  Though recent advanced convolutional neural networks (CNNs) have been
improving the image recognition accuracy, the models are getting more complex
and time-consuming. For real-world applications in industrial and commercial
scenarios, engineers and developers are often faced with the requirement of
constrained time budget. In this paper, we investigate the accuracy of CNNs
under constrained time cost. Under this constraint, the designs of the network
architectures should exhibit as trade-offs among the factors like depth,
numbers of filters, filter sizes, etc. With a series of controlled comparisons,
we progressively modify a baseline model while preserving its time complexity.
This is also helpful for understanding the importance of the factors in network
designs. We present an architecture that achieves very competitive accuracy in
the ImageNet dataset (11.8% top-5 error, 10-view test), yet is 20% faster than
"AlexNet" (16.0% top-5 error, 10-view test).
