I attended the Embedded Systems Conference (ESC) in Boston this week with my colleague Rick Rys. I have two main takeaways: Firstly, IIoT (at the smart edge device level) remains a major new market for the embedded systems industry. The emphasis last year at the ESC was that IoT was the natural evolution for ES, and would significantly grow the market. However, this year, the main message I got from most of the ES companies that I spoke with was security. IIoT technology providers are saying, we can build a new generation of smart connected edge devices that support IIoT, as data/information is moved up the IIoT stack from the edge device to each layer of the IIoT, it must be secure, or the industries will be hesitant to adopt much of the potential of IIoT.
Secondly, on the embedded intelligence side, and an area I’d like to focus a bit more on, ES is moving rapidly to advanced intelligence technologies like cognitive ES, artificial neural networks (ANNs), deep learning, embedded vision and speech, and embedded machine learning in smart edge devices. The forecast is that in 5-10 years, 50 percent of all smart devices will have cognitive systems, and this includes edge devices for manufacturing and productions systems. This is going to have a significant impact on areas like virtual and augmented reality use in production operations and maintenance, predictive and prescriptive analytics, and the actual implementation of autonomous self-healing systems for industry. New, more powerful processors like the next generation ARM Cortex-A72 processor and other multi-core processors will provide an order of magnitude more computing power in embedded devices. ARM claims that the new chip delivers as much as 50 times the performance gain compared to processors from just five years ago. This level of embedded computing power in smart edge devices and the production equipment itself will lead industries into an era of cognitive manufacturing.
Cognitive manufacturing is an evolutionary step in computer-enabled production system control that pushes beyond just “smart” technologies. This is an area in which the intelligence and reasoning is retained by the system, and provides the manufacturing system with capabilities for perception and judgment that enables the autonomous operation of the system based on embedded cognitive reasoning. Cognitive manufacturing systems will perceive changes in the production process and know how to respond to dynamic fluctuations by adapting the production to stay within target ranges of production cost and rate, and, as are increasingly important, sustainability areas such as energy use and carbon footprint. The embedded cognitive capability can be accomplished through the development of cognitive reasoning engines, or distributed intelligence agents, that are deployed throughout the production system.
It is becoming abundantly clear, that immerging technologies like cognitive manufacturing, IIoT, the digital twin, and advanced analytics will be dependent upon the technological progression of much more intelligent embedded systems.