Paper:
Weight quantization for deep convolutional neural networks (CNNs) has shown promising results in compressing and accelerating CNN-powered applications such as semantic segmentation, gesture recognition, and scene understanding. Prior art has shown that different datasets, tasks, and network architectures admit different iso-accurate precision values, which increase the complexity of efficient quantized neural network implementations from both hardware and software perspectives. In this work, we show that when the number of channels is allowed to vary such that networks of different precision values have the same model size, lower precision values outperform higher precision ones in a Pareto sense (accuracy vs. model size) for networks with standard convolutions. Relying on comprehensive empirical analyses, we find that the optimal precision value of a convolution layer depends on the number of input channels per output filters and provide theoretical insights for it. To this end, we develop a simple algorithm to select the precision values for CNNs that outperforms corresponding 8-bit quantized networks by 0.9% and 2.2% in top-1 accuracy on ImageNet for ResNet50 and MobileNetV2, respectively.

Review:
This paper studies the accuracy vs model-size trade-off of quantized CNNs under different channel width multipliers. The authors demonstrated that while all-to-all convolution works well under low bit settings, depthwise conv needs a different sweet spot. The authors then proceed to use the insight to design quantized cnns that have two different schemes for depthwise and normal conv.
Strength
The paper is well written and motivated. By adding network width to the search space,  using the simple heuristics, the authors provide a better results than previously DRL based search method.
Weakness
One of my main concerns is the direct usage of total number of bits as an equivalent measurement between models. While it is useful to measure the storage cost for weights. The choices of bit width will likely affect the computing cost in a non-trivial way, depending on the target hardware platform. It is unclear whether equal model size would mean equal inference latency in practice (most likely they would not be). Providing empirical implementations of these models will shed light into this question.
These weakness makes it a borderline paper.
Question:
How do you handle batchnorm layer, do you use floating points?
How many bits did you use for accumulating the results?
Update after rebuttal:
I have read the authors' response. I would like to keep my current review as it is. Also note that the authors uses floating pt for batchnorm and 32 bit for accumulation, such additional cost might out-weights the benefit of choosing ultra low bits in the low bits regime, making the study less relevant for practice reasons