Neural Network Compression Via Sparse Optimization
This addresses the need for efficient deployment of neural networks in various applications, offering a method that is more effective and easier to integrate than heuristic approaches, though it builds on existing sparse optimization techniques.
The paper tackles the problem of compressing deep neural networks to reduce inference costs by proposing a framework based on sparse stochastic optimization, achieving up to 7.2x and 2.9x FLOPs reduction on VGG16 and ResNet50 models while maintaining accuracy.
The compression of deep neural networks (DNNs) to reduce inference cost becomes increasingly important to meet realistic deployment requirements of various applications. There have been a significant amount of work regarding network compression, while most of them are heuristic rule-based or typically not friendly to be incorporated into varying scenarios. On the other hand, sparse optimization yielding sparse solutions naturally fits the compression requirement, but due to the limited study of sparse optimization in stochastic learning, its extension and application onto model compression is rarely well explored. In this work, we propose a model compression framework based on the recent progress on sparse stochastic optimization. Compared to existing model compression techniques, our method is effective and requires fewer extra engineering efforts to incorporate with varying applications, and has been numerically demonstrated on benchmark compression tasks. Particularly, we achieve up to 7.2 and 2.9 times FLOPs reduction with the same level of evaluation accuracy on VGG16 for CIFAR10 and ResNet50 for ImageNet compared to the baseline heavy models, respectively.