Tiny Machine Learning
Tiny machine learning (tinyML) is the intersection of machine learning and embedded internet of things (IoT) devices. The field is an emerging engineering discipline that has the potential to revolutionize many industries.
TinyML emerged from the concept of the internet of things (IoT). The traditional idea of IoT was that data would be sent from a local device to the cloud for processing. Some individuals raised certain concerns with this concept: privacy, latency, storage, and energy efficiency to name a few.
How TinyML Works
TinyML algorithms work in much the same way as traditional machine learning models. Typically, the models are trained as usual on a user’s computer or in the cloud. Post-training is where the real tinyML work begins, in a process often referred to as deep compression.
Diagram of the deep compression process. Source: ArXiv.