TinyML is a generic approach for shrinking AI models and applications to run on smaller devices, including microcontrollers, cheap CPUs and low-cost AI chipsets. While most AI development tools focus on building bigger and more capable models, deploying TinyML models requires developers to think about doing more with less. TinyML applications are often designed to run on battery-constrained devices with milliwatts of power, a few hundred kilobytes of RAM and slower clock cycles. Teams need to do more upfront planning to meet these stringent requirements. TinyML app developers need to consider hardware, software and data management and how these pieces will fit together during prototyping and scaling up. ABI Research predicts the number of TinyML devices will grow from 15.2 million shipments in 2020 to a total of 2.5 billion by 2030.

One exciting avenue in the world of AI research and development is finding ways to shrink AI algorithms to run on smaller devices closer to sensors, motors and people. Developing embedded AI applications that run machine learning on microcontrollers comes with different constraints around power, performance, connectivity and tools. Embedded AI already has various uses: identifying types of physical activity with smartphone sensors, responding to wake words in consumer electronics, monitoring industrial equipment and distinguishing family members from strangers in home security cameras. A range of new tools, such as TinyML and TensorFlow Lite, can simplify the development of smaller, more power-efficient AI algorithms. "The rise of TinyML deployed on microcontrollers enables intelligence to be distributed into more connected products in the physical world, whether they be smart home gadgets, toys, industrial sensors or otherwise," said Jason Shepherd, vice president of ecosystem at edge-computing platform Zededa.