  This paper introduces a highly flexible, quantized, memory-efficient, and
ultra-lightweight object detection network, called TinyissimoYOLO. It aims to
enable object detection on microcontrollers in the power domain of milliwatts,
with less than 0.5MB memory available for storing convolutional neural network
(CNN) weights. The proposed quantized network architecture with 422k
parameters, enables real-time object detection on embedded microcontrollers,
and it has been evaluated to exploit CNN accelerators. In particular, the
proposed network has been deployed on the MAX78000 microcontroller achieving
high frame-rate of up to 180fps and an ultra-low energy consumption of only
196{\mu}J per inference with an inference efficiency of more than 106
MAC/Cycle. TinyissimoYOLO can be trained for any multi-object detection.
However, considering the small network size, adding object detection classes
will increase the size and memory consumption of the network, thus object
detection with up to 3 classes is demonstrated. Furthermore, the network is
trained using quantization-aware training and deployed with 8-bit quantization
on different microcontrollers, such as STM32H7A3, STM32L4R9, Apollo4b and on
the MAX78000's CNN accelerator. Performance evaluations are presented in this
paper.
