Quantization and Training of Neural Networks for Efficient Integer-Arithmetic-Only Inference
摘要 作者提出了一種只使用整數運算的quantization方式，比起float point運算效率更高。同時提出了一種相應的訓練方式來保證quantization之後的準確率。這篇文章的方法提升了accuracy和on-device latency之間的trade off，並且可以在MobileNets上使用。 1 introduction 作者總結了目前有效將龐大的神經網絡應用在資源更爲有限的
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相關文章
- 1. 論文閱讀——Quantization and Training of Neural Networks for Efficient Integer-Arithmetic-Only Inference
- 2. 【論文閱讀筆記】Quantization and Training of Neural Networks for Efficient Integer-Arithmetic-Only Inference
- 3. MobileNets: Efficient Convolutional Neural Networks for Mobile Vision Applications
- 4. Bag of Freebies for Training Object Detection Neural Networks
- 5. （轉）A Recipe for Training Neural Networks
- 6. STRATEGIES FOR PRE-TRAINING GRAPH NEURAL NETWORKS
- 7. Domain-Adversarial Training of Neural Networks
- 8. DANN：Domain-Adversarial Training of Neural Networks
- 9. Weighted-Entropy-based Quantization for Deep Neural Networks
- 10. Strategies For Pre-Training Graph Neural Networks