“And the Bit Goes Down: Revisiting the Quantization of Neural Networks”, Pierre Stock, Arm Holdings, Joulin, Rémi Gribonval, Benjamin Graham, Hervé Jégou2019-07-12 (reduced-precision NNs; similar)⁠:

In this paper, we address the problem of reducing the memory footprint of convolutional network architectures. We introduce a vector quantization method that aims at preserving the quality of the reconstruction of the network outputs rather than its weights. The principle of our approach is that it minimizes the loss reconstruction error for in-domain inputs.

Our method only requires a set of unlabeled data at quantization time and allows for efficient inference on CPU by using byte-aligned codebooks to store the compressed weights. We validate our approach by quantizing a high performing ResNet-50 model to a memory size of 5MB (20× compression factor) while preserving a top-1 accuracy of 76.1% on ImageNet object classification and by compressing a Mask R-CNN with a 26× factor.

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