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.
A few issues with this paper: 1- I find finding #2 trivial and unworthy of mention, but the author don*t seem to agree with me that it is. See discussions. 2- Finding #1 relies on Fig #4, which appears very noisy and doesn*t provide any error analysis. It makes me question how robust this finding is. One would have naively expected the power usage trend to mirror Fig #3, but given the level of noise, I can*t convince myself whether the null hypothesis of there being no dependency between batch size and power consumption is more likely than the alternative. 3- Paper is unfriendly to colorblind readers (or those with B/W printers) Overall, this paper is a reasonable review of where we are in terms of SOTA vision architectures, but doesn*t provide much new insight. I found most interesting the clear illustration that VGG models stand out in terms of being a bad tradeoff in resource-constrained environments (too many researchers are tempted to benchmark their model compression algorithm on VGG-class models because that*s always where one can show 10x improvements without doing much.)
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>



A few issues with this paper: 1- I find finding #2 trivial and unworthy of mention, but the author don*t seem to agree with me that it is. See discussions. 2- Finding #1 relies on Fig #4, which appears very noisy and doesn*t provide any error analysis. It makes me question how robust this finding is. One would have naively expected the power usage trend to mirror Fig #3, but given the level of noise, I can*t convince myself whether the null hypothesis of there being no dependency between batch size and power consumption is more likely than the alternative. 3- Paper is unfriendly to colorblind readers (or those with B/W printers) Overall, this paper is a reasonable review of where we are in terms of SOTA vision architectures, but doesn*t provide much new insight. I found most interesting the clear illustration that VGG models stand out in terms of being a bad tradeoff in resource-constrained environments (too many researchers are tempted to benchmark their model compression algorithm on VGG-class models because that*s always where one can show 10x improvements without doing much.)
assistant
A few issues with this paper: 1- I find finding #2 trivial and unworthy of mention, but the author don*t seem to agree with me that it is. See discussions. 2- Finding #1 relies on Fig #4, which appears very noisy and doesn*t provide any error analysis. It makes me question how robust this finding is. One would have naively expected the power usage trend to mirror Fig #3, but given the level of noise, I can*t convince myself whether the null hypothesis of there being no dependency between batch size and power consumption is more likely than the alternative. 3- Paper is unfriendly to colorblind readers (or those with B/W printers) Overall, this paper is a reasonable review of where we are in terms of SOTA vision architectures, but doesn*t provide much new insight. I found most interesting the clear illustration that VGG models stand out in terms of being a bad tradeoff in resource-constrained environments (too many researchers are tempted to benchmark their model compression algorithm on VGG-class models because that*s always where one can show 10x improvements without doing much.)