| | NOVEMBER 20188CIOReviewMachine Intelligence (MI) is truly the next breakthrough in computing. Applications' abilities to recognize data patterns, identify classifications as well as identifying anomalies with ever improving accuracy are rapidly growing in number. Maturing MI will transform how we work and play, and certainly the embedded devices market will be turned on its head over the next decade.Embedded devices are quickly becoming imbued with MI, transforming them into even smarter devices. They can leverage historical and real-time data and increasingly deploy machine learning (ML) algorithms. Embedded devices are often located at the edge of the network; think cell towers, robotic arms, and smart city traffic sensors. With ML, embedded smart devices at the edge of the network are rapidly becoming the front line of analysis and decision-making. This transformation will touch virtually every vertical industry, with huge growth and capability for embedded electronics going forward. Data is so important GE planned to spend $1 billion in 2017 alone to analyze data from sensors on gas turbines, jet engines, oil pipelines and other machines, and aims to triple sales of its software products by 2020 to roughly $15 billion.In this new interconnected and more holistic world, we're already seeing improved efficiency and outcomes. Embedded ML applications start from the obvious ­ e.g.; unlocking mobile devices with facial recognition for identity authentication. Behind the scenes, security devices such as routers are rapidly incorporating smart technology. At the device and network levels, real-time analytics analyze traffic and behavior patterns to look for aberrations or signatures that could be security threats.Rapid SimulationThe impact of ML is most apparent for embedded applications with "Digital Twins." These are digital representations of physical objects, usually specific real-world devices active in the field and represent an exact device configuration at a point in time. By adding sensors to the field device, product designers can gather real-world data and sometimes real-time asset performance. Thus enabled, the device becomes part of the Internet of Things (IoT) and real-world data can feed into ML training sets to improve generative design and facilitate design optimization. It can be used with real-life simulators or serve as a simulated digital twin before a physical design is completed. It can drive not only design improvements but add whole new capabilities. When running a specific application, designers instantly have visibility into the simulation By Mark Papermaster, CTO, and SVP Technology and Engineering, AMDMACHINE LEARNING NOW AT THE FOREFRONT OF EMBEDDED APPLICATIONSMark Papermaster
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