  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.
