【論文閱讀筆記】Quantization and Training of Neural Networks for Efficient Integer-Arithmetic-Only Inference
該方法的簡稱爲：IAO 該論文提出了一種允許網絡在推測時只使用整數計算的方法，即 float32 --> int8. 該文在MobileNets等已經壓縮過的網絡進行量化，測試數據集爲ImageNet分類數據集。不同於其他的方法，在高度冗餘的模型，如Alexnet等網絡上進行的，且提出的硬件加速方法需要專門的特殊硬件（即他們並沒有在真正的硬件上加速，只是提出一種理論），該方法在普通的CPU
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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. 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. （MobileNets）Efficient Convolutional Neural Networks for Mobile Vision Applications論文閱讀筆記
- 5. DeepLearning論文閱讀筆記(一):Cyclical Learning Rates for Training Neural Networks(CLR)
- 6. 【論文閱讀筆記】Ristretto： Hardware-Oriented Approximation of Convolutional Neural Networks
- 7. 閱讀筆記--CAN GRAPH NEURAL NETWORKS GO 「ONLINE」? AN ANALYSIS OF PRETRAINING AND INFERENCE
- 8. 論文閱讀筆記：MobileNets: Efficient Convolutional Neural Networks for Mobile Vision Applications
- 9. 【論文閱讀筆記】ECA-Net: Efficient Channel Attention for Deep Convolutional Neural Networks
- 10. 【論文閱讀筆記】MobileNets Efficient Convolutional Neural Networks for Mobile Vision Applications