Neural network simplification using a progressive barrier based approach
2018
Neural networks are indispensable to
state-of-the-art artificial intelligence algorithms. However, its
high accuracy comes at the cost of high computational complexity.
This leads to the high operating cost of data centers and also
hinders its deployment on mobile devices. In this thesis, we
propose an algorithm to address this problem. The proposed
algorithm uses progressive barriers to automatically and
progressively simplify a pre-trained neural network until the
target complexity is met while maximizing the accuracy. Along with
the neural network that meets the target complexity, the algorithm
also generates a family of simplified networks with different
accuracy-complexity trade-offs, which allows for dynamic network
selection and further study. Experiment results show that the
algorithm achieves better accuracy-complexity trade-offs on a
highly compact MobileNet architecture, compared with
state-of-the-art automated network simplification approaches. For
image classification on the ImageNet dataset, the algorithm reduces
the number of multiply-accumulate operations by 1.68x while
achieving 0.9% higher accuracy.
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