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Predictions for embedded machine learning for IoT in 2021
Despite silicon shortages, several new capabilities for embedded machine learning on Internet of Things devices will emerge in 2021, industry watchers predict.
New capabilities mean severing the cord between so many Internet of Things ( IoT) devices and the cloud and instead running processes at the edge. The boost in chip processing capabilities—which will continue to increase next year, as Moore’s Law dictates—means sidestepping cloud-based latency issues, among other benefits.
Experts argue that moving processing to the edge – or “going to local execution,” as Hiroshu Doyu,, an embedded AI researcher at Ericsson, puts it –will deliver five distinct advantages in 2021:
- Network bandwidth
- Network coverage
- Latency
- Power consumption
- Privacy
Privacy will be less “porous,” Doyu said, offering fewer opportunities for data to be stolen while in transit to the cloud or on the return trip. “Once the AI is more powerful, that kind of device can be installed without a power line,” he said.
“More powerful IoT AI chips will be shipped and more domain-specific IoT AI chips will be shipped. Both would enable smarter intelligence on IoT sensors,” Doyu said.
TinyML Fueled by More Capable Hardware, Dev Trends
Lest we undervalue the significance of this, it’s important to note the dynamics underlying more intelligent hardware. It is the result of the convergence of several trends.
First, in recent years, hardware advancements have enabled the microcontrollers that perform calculations much faster. Improved hardware combined with more efficient development practices have made it easier for developers to build programs on these devices. And third, is the rise of tiny machine learning, or TinyML, has ushered in this era of smarter hardware.
To read the complete article, visit IoT World Today.
