title: resources required to run a neural network
body: <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>