Adaptive Quantization for Deep Neural Network
This work addresses the challenge of deploying complex DNNs on resource-constrained mobile platforms, representing an incremental improvement in quantization methods.
The paper tackles the problem of high computational and memory costs in deep neural networks for mobile deployment by proposing an optimization framework for quantization, which achieves 20-40% higher compression rates compared to equal bit-width quantization while maintaining model accuracy.
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. First, we propose a measurement to estimate the effect of parameter quantization errors in individual layers on the overall model prediction accuracy. Then, we propose an optimization process based on this measurement for finding optimal quantization bit-width for each layer. This is the first work that theoretically analyse the relationship between parameter quantization errors of individual layers and model accuracy. Our new quantization algorithm outperforms previous quantization optimization methods, and achieves 20-40% higher compression rate compared to equal bit-width quantization at the same model prediction accuracy.