Deploying neural networks on the edge has become increasingly important as deep learning is being applied in an increasing amount of applications. The devices on the edge are typically characterised as having small computational resources as large computational resources results in a higher energy consumption, which is impractical for these devices. To reduce the complexity of neural networks a wide range of quantization methods have been proposed in recent years. This work proposes Constraint Guided Model Quantization (CGMQ), which is a quantization aware training algorithm that uses an upper bound on the computational resources and reduces the bit-widths of the parameters of the neural network. CGMQ does not require the tuning of a hyperparameter to result in a mixed precision neural network that satisfies the predefined computational cost constraint, while prior work does. It is shown on MNIST that the performance of CGMQ is competitive with state-of-the-art quantization aware training algorithms, while guaranteeing the satisfaction of the cost constraint.

本研究针对边缘设备计算资源有限的问题，提出了一种新的量化感知训练算法——约束指导模型量化（CGMQ）。该方法在不需要调节超参数的情况下，确保生成满足预设计算成本约束的混合精度神经网络，并在MNIST数据集上显示出与最先进的量化感知训练算法相当的性能。

约束指导的神经网络模型量化