Storage Efficient and Dynamic Flexible Runtime Channel Pruning via Deep Reinforcement Learning
In this paper, we propose a deep reinforcement learning (DRL) based framework to efficiently perform runtime channel pruning on convolutional neural networks (CNNs). Our DRL-based framework aims to learn a pruning strategy to determine how many and which channels to be pruned in each convolutional layer, depending on each specific input instance in runtime. The learned policy optimizes the performance of the network by restricting the computational resource on layers under an overall computation budget. Furthermore, unlike other runtime pruning methods which require to store all channels parameters in inference, our framework can reduce parameters storage consumption at deployment by introducing a static pruning component. Comparison experimental results with existing runtime and static pruning methods on state-of-the-art CNNs demonstrate that our proposed framework is able to provide a tradeoff between dynamic flexibility and storage efficiency in runtime channel pruning.
This paper presents an offline and online pruning method for CNNs where RL is used for tuning the sparsity. Pruning methods are important for real-time applications of CNNs on low resourced devices, so the paper addresses a genuine need. However, the paper is poorly written and the experimental results are not as convincing. 1) Majority of the description of the models and architecture is written in text and is very difficult to parse, while this could*ve been avoided by usage of mathematical notations for operations. This is specifically evident for early parts of sec 3 which makes parsing and reading very difficult. 2) It*s not clear why the baseline performances in the experimental section are different across different methods. If I understand correctly, all the original non-pruned models should be the same for this experiment to make sense. This is specially important for Tab 2 where the closest competing algorithms (FBS and CGNN). Due to using different baselines, the numbers are not comparable. 3) One thing that is not clear in the text is the computation taken by the runtime pruner architecture. Are these rolled in the estimations on the speedup?
You are a member of the scientific community tasked with peer review. Review the following paper title and abstract.
### Title + Abstract
Storage Efficient and Dynamic Flexible Runtime Channel Pruning via Deep Reinforcement Learning
In this paper, we propose a deep reinforcement learning (DRL) based framework to efficiently perform runtime channel pruning on convolutional neural networks (CNNs). Our DRL-based framework aims to learn a pruning strategy to determine how many and which channels to be pruned in each convolutional layer, depending on each specific input instance in runtime. The learned policy optimizes the performance of the network by restricting the computational resource on layers under an overall computation budget. Furthermore, unlike other runtime pruning methods which require to store all channels parameters in inference, our framework can reduce parameters storage consumption at deployment by introducing a static pruning component. Comparison experimental results with existing runtime and static pruning methods on state-of-the-art CNNs demonstrate that our proposed framework is able to provide a tradeoff between dynamic flexibility and storage efficiency in runtime channel pruning.
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>



This paper presents an offline and online pruning method for CNNs where RL is used for tuning the sparsity. Pruning methods are important for real-time applications of CNNs on low resourced devices, so the paper addresses a genuine need. However, the paper is poorly written and the experimental results are not as convincing. 1) Majority of the description of the models and architecture is written in text and is very difficult to parse, while this could*ve been avoided by usage of mathematical notations for operations. This is specifically evident for early parts of sec 3 which makes parsing and reading very difficult. 2) It*s not clear why the baseline performances in the experimental section are different across different methods. If I understand correctly, all the original non-pruned models should be the same for this experiment to make sense. This is specially important for Tab 2 where the closest competing algorithms (FBS and CGNN). Due to using different baselines, the numbers are not comparable. 3) One thing that is not clear in the text is the computation taken by the runtime pruner architecture. Are these rolled in the estimations on the speedup?
assistant
This paper presents an offline and online pruning method for CNNs where RL is used for tuning the sparsity. Pruning methods are important for real-time applications of CNNs on low resourced devices, so the paper addresses a genuine need. However, the paper is poorly written and the experimental results are not as convincing. 1) Majority of the description of the models and architecture is written in text and is very difficult to parse, while this could*ve been avoided by usage of mathematical notations for operations. This is specifically evident for early parts of sec 3 which makes parsing and reading very difficult. 2) It*s not clear why the baseline performances in the experimental section are different across different methods. If I understand correctly, all the original non-pruned models should be the same for this experiment to make sense. This is specially important for Tab 2 where the closest competing algorithms (FBS and CGNN). Due to using different baselines, the numbers are not comparable. 3) One thing that is not clear in the text is the computation taken by the runtime pruner architecture. Are these rolled in the estimations on the speedup?