Training With Mixed Precision
Chapter 2. Mixed Precision Training 在NVIDIA DeepLearning SDK的cuda8與Pascal架構中已經在訓練時引入了低精度的能力。 混合精度在計算方法中結合了不同數據精度。 半精度（也被稱爲FP16）對比高精度的FP32與FP64降低了神經網絡的顯存佔用，使得我們可以訓練部署更大的網絡，並且FP16在數據轉換時比FP32或者FP64更節省時間。
相關文章
- 1. HAQ: Hardware-Aware Automated Quantization with Mixed Precision
- 2. TRAINING DEEP NEURAL NETWORKS WITH LOW PRECISION MULTIPLICATIONS
- 3. 【論文筆記 detection】Mixed Precision Training混合精度訓練
- 4. 《SWALP：Stochastic Weight Averaging in Low-Precision Training》
- 5. Distributed Training using Apache MXNet with Horovod
- 6. Streaming with Apache Training
- 7. Mixed Reality Martial Arts Training using Real-time 3D Human Pose Forecasting with a RGB Camera（翻譯二）
- 8. Self-training with Noisy Student
- 9. INQ(incremental network quantization：towards lossless CNNs with low-precision weights
- 10. Training Region-based Object Detectors with Online Hard Example Mining
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相關文章
- 1. HAQ: Hardware-Aware Automated Quantization with Mixed Precision
- 2. TRAINING DEEP NEURAL NETWORKS WITH LOW PRECISION MULTIPLICATIONS
- 3. 【論文筆記 detection】Mixed Precision Training混合精度訓練
- 4. 《SWALP：Stochastic Weight Averaging in Low-Precision Training》
- 5. Distributed Training using Apache MXNet with Horovod
- 6. Streaming with Apache Training
- 7. Mixed Reality Martial Arts Training using Real-time 3D Human Pose Forecasting with a RGB Camera（翻譯二）
- 8. Self-training with Noisy Student
- 9. INQ(incremental network quantization：towards lossless CNNs with low-precision weights
- 10. Training Region-based Object Detectors with Online Hard Example Mining