  Convolutional Neural Networks(CNN) has had a great success in the recent
past, because of the advent of faster GPUs and memory access. CNNs are really
powerful as they learn the features from data in layers such that they exhibit
the structure of the V-1 features of the human brain. A huge bottleneck, in
this case, is that CNNs are very large and have a very high memory footprint,
and hence they cannot be employed on devices with limited storage such as
mobile phone, IoT etc. In this work, we study the model complexity versus
accuracy trade-off on MNSIT dataset, and give a concrete framework for handling
such a problem, given the worst case accuracy that a system can tolerate. In
our work, we reduce the model complexity by 236 times, and memory footprint by
19.5 times compared to the base model while achieving worst case accuracy
threshold.
