Tips and tricks for deploying TinyML

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.