Can we create an ensemble of deep learning models without increasing the classification time?
There is no magic pill for doing what you want. Extra computation cannot come free.
So one way this can be achieved is by using multiple worker machines to run inference in parallel.
Each model could run on a different machine using tensorflow serving.  
For every new inference do the following:  

Have a primary machine which takes up the job of running the inference
This primary machine, submits requests to different workers (all of which can run in parallel)
The primary machine collects results from each individual worker, and creates the final output by combining them based upon your ensemble logic.
I want to improve my ResNet model by creating an ensemble of X number of this model, taking the X best one I have trained. For what I've seen, a technique like bagging will take X time longer to classify an image, which is really not an option in my case. 
Is there a way to create an ensemble without increasing the required classifying time? Note that I don't care about increasing the training time, because it only needs to be done one time, compared to the classification which could be made a very large number of time.