We consider the post-training quantization problem, which discretizes the
weights of pre-trained deep neural networks without re-training the model. We
propose multipoint quantization, a quantization method that approximates a
full-precision weight vector using a linear combination of multiple vectors of
low-bit numbers; this is in contrast to typical quantization methods that
approximate each weight using a single low precision number. Computationally,
we construct the multipoint quantization with an efficient greedy selection
procedure, and adaptively decides the number of low precision points on each
quantized weight vector based on the error of its output. This allows us to
achieve higher precision levels for important weights that greatly influence
the outputs, yielding an 'effect of mixed precision' but without physical mixed
precision implementations (which requires specialized hardware accelerators).
Empirically, our method can be implemented by common operands, bringing almost
no memory and computation overhead. We show that our method outperforms a range
of state-of-the-art methods on ImageNet classification and it can be
generalized to more challenging tasks like PASCAL VOC object detection.

本文提出一种基于多点量化的离线量化方法，该方法通过线性组合多个低精度数据来逼近完整的权重向量。在不使用专门的混合精度实现的情况下，该方法取得了比业界其他方法更好的精度并广泛适用于多个领域。