Venue: NeurIPS 2022 Conference

Reviews:

final_score: rating: 6: Weak Accept: Technically solid, moderate-to-high impact paper, with no major concerns with respect to evaluation, resources, reproducibility, ethical considerations.
review_content: summary: This paper first thoroughly analyzes the difference in distributions of weights and activations in AdderNet and then proposes a new quantization algorithm by redistributing the weights and the activations.
strengths_and_weaknesses: Strengths: 
1. This paper conducts a thorough study of the dilemma in AdderNet quantization, and proposes an effective method to solve this problem. 
2. The paper is clearly presented.
3. I am glad to see that the accuracy drop is within 2% for ImageNet even at 4 bits.

Weaknesses (suggestions): 

1. The accuracy and energy comparisons with quantized CNN seem to be not very adequate. Better to compare the accuracy drops in quantized CNNs as well as the currently presented accuracy drop in AdderNet. So the reader can get the full information whether AdderNet is more advanced as compared to CNNs in terms of quantization.

2. AdderNet is a specific neural network, it is not clear whether the proposed methods can be generalized to other neural networks will similar distribution properties.

3. Only classification results are shown, how about other downstrem tasks, e.g., detection, segemetation. Or other network architectures, e.g., ViTs with adder kernels.

4. Does the AdderNet compatiable with SSL pretraining? e.g., MAE pretraining, and how the quantization scheme different for pretraining stage and fine-tuning or normal training stages?

5. How is the latency or throughput in real devices? I am curious of this since there is no available efficient CUDA implementation of AdderNet open-sourced as of now. Also, quantization is also not efficient for general CPU/GPU devices. Have you deployed the trained models to real devices yet?

I am open to further boost the score or champion this paper if the rebuttal is also sound and can somewhat solve my quesions/concerns.
questions: 1. The authors show the quantization results from 8bit to 4bit, and I am curious about the accuracy on lower-bit quantization, e.g., 3bit?
Fig. 6 shows energy and accuracy comparisons with CNN quantized via BRECQ PTQ method. Could you show comparisons with more CNN quantization methods?

2. Is the quantization bit of output (before adding the bias) consistent with that of input and weight? Do you also quantize gradients and errors during training?

3. The authors cluster weights in AdderNet into several groups, and then leverage different inter-group scale factors to quantize both weights and input. By doing this, I am concerned about whether it will consume more energy to transfer the aforementioned different quantized inputs when deployed onto mobile devices.

4. Do you have any plan to open source the kernel implementation of AdderNet and corresponding quantization schemes?

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After reading the author's post, most of my questions are resolved and I am tending to accept it. The new experiments or tables can be added to the revision.
limitations: The comparison with CNN quantization method seems to be not very adequate.
review_title: null

final_score: rating: 4: Borderline reject: Technically solid paper where reasons to reject, e.g., limited evaluation, outweigh reasons to accept, e.g., good evaluation. Please use sparingly.
review_content: summary: The paper proposes a new quantization scheme for Addernets. Specifically, the authors propose to cluster model weights in the channel dimension, where each cluster is assigned its own scale value for quantization. This ensures that the scales can better represent the range of values, which may be different for different weight channels. The authors further propose to absorb the error caused by clamping the weights inside the layer bias, which helps restore accuracy. Finally, the proposed method removes outliers when quantizing the activations, therefore tailoring the scale to better represent the valid range of data.
strengths_and_weaknesses: Strengths:
- The paper is well-written and the ideas are clearly explained.
- The proposed method significantly improves the accuracy after quantization, compared to prior methods.

Weaknesses:
- Some of the claims are not backed up by the method. Specifically, the authors mention that a shortcoming of prior work is using the same scale for weights and activations which is decided based on either of the two and therefore may not best fit the other. The proposed method also adopts the same scheme, where the scales are still determined by either the weights or the activations with the only difference being the increased granularity of the scale choices due to the channel clustering. Please find more details on this in the next section.
- Some questions remain regarding applying the method to new models, e.g., how to determine the number of clusters for new benchmarks.
questions: Questions:
- It appears that the models are not trained with a weight regularization term which leads to a wide range of weight values in Figure 2. A standing question is whether adding strict regularization to training can change the effectiveness of the proposed method? In that scenario, the weights are forced to be within certain bounds, which may reduce the benefits of clustering as the same scale may likely work for all weights. 

Limitations:
- The I term in equation 13 of the paper merely depends on the weight values and not the activations. Therefore, while the derived scale is perhaps optimized for the weights, there is no guarantee that it will work for the range of activations. The accuracy gains of the proposed method may therefore be due to the more fine-grained weight scales, rather than the fact that the scales are actually tailored to both the activation and the weights, which is what is assumed after reading the introduction. 
limitations: The authors have not discussed the limitations or potential negative social impact of their work.
review_title: null

final_score: rating: 6: Weak Accept: Technically solid, moderate-to-high impact paper, with no major concerns with respect to evaluation, resources, reproducibility, ethical considerations.
review_content: summary: This manuscript focuses on the problem of the quantization of AdderNet. The author has investigated the difference between AdderNet and traditional networks. Based on the differences, the dedicated quantization is achieved by redistributing the weights and activation of AdderNet. In the quantization method, three techniques are proposed to overcome the bit waste and over clamp problems, including clustering-based grouping quantization, range clamp of weights, and outlier clamp of activations. Experimental results show the effectiveness of the proposed method for quantizing AdderNet with different bit widths.
strengths_and_weaknesses: Pros:

- The manuscript is easy to follow. The analysis of the difference between conventional quantization methods for CNN and that for AdderNet is interesting. The statistics of the activations and weights of a pre-trained AdderNet are good.

- As a new kind of efficient neural network (AdderNet), how to effectively quantize it is a challenging problem. Quantization of such NN would put forward a faster and more energy-efficient model.

- The proposed method, includes clustering-based grouping quantization, range clamp of weights, and outlier clamp of activations, is promising for addressing the bit waste or over-clamp issues within AdderNet, which is also verified by the extension experiments.

Cons:

- Besides the FLOPs and energy, it would be great to report the inference time of the proposed method.

- It is highly recommended to add a more detailed recap about AdderNet, which will make the whole manuscript smoother, especially for those who do not familiar with it.

- In Fig. 3, how is the `-126.2` calculated? There is no detailed explanation about it.

- In line 45, “L1-norm quantization” is unclear. Does it mean an L1-norm-based quantization method or quantization for L1-norm operation?

Minor issues

-There is a strange “rec” symbol in Line 193

- There are several minor grammar issues. For example,  in line 21: “well-verified” should be “well verified”.

questions: Please see the weakness part.
limitations: Yes
review_title: null

final_score: rating: 8: Strong Accept: Technically strong paper, with novel ideas, excellent impact on at least one area, or high-to-excellent impact on multiple areas, with excellent evaluation, resources, and reproducibility, and no unaddressed ethical considerations.
review_content: summary: Quantization is an effective method to further reduce the energy consumption of AdderNets. However, previous AdderNets quantization methods cannot properly handle the challenge of large differences in weights and activations and often lead to a large accuracy degradation, especially in the case of low-bit (4-bit). This paper first reveals the key reasons for the poor accuracy of previous AdderNets quantization methods, namely “over clamp” and “bits waste”. Then a novel quantization method for AdderNets is proposed. Experiments on several datasets and models demonstrate the effectiveness of the proposed method.
strengths_and_weaknesses: Strengths
1. The paper is extremely well structured and easy to follow, with motivation well-explained.
2. To my knowledge, this paper is by far the most comprehensive and systematic study of the quantization of AdderNets. Through thorough analysis, this paper concludes two main reasons for the poor accuracy of previous AdderNets quantization methods, namely “over clamp” and “bits waste”, which are insightful. The proposed scheme of the clustering-based weights grouping and the lossless range clamp for weights are interesting and novel.
3. Extensive experiments on different models and datasets. Superior performance compared to other AdderNets quantization methods. The thorough ablation studies verifies the effectiveness of each components. The distributions of weights and activations (Fig.1 in Appendix) demonstrate that the proposed method can effectively solve the problem of “over clamp” and “bits waste”, leading to a higher quantized performance.
Weaknesses
1. The values in Fig. 4 are too small to read. The authors are required to refine them.
2. The histogram for INT4 weights ​​adjacent to “over clamp” is significantly higher (Fig.1 in Appendix), however, this phenomenon is not expressed in the top of Fig.1 (c). The authors are advised to revise this detail for better presentation.
questions: 1. Is it a common phenomenon that the distribution of weights and activations in pre-trained adder neural networks differs greatly?
2. Can the authors further explain the meaning of the values ​​marked in red in Fig.3 (b) and Fig.3 (c)? How are these values ​​calculated?
3. The results in Fig.5 (a) are impressive, yet the authors are advised to make more discussions on the results to make the paper stronger.
4. Writing suggestion: Line 166: “both weights and features” should be “both weights and activations”, to be consistent with the full text.
limitations: The authors have discussed the limitations and potential negative societal impact of their work in Appendix.
review_title: null

Meta-review: metareview: The reviewers were mostly positive about this paper [8,6,6,4], while the negative reviewer did not update the review or respond after the author's response. I do not see any major issues remaining. The suggested method seems interesting, novel, and achieves good empirical results.
confidence: Certain
Decision: Accept