Loss-aware Weight Quantization of Deep Networks
The huge size of deep networks hinders their use in small computing devices. In this paper, we consider compressing the network by weight quantization. We extend a recently proposed loss-aware weight binarization scheme to ternarization, with possibly different scaling parameters for the positive and negative weights, and m-bit (where m > 2) quantization. Experiments on feedforward and recurrent neural networks show that the proposed scheme outperforms state-of-the-art weight quantization algorithms, and is as accurate (or even more accurate) than the full-precision network.
 In this paper, the authors propose a method of compressing network by means of weight ternarization. The network weights ternatization is formulated in the form of loss-aware quantization, which originally proposed by Hou et al. (2017). To this reviewer’s understanding, the proposed method can be regarded as the extension of the previous work of LAB and TWN, which can be the main contribution of the work. While the proposed method achieved promising results compared to the competing methods, it is still necessary to compare their computational complexity, which is one of the main concerns in network compression. It would be appreciated to have discussion on the results in Table 2, which tells that the performance of quantized networks is better than the full-precision network.
You are a member of the scientific community tasked with peer review. Review the following paper title and abstract.
### Title + Abstract
Loss-aware Weight Quantization of Deep Networks
The huge size of deep networks hinders their use in small computing devices. In this paper, we consider compressing the network by weight quantization. We extend a recently proposed loss-aware weight binarization scheme to ternarization, with possibly different scaling parameters for the positive and negative weights, and m-bit (where m > 2) quantization. Experiments on feedforward and recurrent neural networks show that the proposed scheme outperforms state-of-the-art weight quantization algorithms, and is as accurate (or even more accurate) than the full-precision network.
user
<think>Okay, to do a decent peer review, I should start with summarizing what the work is about. This includes novelty, contributions, clarity, and impact. I might include suggestions to issues that I find critical to the usefulness of the work. Starting with novelty, there are different types of novelty. The first aspect of novelty is scope and scale. Is the work covering a broader perspective than previous works? The second aspect is methodology. Is the proposed methodology novel compared to previous methods? Do they provide any significant advantage over previous methodologies? The third aspect is results. Are the results different compared previous work? Does this lead to any novel theories or an extension of current theories? Next is clarity, I will be reading the work in depth and analyze if any sentences can be potentially ambiguous or misleading. If any sentences are ambiguous, I will state them and give a suggestion on how to rephrase such sentence or how to make more clear to the audience. Lastly, impact is important. I will attempt to predict how such work will impact the scientific community. This might include the applications of such work and how the work might transform the way we study a certain topic. Finally, I will might only mention issues that are critical. This is to keep the review as concise and helpful as possible.</think>



 In this paper, the authors propose a method of compressing network by means of weight ternarization. The network weights ternatization is formulated in the form of loss-aware quantization, which originally proposed by Hou et al. (2017). To this reviewer’s understanding, the proposed method can be regarded as the extension of the previous work of LAB and TWN, which can be the main contribution of the work. While the proposed method achieved promising results compared to the competing methods, it is still necessary to compare their computational complexity, which is one of the main concerns in network compression. It would be appreciated to have discussion on the results in Table 2, which tells that the performance of quantized networks is better than the full-precision network.
assistant
 In this paper, the authors propose a method of compressing network by means of weight ternarization. The network weights ternatization is formulated in the form of loss-aware quantization, which originally proposed by Hou et al. (2017). To this reviewer’s understanding, the proposed method can be regarded as the extension of the previous work of LAB and TWN, which can be the main contribution of the work. While the proposed method achieved promising results compared to the competing methods, it is still necessary to compare their computational complexity, which is one of the main concerns in network compression. It would be appreciated to have discussion on the results in Table 2, which tells that the performance of quantized networks is better than the full-precision network.