Fast and Energy-Efficient CNN Inference on IoT Devices
This addresses the challenge of deploying CNNs on IoT devices, which is incremental as it builds on existing methods for mobile platforms.
The paper tackles the problem of running computationally intensive CNN inference on resource-constrained IoT devices by presenting a technique for fast and energy-efficient inference on mobile SoC platforms, with experiments on three mobile devices confirming its effectiveness.
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