无需调参即可按硬件资源自动量化神经网络，兼顾精度与效率。
Constraint Guided Model Quantization of Neural Networks
- 基于计算资源上限自动调整网络参数位宽，无需手动调超参数。
- 在MNIST和CIFAR10上达到主流量化方法的性能水平。
- 适合边缘设备部署，确保推理复杂度不超硬件限制。
将神经网络部署到边缘设备的重要性日益提升，但边缘硬件资源有限，难以运行高复杂度模型。近年来提出多种量化方法以降低模型复杂度。本文提出约束引导量化（CGMQ），一种感知量化训练算法，通过设定计算资源上限，自动降低神经网络参数的位宽。CGMQ无需调节超参数即可生成满足预设计算成本约束的混合精度网络，而现有方法需依赖人工调参。在MNIST和CIFAR10数据集上的实验表明，CGMQ性能可与当前最先进的量化训练方法媲美，同时严格保证计算复杂度不超过边缘硬件的资源上限。
原文摘要 · Abstract (English)
Deploying neural networks on the edge has become increasingly important as deep learning is being applied in an increasing amount of applications. At the edge computing hardware typically has limited resources disallowing to run neural networks with high complexity. 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 and CIFAR10 that the performance of CGMQ is competitive with state-of-the-art quantization aware training algorithms, while guaranteeing the satisfaction of an upper bound on the computational complexity defined by the computational resources of the on edge hardware.
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