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 authors did solid work in collecting all the reported data. However, most findings don*t seem to be too surprising to me: - Finding #1 mainly shows that all architectures and batch sizes manage to utilize the GPU fully (or to the same percentage). - Regarding Finding #2, I agree that from a linear relationship in Figure 9 you could conclude said hyperbolic relationship. However, for this finding to be relevant, it has to hold especially for the latest generations of models. These cluster in the upper left corner of Figure 9 and on their own do not seem to show too much of a linear behaviour. Therefore I think there is not enough evidence to conclude asymptotic hyperbolic behaviour: For this the linear behaviour would have to be the stronger, the more models approach the upper left corner. - Finding #3 seems to be a simple conclusion from finding #1: As long as slower models are better and faster models do draw the same power, finding #3 holds. - Finding #4 is again similar to finding #1: If all architectures manage to fully utilize the GPU, inference time should be proportional to the number of operations. Maybe the most interesting finding would be that all tested models seem to use the same percentage of computational resources available on the GPU, while one might expect that more complex models don*t manage to utilize as much computational resources due to inter-dependencies. However actual GPU utilization was not evaluated and as the authors choose to use an older GPU, one would expect that all models manage to make use of all available computational power. Additionally, I think these findings would have to be put in relation with compressing techniques or tested on actual production networks to be of more interest.
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 authors did solid work in collecting all the reported data. However, most findings don*t seem to be too surprising to me: - Finding #1 mainly shows that all architectures and batch sizes manage to utilize the GPU fully (or to the same percentage). - Regarding Finding #2, I agree that from a linear relationship in Figure 9 you could conclude said hyperbolic relationship. However, for this finding to be relevant, it has to hold especially for the latest generations of models. These cluster in the upper left corner of Figure 9 and on their own do not seem to show too much of a linear behaviour. Therefore I think there is not enough evidence to conclude asymptotic hyperbolic behaviour: For this the linear behaviour would have to be the stronger, the more models approach the upper left corner. - Finding #3 seems to be a simple conclusion from finding #1: As long as slower models are better and faster models do draw the same power, finding #3 holds. - Finding #4 is again similar to finding #1: If all architectures manage to fully utilize the GPU, inference time should be proportional to the number of operations. Maybe the most interesting finding would be that all tested models seem to use the same percentage of computational resources available on the GPU, while one might expect that more complex models don*t manage to utilize as much computational resources due to inter-dependencies. However actual GPU utilization was not evaluated and as the authors choose to use an older GPU, one would expect that all models manage to make use of all available computational power. Additionally, I think these findings would have to be put in relation with compressing techniques or tested on actual production networks to be of more interest.
assistant
The authors did solid work in collecting all the reported data. However, most findings don*t seem to be too surprising to me: - Finding #1 mainly shows that all architectures and batch sizes manage to utilize the GPU fully (or to the same percentage). - Regarding Finding #2, I agree that from a linear relationship in Figure 9 you could conclude said hyperbolic relationship. However, for this finding to be relevant, it has to hold especially for the latest generations of models. These cluster in the upper left corner of Figure 9 and on their own do not seem to show too much of a linear behaviour. Therefore I think there is not enough evidence to conclude asymptotic hyperbolic behaviour: For this the linear behaviour would have to be the stronger, the more models approach the upper left corner. - Finding #3 seems to be a simple conclusion from finding #1: As long as slower models are better and faster models do draw the same power, finding #3 holds. - Finding #4 is again similar to finding #1: If all architectures manage to fully utilize the GPU, inference time should be proportional to the number of operations. Maybe the most interesting finding would be that all tested models seem to use the same percentage of computational resources available on the GPU, while one might expect that more complex models don*t manage to utilize as much computational resources due to inter-dependencies. However actual GPU utilization was not evaluated and as the authors choose to use an older GPU, one would expect that all models manage to make use of all available computational power. Additionally, I think these findings would have to be put in relation with compressing techniques or tested on actual production networks to be of more interest.