TinyML (machine learning) enables the performance of data analytics on low-powered hardware with low processing power and small memory size. ABI Research says it could revolutionise IoT.
ABI Research says a growing number of devices are being developed to support AI at the network edge, but are underpowered for such a task, and TinyML could be the answer.
In a whitepaper, TinyML: The Next Big Opportunity in Tech, ABI Research says TinyML—also referred to as ‘very edge AI’, edge AI, a subset of edge AI—is an ML technology that enables the performance of data analytics on low-power hardware and software, typically in the milliwatt range, using algorithms, networks and models of 100 Kilobytes or less.
ABI Research forecasts the global installed base of IoT devices will grow from 6.6 billion in 2020 to 23.72 billion by 2026, and that each represents an opportunity for AI and ML. As a result, it is forecasting the TinyML market to grow from 15.2 million shipments in 2020 to 2.5 billion in 2030.
It suggests the technology has benefits for data privacy and, live saving potential.
“AI processing at the very edge minimises the data traffic between IoT devices and gateways. Only data that are deemed critical to the system will be sent as an action point. Most of the data stay in the IoT devices without the risk of being collected for malicious purposes.”
And: “Indoor air pollution is responsible for 1.6 million deaths every year. Smart home devices equipped with TinyML chipsets can provide feedback to end users based on the changes to their surrounding environment, including drastic fluctuations in humidity and temperature, the absence of light source and specific nutrients, and sudden spikes in harmful air particles, without a major impact on the overall power consumption.”
