AutoQB: AutoML for Network Quantization and Binarization on Mobile Devices
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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 ∼79% less computing overhead, or improves the inference accuracy by ∼2% with the same computing cost.
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