Training Long Short-Term Memory With Sparsified Stochastic Gradient Descent
Prior work has demonstrated that exploiting the sparsity can dramatically improve the energy efficiency and shrink the memory footprint of Convolutional Neural Networks (CNNs). However, these sparsity-centric optimization techniques might be less effective for Long Short-Term Memory (LSTM) based Recurrent Neural Networks (RNNs), especially for the training phase, because of the significant structural difference between the neurons. To investigate if there is possible sparsity-centric optimization for training LSTM-based RNNs, we studied several applications and observed that there is potential sparsity in the gradients generated in the backward propagation. In this paper, we illustrate why the sparsity exists and propose a simple yet effective thresholding technique to induce further more sparsity during training an LSTM-based RNN training. Experiment results show that the proposed technique can increase the sparsity of linear gate gradients to higher than 80\% without loss of performance, which makes more than 50\% multiply-accumulate (MAC) operations redundant in an entire LSTM training process. These redudant MAC operations can be eliminated by hardware techniques to improve the energy efficiency and training speed of LSTM-based RNNs.
The findings of applying sparsity in the backward gradients for training LSTMs is interesting. But the paper seems incomplete without the proper experimental justification. Only the validation loss is reported which is definitely insufficient. Proper testing results and commonly reported evaluation criterion needs to be included to support the claim of no degradation when applying the proposed sparsity technique. Also actual justification of the gains in terms of speed and efficiency would make the paper much stronger.
You are a member of the scientific community tasked with peer review. Review the following paper title and abstract.
### Title + Abstract
Training Long Short-Term Memory With Sparsified Stochastic Gradient Descent
Prior work has demonstrated that exploiting the sparsity can dramatically improve the energy efficiency and shrink the memory footprint of Convolutional Neural Networks (CNNs). However, these sparsity-centric optimization techniques might be less effective for Long Short-Term Memory (LSTM) based Recurrent Neural Networks (RNNs), especially for the training phase, because of the significant structural difference between the neurons. To investigate if there is possible sparsity-centric optimization for training LSTM-based RNNs, we studied several applications and observed that there is potential sparsity in the gradients generated in the backward propagation. In this paper, we illustrate why the sparsity exists and propose a simple yet effective thresholding technique to induce further more sparsity during training an LSTM-based RNN training. Experiment results show that the proposed technique can increase the sparsity of linear gate gradients to higher than 80\% without loss of performance, which makes more than 50\% multiply-accumulate (MAC) operations redundant in an entire LSTM training process. These redudant MAC operations can be eliminated by hardware techniques to improve the energy efficiency and training speed of LSTM-based RNNs.
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>



The findings of applying sparsity in the backward gradients for training LSTMs is interesting. But the paper seems incomplete without the proper experimental justification. Only the validation loss is reported which is definitely insufficient. Proper testing results and commonly reported evaluation criterion needs to be included to support the claim of no degradation when applying the proposed sparsity technique. Also actual justification of the gains in terms of speed and efficiency would make the paper much stronger.
assistant
The findings of applying sparsity in the backward gradients for training LSTMs is interesting. But the paper seems incomplete without the proper experimental justification. Only the validation loss is reported which is definitely insufficient. Proper testing results and commonly reported evaluation criterion needs to be included to support the claim of no degradation when applying the proposed sparsity technique. Also actual justification of the gains in terms of speed and efficiency would make the paper much stronger.