<p>There is a recently released CVPR 2017 paper with the title<a href="https://arxiv.org/abs/1611.10012" rel="nofollow noreferrer"> Speed/accuracy trade-offs for modern convolutional object detectors</a> (Huang et al.) that compares different feature extractors with some neural network architectures which the authors call "meta-architectures". They compare the so build models towards their speed, memory usage and accuracy.</p>
resources required to run a neural network
<p>In general, a more complicated neural network(say, an object classification CNN with 128 layers) requires more "resources"(time,number of gpu) to train than a less complicated neural network(for example, an object classification CNN with 32 layers).I found a link which has a very nice summary of different types of CNN and "resources" required to train them:
<a href="https://adeshpande3.github.io/adeshpande3.github.io/The-9-Deep-Learning-Papers-You-Need-To-Know-About.html" rel="nofollow noreferrer">https://adeshpande3.github.io/adeshpande3.github.io/The-9-Deep-Learning-Papers-You-Need-To-Know-About.html</a></p>

<p>However, after the training is complete, when we're actually using these neural networks(say, an autonomous driving car using a trained CNN to help navigate the car), do more complicated and more accurate neural networks require more "computational resources"(could be cpu,memory,etc) to run than a less complicated,less accurate neural networks? </p>

<p>I'm asking a generic question and the neural networks are not limited to object classification but can also include neural networks in NLP or other areas.</p>

<p>If the answer is "it depends",can you provide some examples of more complicated, more accurate neural networks using more resources to run than less complicated/accurate neural networks?</p>