Training With Mixed Precision
Chapter 2. Mixed Precision Training 在NVIDIA DeepLearning SDK的cuda8与Pascal架构中已经在训练时引入了低精度的能力。 混合精度在计算方法中结合了不同数据精度。 半精度（也被称为FP16）对比高精度的FP32与FP64降低了神经网络的显存占用，使得我们可以训练部署更大的网络，并且FP16在数据转换时比FP32或者FP64更节省时间。
相关文章
- 1. HAQ: Hardware-Aware Automated Quantization with Mixed Precision
- 2. TRAINING DEEP NEURAL NETWORKS WITH LOW PRECISION MULTIPLICATIONS
- 3. 【论文笔记 detection】Mixed Precision Training混合精度训练
- 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（翻译二）
- 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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相关文章
- 1. HAQ: Hardware-Aware Automated Quantization with Mixed Precision
- 2. TRAINING DEEP NEURAL NETWORKS WITH LOW PRECISION MULTIPLICATIONS
- 3. 【论文笔记 detection】Mixed Precision Training混合精度训练
- 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（翻译二）
- 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