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This article shows you how to use fpga cloud server, the content is concise and easy to understand, it will definitely brighten your eyes. I hope you can get something through the detailed introduction of this article.

Generally speaking, small and medium-sized enterprises or individual webmasters do not use fpga CVM. Fpga CVM has high-performance accelerated IP, and its price is higher than that of ordinary CVM. The usage scenarios of fpga CVM are generally as follows.

Video processing

Video applications such as automatic image classification and recognition, image search, video transcoding, real-time rendering, Internet live streaming and AR/VR require a lot of real-time computing power, but ordinary CVM is difficult to meet the performance requirements. FPGA CVM can provide a cost-effective video solution, which is an ideal choice for video scenarios.

Genomics research

Accurate medicine can be achieved through rapid analysis of massive data such as gene sequencing and analysis, biological and medical information, and massive data processing in drug research and development, molecular breeding and other fields. hardware acceleration is needed to solve the performance bottleneck of biological computing in this field. The powerful programmable hardware computing power provided by FPGA CVM lease can well meet the needs of fast computing of massive biological data.

Deep learning

Multi-layer neural network in machine learning needs a lot of computing resources, in which the training process needs to deal with huge amounts of data, and the reasoning process wants a very low delay. At the same time, machine learning algorithms are still being optimized. With the advantages of high parallel computing, hardware programmability, low power consumption and low delay, FPGA CVM can dynamically program and design the most matching hardware circuits for different algorithms to meet the requirements of massive computing and low delay in machine learning. Therefore, FPGA provides attractive alternatives to meet the hardware requirements of machine learning.

Financial analysis

The financial industry has high requirements for computing power, timely response based on ultra-low latency and high throughput capacity, such as financial computing based on pricing tree model, high-frequency financial transactions, fund / securities trading algorithms, financial risk analysis and decision-making, transaction security assurance, etc. FPGA cloud services can provide optimal hardware acceleration solutions for various scenarios through programmable hardware acceleration technology. Some scenarios can provide a hundredfold performance improvement over pure software.

The above content is how to use fpga CVM. Have you learned any knowledge or skills? If you want to learn more skills or enrich your knowledge reserve, you are welcome to follow the industry information channel.

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