An Analysis of Deep Neural Network Models for Practical Applications
Since the emergence of Deep Neural Networks (DNNs) as a prominent technique in the field of computer vision, the ImageNet classification challenge has played a major role in advancing the state-of-the-art. While accuracy figures have steadily increased, the resource utilisation of winning models has not been properly taken into account. In this work, we present a comprehensive analysis of important metrics in practical applications: accuracy, memory footprint, parameters, operations count, inference time and power consumption. Key findings are: (1) power consumption is independent of batch size and architecture; (2) accuracy and inference time are in a hyperbolic relationship; (3) energy constraint are an upper bound on the maximum achievable accuracy and model complexity; (4) the number of operations is a reliable estimate of the inference time. We believe our analysis provides a compelling set of information that helps design and engineer efficient DNNs.
The paper evaluates recent development in competitive ILSVRC CNN architectures from the perspective of resource utilization. It is clear that a lot of work has been put into the evaluations. The findings are well presented and the topic itself is important. However, most of the results are not surprising to people working with CNNs on a regular basis. And even if they are, I am not convinced about their practical value. It is hard to tell what we actually learn from these findings when approaching new problems with computational constraints or when in production settings. In my opinion, this is mainly because the paper does not discuss realistic circumstances. Main concerns: 1) The evaluation does not tell me much for realistic scenarios, that mostly involve fine-tuning networks, as ILSVRC is just a starting point in most cases. VGG for instance really shines for fine-tuning, but it is cumbersome to train from scratch. And VGG works well for compression, too. So possibly it is a very good choice if these by now standard steps are taken into account. Such questions are of high practical relevance! 2) Compressed networks have a much higher acc/parameter density, so comparison how well models can be compressed is important, or at least comparing to some of the most well-known and publicly available compressed networks. 3) There is no analysis on the actual topology of the networks and where the bottlenecks lie. This would be very useful to have as well. Minor concern: 1) Why did the authors choose to use batch normalization in NiN and AlexNet?
You are a member of the scientific community tasked with peer review. Review the following paper title and abstract.
### Title + Abstract
An Analysis of Deep Neural Network Models for Practical Applications
Since the emergence of Deep Neural Networks (DNNs) as a prominent technique in the field of computer vision, the ImageNet classification challenge has played a major role in advancing the state-of-the-art. While accuracy figures have steadily increased, the resource utilisation of winning models has not been properly taken into account. In this work, we present a comprehensive analysis of important metrics in practical applications: accuracy, memory footprint, parameters, operations count, inference time and power consumption. Key findings are: (1) power consumption is independent of batch size and architecture; (2) accuracy and inference time are in a hyperbolic relationship; (3) energy constraint are an upper bound on the maximum achievable accuracy and model complexity; (4) the number of operations is a reliable estimate of the inference time. We believe our analysis provides a compelling set of information that helps design and engineer efficient DNNs.
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 paper evaluates recent development in competitive ILSVRC CNN architectures from the perspective of resource utilization. It is clear that a lot of work has been put into the evaluations. The findings are well presented and the topic itself is important. However, most of the results are not surprising to people working with CNNs on a regular basis. And even if they are, I am not convinced about their practical value. It is hard to tell what we actually learn from these findings when approaching new problems with computational constraints or when in production settings. In my opinion, this is mainly because the paper does not discuss realistic circumstances. Main concerns: 1) The evaluation does not tell me much for realistic scenarios, that mostly involve fine-tuning networks, as ILSVRC is just a starting point in most cases. VGG for instance really shines for fine-tuning, but it is cumbersome to train from scratch. And VGG works well for compression, too. So possibly it is a very good choice if these by now standard steps are taken into account. Such questions are of high practical relevance! 2) Compressed networks have a much higher acc/parameter density, so comparison how well models can be compressed is important, or at least comparing to some of the most well-known and publicly available compressed networks. 3) There is no analysis on the actual topology of the networks and where the bottlenecks lie. This would be very useful to have as well. Minor concern: 1) Why did the authors choose to use batch normalization in NiN and AlexNet?
assistant
The paper evaluates recent development in competitive ILSVRC CNN architectures from the perspective of resource utilization. It is clear that a lot of work has been put into the evaluations. The findings are well presented and the topic itself is important. However, most of the results are not surprising to people working with CNNs on a regular basis. And even if they are, I am not convinced about their practical value. It is hard to tell what we actually learn from these findings when approaching new problems with computational constraints or when in production settings. In my opinion, this is mainly because the paper does not discuss realistic circumstances. Main concerns: 1) The evaluation does not tell me much for realistic scenarios, that mostly involve fine-tuning networks, as ILSVRC is just a starting point in most cases. VGG for instance really shines for fine-tuning, but it is cumbersome to train from scratch. And VGG works well for compression, too. So possibly it is a very good choice if these by now standard steps are taken into account. Such questions are of high practical relevance! 2) Compressed networks have a much higher acc/parameter density, so comparison how well models can be compressed is important, or at least comparing to some of the most well-known and publicly available compressed networks. 3) There is no analysis on the actual topology of the networks and where the bottlenecks lie. This would be very useful to have as well. Minor concern: 1) Why did the authors choose to use batch normalization in NiN and AlexNet?