deep learning, model compression, run-time resource management, cost optimization
Despite the rapid development of mobile and embedded hardware, directly executing computation-expensive and storage-intensive deep learning algorithms on these devices’ local side remains constrained for sensory data analysis. In this paper, we first summarize the layer compression techniques for the state-of-the-art deep learning model from three categories: weight factorization and pruning, convolution decomposition, and special layer architecture designing. For each category of layer compression techniques, we quantify their storage and computation tunable by layer compression techniques and discuss their practical challenges and possible improvements. Then, we implement Android projects using TensorFlow Mobile to test these 10 compression methods and compare their practical performances in terms of accuracy, parameter size, intermediate feature size, computation, processing latency, and energy consumption. To further discuss their advantages and bottlenecks, we test their performance over four standard recognition tasks on six resource-constrained Android smartphones. Finally, we survey two types of run-time Neural Network (NN) compression techniques which are orthogonal with the layer compression techniques, run-time resource management and cost optimization with special NN architecture, which are orthogonal with the layer compression techniques.
Tsinghua University Press
Kaiming Nan, Sicong Liu, Junzhao Du et al. Deep Model Compression for Mobile Platforms: A Survey. Tsinghua Science and Technology 2019, 24(06): 677-693.