Adaptive Quantization for Deep Neural Network

Understand

In recent years Deep Neural Networks (DNNs) have been rapidly developed in various applications, together with increasingly complex architectures.

• The performance gain of these DNNs generally comes with high computational costs and large memory consumption, which may not be affordable for mobile platforms.
• Deep model quantization can be used for reducing the computation and memory costs of DNNs, and deploying complex DNNs on mobile equipment.
• In this work, we propose an optimization framework for deep model quantization.