Super-charge your Data Analytics Journey with FPGA-accelerated computing - Seguridad Mania.com - España y América Latina
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Estás en »Webinars
Estás en »Webinars
Jueves 22 de Oct, 2020
Enterprise and scientific users are becoming increasingly aware of the value contained within the vast quantities of data now collected from the physical and virtual worlds. Demand for data analytics is typically proportional to the amount of data being generated. As the quantity of data continues to expand exponentially, the need for data analytics will grow at a similar rate. Contemporary hardware and software architectures cannot be leveraged cost-effectively to meet the data generation, storage, and analytics needs. Hence, there is an urgent need for new and novel approaches based on heterogeneous compute platforms that appropriately couple CPU and FPGA resources. FPGAs, with their configurability, flexibility, scope for parallelism, and power efficiency, ensure effective and efficient acceleration of data-processing workloads. Artificial intelligence and data analytics are the defining workloads of the coming decade. Xilinx hardware and software solutions accelerate the development and deployment of AI and data analytics workloads in the data center, cloud and intelligent edge. Eventually taking the leap in data centre modernization requires three things and Presenters will talk about how to handle these challenges with Xilinx technology: • Need of Advancement in computing power that enable real-time, in-memory processing of vast amounts of data. • Tools that let data scientists develop analytic models using machine-learning techniques that can extract value from unstructured data. • Frameworks that enable software developers to easily incorporate data science models in the applications they develop. Take Away for attendees: • You will learn about Xilinx new Hardware architecture and Vitis Software Environment. • You will learn how Xilinx technology can supercharge your data science activities. • You will learn the Top N challenges in Data Analytics and how to tackle them.
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