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A Survey of Model Compression and Acceleration for Deep Neural Networks

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arxiv 1710.09282 v9 pith:QBKK4ERH submitted 2017-10-23 cs.LG cs.CV

Yu Cheng , Duo Wang , Pan Zhou , Tao Zhang This is my paper · ORCID
classification cs.LGcs.CV
keywords networksdeepmodelneuralperformancerecenttechniquesacceleration
verification ladder T0 review T1 audit T2 compute T3 formal
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Deep neural networks (DNNs) have recently achieved great success in many visual recognition tasks. However, existing deep neural network models are computationally expensive and memory intensive, hindering their deployment in devices with low memory resources or in applications with strict latency requirements. Therefore, a natural thought is to perform model compression and acceleration in deep networks without significantly decreasing the model performance. During the past five years, tremendous progress has been made in this area. In this paper, we review the recent techniques for compacting and accelerating DNN models. In general, these techniques are divided into four categories: parameter pruning and quantization, low-rank factorization, transferred/compact convolutional filters, and knowledge distillation. Methods of parameter pruning and quantization are described first, after that the other techniques are introduced. For each category, we also provide insightful analysis about the performance, related applications, advantages, and drawbacks. Then we go through some very recent successful methods, for example, dynamic capacity networks and stochastic depths networks. After that, we survey the evaluation matrices, the main datasets used for evaluating the model performance, and recent benchmark efforts. Finally, we conclude this paper, discuss remaining the challenges and possible directions for future work.

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Cited by 8 Pith papers

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