Paper:
Parameter quantization is a popular model compression technique due to its regular form and high compression ratio. In particular, quantization based on binary codes is gaining attention because each quantized bit can be directly utilized for computations without dequantization using look-up tables. Previous attempts, however, only allow for integer numbers of quantization bits, which ends up restricting the search space for compression ratio and accuracy. Moreover, quantization bits are usually obtained by minimizing quantization loss in a local manner that does not directly correspond to minimizing the loss function. In this paper, we propose an encryption algorithm/architecture to compress quantized weights in order to achieve fractional numbers of bits per weight and new compression configurations further optimize accuracy/compression trade-offs. Decryption is implemented using XOR gates added into the neural network model and described as tanh(x), which enable gradient calculations superior to the straight-through gradient method. We perform experiments using MNIST, CIFAR-10, and ImageNet to show that inserting XOR gates learns quantization/encrypted bit decisions through training and obtains high accuracy even for fractional sub 1-bit weights.

Review:
Summary: 
The authors propose quantize the weights of a neural network by enabling a fractional number of bits per weight. They use a network of differentiable XOR gates that maps encrypted weights to higher-dimensional decrypted weights to decode the parameters on-the-fly and learn both the encrypted weights and the scaling factors involved in the XOR networks by gradient descent.
Strengths of the paper:
- The method allows for a fractional number of bits per weights and relies of well-known differentiable approximations of the sign function. Indeed, virtually any number of bits/weights can be attained by varying the ratio N_in/N_out.
- The papers displays good results on ImageNet for a ResNet-18.
Weaknesses of the paper:
- Some arguments that are presented could deserve a bit more precision. For instance, quantizing to a fractional number of bits per weights per layer is in itself interesting. However, if we were to quantize different layers of the same network with distinct integer  ratio of bits per weights (say 1 bit per weight for some particular layers and 2 bits per weight for the other layers), the average ratio would also be fractional (see for instance "Hardware-aware Automated Quantization with Mixed Precision", Wang et al., where the authors find the right (integer) number of bits/weights per layer using RL). Similarly, using vector quantization does allow for on-chip low memory: we do not need to re-instantiate the compressed layer but we can compute the forward in the compressed domain (by splitting the activations into similar block sizes and computing dot products). 
- More extensive and thorough experiments could improve the impact of the paper. For instance, authors could compress the widely used (and more challenging) ResNet-50 architecture, or try other tasks such as image detection (Mask R-CNN). The table is missing results from: "Hardware Automated Quantization", Wang et al ; "Trained Ternary Quantization", Zhu et al ; "Deep Compression",  Han et al; "Ternary weight networks", Li et al (not an extensive list).
- Similarly, providing some code and numbers for inference time would greatly strengthen the paper and the possible usage of this method by the community. Indeed, I wonder what the overhead of decrypting the weights on-the-fly is (although it only involves XOR operations and products)
- Small typos: for instance, two points at the very end of section 5.
Justification fo rating:
The proposed method is well presented and illustrated. However, I think the paper would need either (1) more thorough experimental results (see comments above, points 2 and 3 of weaknesses) or (2) more justifications for its existence (see comments above, point 1 of weaknesses).