  TinyML is a fast-growing multidisciplinary field at the intersection of
machine learning, hardware, and software, that focuses on enabling deep
learning algorithms on embedded (microcontroller powered) devices operating at
extremely low power range (mW range and below). TinyML addresses the challenges
in designing power-efficient, compact deep neural network models, supporting
software framework, and embedded hardware that will enable a wide range of
customized, ubiquitous inference applications on battery-operated,
resource-constrained devices. In this report, we discuss the major challenges
and technological enablers that direct this field's expansion. TinyML will open
the door to the new types of edge services and applications that do not rely on
cloud processing but thrive on distributed edge inference and autonomous
reasoning.
