Model quantization is challenging due to many tedious hyper-parameters such
as precision (bitwidth), dynamic range (minimum and maximum discrete values)
and stepsize (interval between discrete values). Unlike prior arts that
carefully tune these values, we present a fully differentiable approach to
learn all of them, named Differentiable Dynamic Quantization (DDQ), which has
several benefits. (1) DDQ is able to quantize challenging lightweight
architectures like MobileNets, where different layers prefer different
quantization parameters. (2) DDQ is hardware-friendly and can be easily
implemented using low-precision matrix-vector multiplication, making it capable
in many hardware such as ARM. (3) Extensive experiments show that DDQ
outperforms prior arts on many networks and benchmarks, especially when models
are already efficient and compact. e.g., DDQ is the first approach that
achieves lossless 4-bit quantization for MobileNetV2 on ImageNet.

我们提出了一种完全可微的方法，名为 differentiable dynamic quantization (DDQ)，可用于学习模型量化中的所有超参数，通过实验表明 DDQ 在像 MobileNet 这样的轻量架构上表现最好，并且 DDQ 是硬件友好型。