and the future of the world trend. The explosive growth of data places higher and higher demands on computing speed. The original chip solutions can no longer meet the needs of the company, and there is an urgent need to develop new products, new technologies and new business models. Moore's Law is slowing down in Greater China, where innovation is growing at a high rate.

XC5VLX85-1FFG676C_XC5VSX240T-2FF1738C

XC5VLX330T-2FFG1738I

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 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.

However, with opportunities come challenges. AI inference, the process of using trained machine learning algorithms to make predictions, whether deployed in the cloud, edge, or on-device, requires excellent processing performance within a tight power budget. The prevailing view is that this requirement cannot be met by CPUs alone, and that some form of computational acceleration is needed to handle AI inference workloads more efficiently.

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. The resulting archive conforms to the RFC 1952 GZIP file format specification. Go language to FPGA platform builds custom, reprogrammable, low-latency accelerators using software-defined chips. GraphSim is a graph-based ArtSim SSSP algorithm.

XC5VLX85-1FFG676C_XC5VSX240T-2FF1738C

XC5VLX50T-2FFG665C

XC6VLX75T-1FF784C XC6VLX75T-1FF484I XC6VLX75T-1FF484C XC6VLX75T-3FFG784C XC6VLX75T-3FFG484C XC6VLX760-1FF1760I XC6VLX760-1FF1760C XC6VLX550T-2FFG1760C XC6VLX760-2FF1760C XC6VLX550T-2FFG1759C XC6VLX550T-2FFG1759I XC6VLX75T-2FF784I XC6VLX75T-2FF784C XC6VLX75T-2FFG484I XC6VLX75T-2FFG484C XC6VLX75T-2FFG784I XC6VLX75T- 2FFG784C XC6VLX75T-3FF784C XC6VLX75T-3FF484C XC6VLX365T-1FFG1156C XC6VLX365T-1FFG1759I XC6VLX365T-2FF1759C XC6VLX365T-1FFG1156I .

XC5VTX240T-2FF1759C XC5VTX240T-1FF1759I XC5VTX240T-1FFG1759C XC5VSX95T-3FFG1136C XC5VSX95T-1FFG1136C XC5VSX50T-3FFG665C XC5VSX95T-1FF1136C XC5VTX240T-1FFG1759I XC5VSX95T-2FFG1136C XC5VSX95T-1FFG1136I XC5VSX95T-2FF1136C XC5VSX95T-1FF1136I XC5VSX50T-2FF665C XC5VSX50T-2FFG665C XC5VSX50T-2FFG1136I XC5VSX95T-2FF1136I 。

XC5VSX50T-1FFG665C XC5VSX35T-2FFG665C XC5VSX50T-1FF1136C XC5VSX35T-1FFG665I XC5VSX35T-3FF665C XC5VSX35T-3FFG665C XC5VSX240T-2FFG1738I XC5VSX35T-2FFG665I XC5VSX240T-2FF1738I XC5VSX240T-2FFG1738C XC5VSX35T-1FF665I XC5VSX240T-2FF1738C XC5VSX240T-3FFG1738C XC5VSX35T-1FF665C XC5VSX240T-1FF1738C XC5VSX240T-3FF1738C XC5VSX240T- 1FFG1738C XC5VSX240T-1FF1738I XC5VSX240T-1FFG1738I XC5VLX85T-2FFG1136C XC5VLX85T-2FFG1136I XC5VLX85T-3FF1136C XC5VLX85T-3FFG1136C XC5VLX85T-1FFG1136C XC5VLX85T-1FFG1136I XC5VLX85T-2FF1136C XC5VLX85T-2FF1136I 。

XCV200-6BG256AF XCV200-5PQG240I XCV200-5PQG240C XCV200-5PQ240I XCV200-5PQ240C XCV2005PQ240C XCV200-5FGG456I XCV200-5FGG456C XCV200-5FGG256I XCV200-5FGG256C XCV200-5FG456I XCV200-5FG456C XCV200-5FG456 XCV200-5FG256I XCV200-5FG256C XCV200-5BGG352I XCV200-5BGG352C 。

XC5VLX85-1FFG676C_XC5VSX240T-2FF1738C

In July 2016, Xilinx said it would become an all-programmable company within the next five years, using its strengths to help customers differentiate and target emerging areas such as cloud computing, Internet of Things, 5G wireless and embedded vision. This is an adaptive computing acceleration platform. At present, the main series of FPGA products include high-performance virtex series, mid-range kintex series and low-cost artix and spartan series. Cyrus defines it as a new product different from CPU, GPU and FPGA. In fact, in 2014, Xilinx began work on a new generation of products that debuted in early 2018.

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.