論文閱讀——Quantization and Training of Neural Networks for Efficient Integer-Arithmetic-Only Inference
論文閱讀——Quantization and Training of Neural Networks for Efficient Integer-Arithmetic-Only Inference from Google Abstract 隨着移動終端的普及以及深度學習模型對算力的巨大要求急需高效且準確的在設備進行推理的方案。本文提出了一種量化方案可以只用整數進行推理，比浮點數效率更高。本文還設計
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相關文章
- 1. 【論文閱讀筆記】Quantization and Training of Neural Networks for Efficient Integer-Arithmetic-Only Inference
- 2. Quantization and Training of Neural Networks for Efficient Integer-Arithmetic-Only Inference
- 3. 論文閱讀：Bag of Freebies for Training Object Detection Neural Networks
- 4. 論文閱讀《Knowledge Projection for Effective Design of Thinner and Faster Deep Neural Networks》
- 5. Pruning Convolutional Neural Networks For Resource Efficient Inference
- 6. 【論文閱讀】Image Preprocessing for Efficient Training of YOLO
- 7. （MobileNets）Efficient Convolutional Neural Networks for Mobile Vision Applications論文閱讀筆記
- 8. DeepLearning論文閱讀筆記(一):Cyclical Learning Rates for Training Neural Networks(CLR)
- 9. 【論文閱讀筆記】Ristretto： Hardware-Oriented Approximation of Convolutional Neural Networks
- 10. 論文閱讀：EfficientDet: Scalable and Efficient Object Detection