  The emergence of Internet of Things (IoT) applications requires intelligence
on the edge. Microcontrollers provide a low-cost compute platform to deploy
intelligent IoT applications using machine learning at scale, but have
extremely limited on-chip memory and compute capability. To deploy computer
vision on such devices, we need tiny vision models that fit within a few
hundred kilobytes of memory footprint in terms of peak usage and model size on
device storage. To facilitate the development of microcontroller friendly
models, we present a new dataset, Visual Wake Words, that represents a common
microcontroller vision use-case of identifying whether a person is present in
the image or not, and provides a realistic benchmark for tiny vision models.
Within a limited memory footprint of 250 KB, several state-of-the-art mobile
models achieve accuracy of 85-90% on the Visual Wake Words dataset. We
anticipate the proposed dataset will advance the research on tiny vision models
that can push the pareto-optimal boundary in terms of accuracy versus memory
usage for microcontroller applications.
