Convolutional Neural Networks (CNNs) exhibit remarkable performance in various machine learning tasks. As
sensor-equipped internet of things (IoT) devices permeate into
every aspect of modern life, it is increasingly important to
run CNN inference, a computationally intensive application, on
resource constrained devices. We present a technique for fast
and energy-efficient CNN inference on mobile SoC platforms,
which are projected to be a major player in the IoT space. We
propose techniques for efficient parallelization of CNN inference
targeting mobile GPUs, and explore the underlying tradeoffs.
Experiments with running Squeezenet on three different mobile
devices confirm the effectiveness of our approach. For further
study, please refer to the project repository available on our
GitHub page: https://github.com/mtmd/Mobile_ConvNet.