The 8 bits quantization has been widely applied to accelerate network
inference in various deep learning applications. There are two kinds of
quantization methods, training-based quantization and post-training
quantization. Training-based approach suffers from a cumbersome training
process, while post-training quantization may lead to unacceptable accuracy
drop. In this paper, we present an efficient and simple post-training method
via scale optimization, named EasyQuant (EQ),that could obtain comparable
accuracy with the training-based method.Specifically, we first alternately
optimize scales of weights and activations for all layers target at
convolutional outputs to further obtain the high quantization precision. Then,
we lower down bit width to INT7 both for weights and activations, and adopt
INT16 intermediate storage and integer Winograd convolution implementation to
accelerate inference.Experimental results on various computer vision tasks show
that EQ outperforms the TensorRT method and can achieve near INT8 accuracy in 7
bits width post-training.

本文提出一种名为 EasyQuant (EQ) 的简单有效的后训练量化方法，通过尺度优化来获得类似于基于训练方法的精度。具体地，我们针对卷积输出的所有层交替优化权重和激活的规模，以进一步获取高量化精度。然后，我们将位宽降低到 INT7，同时采用 INT16 中间存储和整数 Winograd 卷积实现加速推断。各种计算机视觉任务的实验结果表明，EQ 优于 TensorRT 方法，并且在经过 7 位宽后训练可达到接近 INT8 的精度。