Towards efficient quantized neural network inference on mobile devices
Yaman Umuroglu, Magnus Jahre · 2017
From voice recognition to object detection, Deep Neural Networks (DNNs) are steadily getting better at extracting information from complex raw data. Combined with the popularity of mobile computing and the rise of the Internet-of-Things (IoT), there is enormous potential for widespread deployment of intelligent devices, but a computational challenge remains. A modern DNN can require billions of floating point operations to classify a single image, which is far too costly for energy-constrained mobile devices. Offloading DNNs to powerful servers in the cloud is only a limited solution, as it requires significant energy for data transfer and cannot address applications with low-latency requirements such as augmented reality or navigation for autonomous drones.