Deep Learning and Reconfigurable Platforms in the Internet of Things: Challenges and Opportunities in Algorithms and Hardware

• Published in: IEEE industrial electronics magazine Vol. 12; no. 2; pp. 36 - 49
• Main Authors: Fernandez Molanes, Roberto, Amarasinghe, Kasun, Rodriguez-Andina, Juan, Manic, Milos
• Format: Magazine Article
• Language: English
• Published: New York IEEE 01.06.2018; The Institute of Electrical and Electronics Engineers, Inc. (IEEE)
• Subjects: Autonomous cars; Big Data; Biochips; Data mining; Deep learning; Driving; Electronic devices; Internet of Things; Machine learning; Medical devices; Medical electronics; Medical equipment; Neural networks; Nuclear power plants; Pacemakers; Predictive maintenance; Situational awareness; Smart buildings; Smart cities; Smart homes; Surgical implants
• ISSN: 1932-4529; 1941-0115
• DOI: 10.1109/MIE.2018.2824843

As the Internet of Things (IoT) continues its run as one of the most popular technology buzzwords of today, the discussion really turns from how the massive data sets are collected to how value can be derived from them, i.e., how to extract knowledge out of such (big) data. IoT devices are used in an ever-growing number of application domains (see Figure 1), ranging from sports gadgets (e.g., Fitbits and Apple Watches) or more serious medical devices (e.g., pacemakers and biochips) to smart homes, cities, and self-driving cars, to predictive maintenance in missioncritical systems (e.g., in nuclear power plants or airplanes). Such applications introduce endless possibilities for better understanding, learning, and informedly acting (i.e., situational awareness and actionable information in government lingo). Although rapid expansion of devices and sensors brings terrific opportunities for taking advantage of terabytes of machine data, the mind-boggling task of understanding growth of data remains and heavily relies on artificial intelligence and machine learning [1], [2].