Venue: ICLR 2023 Conference

Reviews:

final_score: rating: 3: reject, not good enough
review_content: summary_of_the_paper: The paper considers the problem of channel equalization for wired-line communications systems,
and proposes a machine learning based equalizer inspired by forward-backward algorithm.
strength_and_weaknesses: The proposed approach itself might have some novelty, however, I cannot recommend the acceptance 
of the paper, mainly due to the lack of effectiveness.
Some detailed comments are listed below.

-From the view point of the development of equalization systems, this kind of serial equalizer
is considered as rather classical approach, and block transmission based equalization scheme
such as OFDM will be the main stream of the equalization scheme nowadays. Thus, more concrete 
target of the wired line communications systems including specific standard should be clarified
in the paper instead of only "wired-line communication. Especially, it is hard to imagine a
wired-communications systems which have difficulty to use PAM4 in this era.

-The complexity of the proposed approach is compared as that of the FB algorithm to show the 
effectiveness of the proposed approach, but it should be compared with frequency domain equalization
approach, which has much lower computational complexity of O(N log N).

-In the performance evaluation, the channel models used in the simulations will not be sufficient.
It is true that a certain pre-cursors can be recognized, but judging from Fig. 7, all channel models
are almost minimum phase channel. Please show the performance in much more non-minimum phase channel
as well.

clarity,_quality,_novelty_and_reproducibility: A certain novelty can be recognized in the proposed approach, while the system and channel models are too much naive from a view point of implementation. 
summary_of_the_review: A certain contribution can be found in the proposed method, while a classical toy model is assumed. However, authors also claims the effectiveness of the proposed method in modern wire-line communications systems, which I cannot agree.
review_title: null

final_score: rating: 6: marginally above the acceptance threshold
review_content: summary_of_the_paper: This paper proposes a neural network-based equalizer that mimics the forward-backward algorithm for wireline communication. The authors also tried pruning the neural network.
strength_and_weaknesses: Strength:
The proposed solution has lower computational complexity than the forward-backward (FB) algorithm, while the BER performance is better than conventional equalizers.

Weakness:
1. The BER performance of the proposed solution is worse than the FB algorithm, especially in the high SNR region. 
2. The authors did not provide a quantitative comparison between the proposed solution and the FB algorithm. The authors did not provide any detailed FLOPs numbers.
clarity,_quality,_novelty_and_reproducibility: 1. It is an interesting idea to replace the fixed components in the FB computational graph with neural networks.
2. The clarity of the writing is good enough.
summary_of_the_review: Overall, the proposed idea is interesting. It has lower complexity than the optimal FB equalizer while achieving better BER performance than conventional equalizers. However, the authors did not provide a quantitative comparison for the computational complexity. How much more computation cost does the FB algorithm have compared to the proposed solution? Also, it is unclear whether the computation cost of the proposed solution is much higher than conventional equalizers.
review_title: null

final_score: rating: 6: marginally above the acceptance threshold
review_content: summary_of_the_paper: UPDATE: I have read the rebuttal and updated the score.

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This paper addresses the problem of extremely fast wired connections, such as in very fast ethernet or PCIe connections. The main issue when increasing the communication speed is ISI (inter-symbol interference), which is traditionally solved using an equalizer. A very good equalizer is the forward-backward algorithm, but it is prohibitively high in computational complexity. This paper presents a learned equalizer, which has lower computational complexity in some cases, while retaining good bit error rate performance.

strength_and_weaknesses: Strengths:

—-------------------

The paper has many strengths and is tackling an important problem. For the sake of time  I am only writing about weaknesses in this review, since those are the ones that should be actioned upon.

Weaknesses (W)

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W: this paper is about applying neural network to replace a specific algorithm, namely the equalizer of a wired connection receiver. This makes the impact of the paper low for the machine learning community. Which of the finding can be generalized to other domains and how? If this cannot be described, maybe this paper is better suited in the publications in the communications domain.

W: The authors do not extensively study the reasons behind the good performance of the NeuralEQ nor they discuss how the resulting NeuralEQ is different from the Forward-Backward algorithm, from which it is motivated. It is clearly different since it has lower complexity in many cases. Is it because of some assumptions in the Forward-Backward algorithm are wrong?

W: The authors do not discuss the training and validation data split in detail. The training and validation data should be described in detail in every setting. 

W: How general is the trained algorithm? Does the algorithm need to be retrained if something in the transmission path changes? What are the typical changes? How often in real life would this happen?

W: How representative are the simulations to the real world? Since all results are produced by simulations, the authors should cite previous works that have compared similar simulations to real channels. The best option would of course be to record real data and validate the simulation trained algorithm on real data.


Minor weaknesses and comments:

—---------------------

Abstract: “Rapid growth of ML applications demands high-performance computing systems
to perform massive data processing.”, I don’t think ML applications are the only ones that require high bandwith, e.g., video applications is pretty large.

Minor spelling mistakes:

Header 4: “EXPERMIENT RESULTS” -> EXPERIMENT RESULTS 

4.2 “NerualEQ” -> NeuralEQ 

Figure 4: “pruning increase
the performance of NeuralEQ.” 🡪 … pruning increases the performance of NeuralEQ

clarity,_quality,_novelty_and_reproducibility: These are good in this paper.

summary_of_the_review: Relatively good work with some flaws that would warrant a new revision.
review_title: null

Meta-review: metareview:_summary,_strengths_and_weaknesses: This paper proposes a neural-network-based equalizer for high-speed wireline communication. The proposed equalizer is inspired by the forward-backward (FB) algorithm for MAP inference in hidden Markov models, replacing building blocks in FB with fully-connected layers. It was demonstrated via computer simulations that the proposal provides better trade-off between BER and throughput than conventional approaches including FB as well as feed-forward equalizer (FFE) and/or decision-feedback equalizer (DFE). I share my concern on this paper with Reviewer Qiy5, that the proposal consists of replacing building blocks in a standard algorithm (FB) with trainable neural networks in a rather straightforward manner, and this idea itself could be regarded as falling within the category of deep unfolding and thus it is not novel enough from the perspective of deep learning.
justification_for_why_not_higher_score: I cannot recommend acceptance of this paper because it does not appear to have significant contributions to the machine-learning community. As the authors state, the ICLR conference welcomes papers on applications. At the same time, as it is a competitive venue so that it would be desirable that papers presented there should be of interest for a wide audience. I judged that the contribution of this paper falls short of the bar in this respect.
Decision: Reject