AutoQB: AutoML for Network Quantization and Binarization on Mobile Devices

In this paper, we propose a hierarchical deep reinforcement learning (DRL)-based AutoML framework, AutoQB, to automatically explore the design space of channel-level network quantization and binarization for hardware-friendly deep learning on mobile devices. Compared to prior DDPG-based quantization techniques, on the various CNN models, AutoQB automatically achieves the same inference accuracy by $\sim79\%$ less computing overhead, or improves the inference accuracy by $\sim2\%$ with the same computing cost.