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And for performance reasons, the architecture based on OpenCV is more complex and consumes more power. OpenCV image processing is built on the memory frame cache, it always assumes that the video frame data is stored in the external DDR memory, therefore, OpenCV has poor performance for accessing local images, because the processor's small cache performance is not enough to complete this Task. OpenCV seems to be sufficient for many applications when the resolution or frame rate requirements are low, or when processing the required features or regions in larger images, but for high-resolution high-frame-rate real-time processing scenarios, It is difficult for OpenCV to meet the demands of high performance and low power consumption.

In addition, Xilinx has released the world's first FPGA-based Open Compute Accelerator Module (OAM) proof-of-concept board. Based on Xilinx UltraScale+™ VU37P FPGA and equipped with 8GB HBM memory, the mezzanine card complies with the Open Accelerator Infrastructure (OAI) specification and can support seven 25Gbps x8 links, providing a rich inter-module system topology for distributed acceleration.

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DRAGEN Complete Suite - Ultra-Fast Analysis of Next Generation Sequencing - Exome The DRAGEN Complete Suite (Exome) enables next generation sequencing (NGS) data on large datasets such as whole exomes and target groups.

FAGP runs on a variety of accelerator cards on-premises or in the cloud, including AWS, Huawei, and Alibaba. FireSim is a cycle-accurate open-source FPGA-accelerated full-system hardware simulation platform running on cloud FPGAs (Amazon EC2 F1). FAGP (Falcon Accelerated Genomics Pipelines) is an accelerated genome analysis software solution that runs on Xilinx Alveo accelerator cards to provide faster turnaround times for computationally intensive algorithms in the life sciences.

The VivadoHLS video library is used to replace many basic OpenCV functions. It has similar interfaces and algorithms to OpenCV. It is mainly aimed at image processing functions implemented in the FPGA architecture, and includes FPGA-specific optimizations, such as fixed-point operations instead of floating-point operations. (Not necessarily accurate to bits), on-chip line buffer (line buffer) and window buffer (window buffer). Figure 2.1 shows the system architecture for implementing video processing on a Xilinx Zynq AP SoC device.

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

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Embedded ARM processors provide unique, critical control-plane processing capabilities to support emerging bare-metal server use cases. Standard full-featured NIC solution and driver with patented Onload™ application acceleration software reduces latency by up to 80% and improves efficiency for Transmission Control Protocol (TCP)-based server applications in cloud applications—maximum up to 400%. The U25 SmartNIC platform supports “bump-in-the-wire” seamless embedding of networking, storage, and compute offload and acceleration, which avoids unnecessary data transfers and CPU processing to maximize efficiency . Relying on Xilinx's industry-leading FPGA technology, the Alveo U25 SmartNIC platform can provide higher throughput and a more powerful adaptable engine than SoC-based NICs, enabling cloud architects to quickly create multiple types of functions and applications. Speed up. Basic NICs provide ultra-high throughput, small packet performance, and low latency. This also significantly reduces the burden on the CPU and frees up more resources to run more applications.

In the global fpga market, Xilinx and altera have a market share of about 90%. Sales revenue was US$850 million, an increase of 24% over the same period last year; net profit was US$241 million, an increase of 27% over the same period last year. On the one hand, chip manufacturers need to rely on FPGAs for simulation and prototyping; on the other hand, CPUs, GPUs, FPGAs, and ASICs (application-specific integrated circuits) are increasingly competing in the AI market. Even in the chip design of cpu and other chip giants such as Xilinx and intel, they will first simulate on the fpga, and then perform the streaming processing of the chip, not to mention the AI-specific chips launched by many AI algorithm companies in recent years. . With the development of 5G and artificial intelligence, it is expected that by 2025, the scale of FPGAs will reach about 12.521 billion US dollars. In 2013, the global FPGA market size was $4.563 billion, and by 2018, this figure will grow to $6.335 billion.