Scale invariant template matching
To increase the speed, I think the dimensionality of the feature descriptors should be reduced. SIFT feature descriptor is of 128 dimensions. 

So you can try PCA-SIFT which reduces the descriptor number to 20. It uses Principal Component Analysis to the SIFT descriptors.

You can also try using Speeded Up Robust Features (SURF). The feature descriptors obtained from SURF are invariant to rotation and scale, and it can reduce the dimensions of feature descriptors significantly. it enables faster feature detection as well as template matching. Also, MATLAB has a specific command for SURF i.e. detectSURFFeatures, so if you are using MATLAB for your job you are in luck. I think OpenCV also has the SURF feature command so that you can use it on python too. I have used the SURF features which I think is pretty fast.