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White Paper: Accelerating Big Data Processing and AI at the Edge

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WHITE PAPER Accelerating Big Data Processing and AI at the Edge mrcy.com 7 The Mercury Systems logo and the following are trademarks or registered trademarks of Mercury Systems, Inc.: Mercury Systems, Innovation That Matters, and BuiltSECURE. Other marks used herein may be trademarks or registered trademarks of their respective holders. Mercury believes this information is accurate as of its publication date and is not responsible for any inadvertent errors. The information contained herein is subject to change without notice. © 2021 Mercury Systems, Inc. 8083.00E-0921-wp-AcceleratingAI_Edge MADE IN USA Corporate Headquarters 50 Minuteman Road Andover, MA 01810 USA +1 978.967.1401 tel +1 866.627.6951 tel +1 978.256.3599 fax International Headquarters Mercury International Avenue Eugène-Lance, 38 PO Box 584 CH-1212 Grand-Lancy 1 Geneva, Switzerland +41 22 884 51 00 tel Learn more Visit: mrcy.com/nvidia In any communications network, roughly 80% of a node's battery power will be consumed by receive/transmit functions. AI at the edge significantly reduces data communications volume by identifying and transmitting only potentially significant portions of a sensor data stream, preserving battery power. AI on GPUs offers other potential ways to enhance 5G efficiency. NVIDIA's Aerial SDK is just one example of using GPUs directed by AI to speed up broadband signal processing. Beyond that, advanced self-organizing networks (SONs) may soon use AI to continually optimize 5G communications as radio frequency (RF) links between nodes face changes in effective bandwidth or are eliminated completely. The symbiotic relationship between all three technologies offers vast potential for both newly designed edge-based applications and continued performance enhancements. INTERTWINED AT THE EDGE: GPUS, AI AND 5G GPU semiconductors, AI algorithms and 5G networking are a set of intertwined technologies. Together they are enabling a new generation of edge applications. GPUs are an excellent processing platform for AI-based applications, implementing parallel processing of input data streams using a single instruction, multiple data (SIMD) model. This architecture makes GPUs extremely efficient at executing AI deep learning algorithms, which perform the same operation on many segments of input data. As new 5G networks connect thousands or millions of nodes, GPUs running AI can move intelligence to the edge. However, most of these deployed edge nodes will have a limited power budget. While NVIDIA has been steadily reducing GPU power requirements, it is still an issue. Fortunately, AI can be used to reduce the overall power needs of 5G nodes. Traditional Communications Network GPU Power Requirements vs AI Roughly 80% batter y power will be consumed by receive/transmit functions AI at the edge significantly reduces volume of data, preserving batter y power

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