We study the challenging task of neural network quantization without
end-to-end retraining, called Post-training Quantization (PTQ). PTQ usually
requires a small subset of training data but produces less powerful quantized
models than Quantization-Aware Training (QAT). In this work, we propose a novel
PTQ framework, dubbed BRECQ, which pushes the limits of bitwidth in PTQ down to
INT2 for the first time. BRECQ leverages the basic building blocks in neural
networks and reconstructs them one-by-one. In a comprehensive theoretical study
of the second-order error, we show that BRECQ achieves a good balance between
cross-layer dependency and generalization error. To further employ the power of
quantization, the mixed precision technique is incorporated in our framework by
approximating the inter-layer and intra-layer sensitivity. Extensive
experiments on various handcrafted and searched neural architectures are
conducted for both image classification and object detection tasks. And for the
first time we prove that, without bells and whistles, PTQ can attain 4-bit
ResNet and MobileNetV2 comparable with QAT and enjoy 240 times faster
production of quantized models. Codes are available at
this https URL

本文介绍了一种名为 BRECQ 的新型 PTQ 框架，通过重构神经网络的组件来将 PTQ 的位宽降至 INT2，并将混合精度技术结合到框架中。对多种手工制作和搜索的神经体系结构进行了广泛的实验，证明了 PTQ 可以实现比使用 QAT 的 4 位 ResNet 和 MobileNetV2 更快的速度制作量化模型。