  Most of the research in convolutional neural networks has focused on
increasing network depth to improve accuracy, resulting in a massive number of
parameters which restricts the trained network to platforms with memory and
processing constraints. We propose to modify the structure of the Very Deep
Convolutional Neural Networks (VDCNN) model to fit mobile platforms constraints
and keep performance. In this paper, we evaluate the impact of Temporal
Depthwise Separable Convolutions and Global Average Pooling in the network
parameters, storage size, and latency. The squeezed model (SVDCNN) is between
10x and 20x smaller, depending on the network depth, maintaining a maximum size
of 6MB. Regarding accuracy, the network experiences a loss between 0.4% and
1.3% and obtains lower latencies compared to the baseline model.
