White Paper

Embedded Cognitive Computing and Artificial Intelligence for Military Applications (part 1)

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www.mrcy.com INNOVATION THAT MATTERS ® Corporate Headquarters 50 Minuteman Road • Andover, MA 01810 USA (978) 967-1401 • (866) 627-6951 • Fax (978) 256-3599 Mercury Systems and Innovation That Matters are registered trademarks of Mercury Systems, Inc. Other products mentioned may be trademarks or registered trademarks of their respective holders. Mercury Systems, Inc. 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. Copyright © 2018 Mercury Systems, Inc. 3466.00E-1118-wp-AI • Chemical, biological analysis – Medical diagnostics • Software defined radios - SIGINT • Target acquisition and tracking • Logistics – Infrastructure – Predictive maintenance • C2 – Battlefield management • Autonomous vehicles – Swarm behavior – Robotics • Automated Reverse Engineering • Human interaction / teaming To mitigate these challenges, modern militaries have to develop and deploy better AI-powered capabilities for all manner of defense and offensive missions. The most effective and practical way to do this it to leverage the commercial domain with its vast, existing artificial intelligence IP and make it ready for military missions. Characteristics of an embedded AI military processing system: • Powered by the most contemporary data center devices including CPUs and ASICS • GPGPU and FPGA co-processing and accelerators • Low-SWaP, compact, light and power efficient • Vast memory and storage • High bandwidth I/O for sensor ingestion • Mission pre-processing • Scalable to leverage technology across platforms and form-factors • Open systems architecture for interoperability and tech refreshes • Use standardized data formatting, I/O interfaces for efficient consistency • Rugged for deployment • SSE and cyber hardened for deployment anywhere • Trusted hardware – Trusted software • Designed, made, coded and supported from domestic DMEA facilities Other white papers in this series will look at the technology enablers that are making the data center-to-platform transition possible through system miniaturization and military-grade packaging. They will study effector determinism as defined by flight safety certification and embedded holistic system-wide security that enable AI-powered systems to be deployed anywhere. These white papers will explore the proven approaches that are enabling the best commercial artificial intelligence IP to migrate from the commercial to the military domain. Table of Acronyms AI Artificial Intelligence API Application Program Interface ASIC Application Specific Integrated Circuit C4I Command, Control, communication and Computers, Intelligence CC Cognitive Computing CPU Central Processor Units CSfC Commercial Solutions for Classified DoD Department of Defense DMEA Defense Microelectronics Activity DMS Diminished Manufacturing Supply EO/IR Electro Optical / Infrared EW Electronic Warfare FPGA Field Programmable Gate Array GB Gigabit GPGPU General Process Graphics Processor Unit GPS Global Positioning System HPC High Performance Computing IoE Internet of Everything IoT Internet of Things IP Intellectual Property KLIF Key Loading and Initialization Facility M2M Machine to Machine ML Machine Learning PC Personal Computer PEC High Performance Embedded Computing R&D Research and Development RFID Radio Frequency Identification (tracking) SKU Standard Keeping Unit SSD Solid State Drive SUAS Small Unmanned Aircraft System SWaP Size, Weight and Power UAV Unmanned Aerial Vehicle ZB ZettaBytes About the Authors Dr. Ian Dunn is Vice President and General Manager for Mercury Systems' Sensor and Mission Processing Group. Dr. Dunn is responsible for embedded product development across the entire sensor processing chain. Previously, Dr. Dunn was VP and General Manager of Mercury's Microwave and Digital Solutions Group. Before that, he was the company's CTO responsible for technology strategy and R&D projects. Dr. Dunn joined Mercury Systems in 2000 as a systems engineer upon completing a Ph.D. at Johns Hopkins University in Electrical Engineering. As a doctoral student at Johns Hopkins, Dr. Dunn consulted for Disney Imagineering and Northrop Grumman on distributed automation and various high performance computing projects. He has 20 years of experience designing and programming parallel computers for real-time signal processing applications and has authored numerous papers and a book on designing signal processing applications for high-performance computer architectures Devon Yablonski is Principal Product Manager for Mercury Systems' Sensor and Mission Processing Group. Mr. Yablonski has over ten years of HPC and HPEC applied and practical experience industry experience. He is responsible for strategic software and hardware product roadmaps and supporting capabilities as they are applied to embedded signal processing, artificial intelligence and deep/machine learning. He has a Bachelor's Degree in computer and electrical engineering from The University of Rhode Island and a Master's Degree in computer and electrical engineering from The Northeastern University.

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