Live Demonstration: Bringing Powerful Deep Learning into Daily-Life Devices (Mobiles and FPGAs) Via Deep k-Means

摘要

The record-breaking success of convolutional neural networks (CNNs) comes at the cost of a large amount of model parameters. The resulting prohibitive memory storage and data movement energy have been limiting the extensive deployment of deep learning on daily-life edge devices which usually have limited storage capability and are battery-powered. To this end, we explore the employment of a recently published weight clustering technique, called deep k-Means which makes use of the redundancy within CNN parameters for reduced memory storage and data movement, and demonstrate k-Means's effectiveness in the context of an interactive real-time object detection using three representative daily-life devices (iPhone, iPad and FPGA).