XC5VLX85-1FFG1153I_XC5VSX240T-2FF1738I Introduction

If each instance can reduce power consumption by a certain amount, the total power consumption will be significantly reduced. In data center applications, applications may have thousands or even millions of parallel instances. To build a real application, you need to implement the monolith efficiently.

As an FPGA (Field Programmable Gate Array)-based company, Xilinx's strategy lies in three aspects: "data center priority", "accelerating the development of the core market" and "driving adaptive computing". This year, it has successively released the integrated SmartNIC platform AlveoU25, the strongest 7nm cloud chip Versal Premium, and an innovative TCON (Timing Controller, timing controller) solution for FPGA devices.

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. 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.

Similar to AI inference implementations, non-AI preprocessing and postprocessing functions begin to require some form of acceleration. There is also a third challenge, and this is a lesser known one, which arises because AI inference cannot be deployed on its own. These traditional processing functions must run at the same throughput as the AI functions, with the same high performance and low power consumption. For example, an image may need to be decompressed and scaled to meet the data input requirements of an AI model. True AI deployments often require non-AI processing, either before or after AI capabilities.

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Another optimization of IplImage for images is the variable origin, to compensate for this, OpenCV allows users to define their own origin settings. In the OpenCV type relationship, we can say that the IplImage type inherits from the CvMat type, and of course other variables are included to parse it into image data. The IplImage type has many more parameters than CvMat, such as depth and nChannels.

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.