MobileNets family of computer vision neural networks have fueled tremendous progress in the design and organization of resource-efficient architectures in recent years. New applications with stringent real-time requirements in highly constrained devices require further compression of MobileNets-like already computeefficient networks. Model quantization is a widely used technique to compress and accelerate neural network inference and prior works have quantized MobileNets to 4 − 6 bits albeit with a modest to significant drop in accuracy. While quantization to sub-byte values (i.e. precision ≤ 8 bits) has been valuable, even further quantization of MobileNets to binary or ternary values is necessary to realize significant energy savings and possibly runtime speedups on specialized hardware, such as ASICs and FPGAs. Under the key observation that convolutional filters at each layer of a deep neural network may respond differently to ternary quantization, we propose a novel quantization method that generates per-layer hybrid filter banks consisting of full-precision and ternary weight filters for MobileNets. The layer-wise hybrid filter banks essentially combine the strengths of full-precision and ternary weight filters to derive a compact, energy-efficient architecture for MobileNets. Using this proposed quantization method, we quantized a substantial portion of weight filters of MobileNets to ternary values resulting in 27.98% savings in energy, and a 51.07% reduction in the model size, while achieving comparable accuracy and no degradation in throughput on specialized hardware in comparison to the baseline full-precision MobileNets.
The paper presents a quantization method that generates per-layer hybrid filter banks consisting of full-precision and ternary weight filters for MobileNets. 
Strength:
(1)	The paper proposes to only quantize easy-to-quantize weight filters of a network layer to ternary values while also preserving the representational ability of the overall network by relying on few full-precision difficult-to-quantize weight filters. 
(2)	The proposed method maintains a good balance between overall computational costs and predictive performance of the overall network. Experimental results show that the proposed hybrid filter banks for MobileNets achieves savings in energy and reduction in model size while preserving comparable accuracy. 
(3)	The description is clear in general. 
Weakness:
(1)	Though the paper claims that recent works on binary/ternary quantization either do not demonstrate their potential to quantize MobileNets on ImageNet dataset or incur modest to significant drop in accuracy while quantizing MobileNets with 4-6-bit weights, it may worth comparing to the methods that achieved start-of-art results on other datasets to demonstrate the efficiency of the proposed method. 
(2)	Figure 1 and Figure 2 is a little blurry. 
(3).  How is about the performance compared to latest work? Is it possible to apply current framework to MobileNetV2 ? If can, what's performance?