Embedded machine learning for industrial applications
Summary
On the other hand, ML can be more narrowly defined as enabling computers to automatically learn and improve from working with data, as opposed to a human designing all aspects of a program or solution. In industry, ML is finding uses in a whole range of areas, from preventative maintenance to optimising process efficiency, to straightforward but vital tasks such as managing when replacement parts and consumables need ordering. Running the ML models locally, either in an embedded system or in a PC at the ‘edge’, has some clear advantages over sending data off to a remote cloud or central server for processing. (Source: Maxim Integrated) Its clear that ML can be an enabling technology in industrial applications and can improve manufacturing and other processes by increasing efficiency, scalability and productivity – as well as keeping costs low. A growing number of high-performance embedded processors can be used to implement ML in an industrial application, supported by an ecosystem of software and development tools from major vendors such as Maxim, Microchip and NXP.
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